text:
\section{Introduction}
A fundamental step in many computer vision and artificial intelligence applications involves objects segmentation, for various tasks such as object detection \cite{wu2017moving, bouwmans2018applications}, visual object tracking \cite{zhang2015robust, vaswani2017robust}, video surveillance \cite{bouwmans2014robust, javed2016spatiotemporal}, salient motion detection \cite{chen2018scom} and image inpainting \cite{sultana2018unsupervised}. For object segmentation, background modeling plays a crucial role as it is a key process, which describes a scene without the presence of any foreground objects. However, foreground object detection is the process of extracting moving objects with prior knowledge of the background. Foreground object detection becomes more challenging in real-time environments for instance illumination variations, out of range scenarios and color camouflage of foreground objects concerning the background information \cite{maddalena2018background}. There are other challenging conditions as well, such as sequences with shadows or ghosting artifacts and also bootstrapping in which foreground objects are present in almost all frames of a video sequence. Over the past decade, many techniques have been proposed in the literature to address the problems of these challenging background scenes for the tasks of foreground detection and evaluation \cite{bouwmans2017scene, javed2017background, shimada2013background}.

To address the challenges mentioned above for object segmentation, we present a fusion based deep learning method. Our proposed method is based on Generative Adversarial Network (GAN) \cite {goodfellow2014generative} working on the idea of back propagation steps to generate specific kind of data. In this study, our primary focus is foreground object segmentation in the presence of various challenging conditions in background scenes. To address this problem, we present a solution based on GANs \cite{schlegl2017unsupervised} which works on the principle of generating background samples with scene specific information. Our proposed GAN model trains in an unsupervised manner on all video sequences irrespective of the presence of foreground objects in them as scene specific model. The primary purpose of training scene-specific GAN model is that the network should be able to learn the semantics of the scene containing various foreground background objects in the presence of challenging conditions such as illumination variations, shadows, and dynamic background information. The key idea behind training our GAN model with various challenging scenes is that during testing, our model will be able to generate the background scene according to our given test sample information. Since our network is trained on the data containing both background as well as foreground information, we need to eliminate moving foreground objects from our test sample in order to generate the exact background image via our trained GAN model. This can be done by multiplying test samples containing foreground objects with their motion masks evaluated by using optical flow \cite{liu2009beyond}. This step helps our GAN model to generate exact image sample containing only background information similar in semantics as test sample via back-propagation technique. The generated background sample is then subtracted from the given test sample to detect foreground objects as shown in Figure \ref{fig_framework}.

\section{Related Work}
In the last two decades, several methods have proposed for foreground detection by exploiting color and depth information. There are various traditional background subtraction methods, for instance, a very popular and classic method for background subtraction is GMM \cite{stauffer1999adaptive}. The idea of this proposed method is, modeling of each pixel is done with a mixture of Gaussian, and it works on color information processing only.
However, SOBS algorithms, such as \cite{maddalena2008self} and \cite{maddalena2012sobs}, shows improvement in performance by using color and depth features. They are based on self-organizing neural networks, which achieves better results in various challenging environments. Another efficient background subtraction model with a fusion of depth information embedded in the basic structure of \textit{Robust Principal Component Analysis} (RPCA) is proposed by Javed \textit{et al.} \cite {javed2017moving, javed2018moving}. It is a hybrid RPCA model with spatial and temporal information handling mechanism. Therefore, it is called \textit{Spatiotemporal Robust Principal Component Analysis} (SRPCA). SRPCA algorithm is a graph-regularized method, which preserves the spatiotemporal information of background that is low-rank matrix formation in the form of dual spectral graphs. To address the challenges in background subtraction problem,  Bo Xin \textit{et al.} \cite{xin2015background} presented a technique called \textit{Background Subtraction via Generalized Fused Lasso Foreground Modeling} (BS-GFL). Their primary objective is to address the problem of missing information in foreground detection because of various challenges like illumination variations and dynamic backgrounds. To solve this problem, they consider generalized fused lasso regularization to search for intact structured foregrounds to recover the missing content during the background subtraction process. Although BS-GFL is an efficient algorithm with a very good performance, however, it is an offline and partially supervised method. To solve the same problem of the missing content of foreground regions during background subtraction process, Xiaowei Zhou \textit{et al.} \cite{DECOLOR} proposed a method called \textit{Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation} (DECOLOR). The authors of this proposed method exploited Markov random field technique to recover the missing content of foreground detection during background subtraction process. For foreground detection by exploiting color as well as depth information, Massimo De Gregorio \textit{et al.} \cite{de2017cwisardh} presented an algorithm known as \textit{cwisardH+} working on the idea of decoupling the color information from the pixel depth information. The two video sequences runs are synchronously in this algorithm but independently and modeled by weightless neural networks at each pixel value.

\section{Proposed Method} \label{Proposed}
In this section, we describe each step of our proposed algorithm in detail. The workflow diagram of our proposed method ForeGAN\_RGBD is presented in Figure \ref{fig_framework}. Our proposed method aims to perform foreground object segmentation by adding RGB and depth information into  DCGAN model \cite{radford2015unsupervised}.
ForeGAN\_RGBD has two phases: Phase 1.) Training of the RGB and depth video sequences with two independent models as shown in Figure \ref{fig_framework}. Phase 2.) Testing of the two trained models with video sequences including foreground objects or background objects via backpropagation technique. It means that in phase 1 we learn two models based on GAN \cite{yeh2017semantic} representing indoor and outdoor scenes with various challenges on color as well as depth information. Each GAN train two models simultaneously, a discriminator model and a generator model, to differentiate between real and fake generated data containing RGB or depth information. The objective of the model is to achieve equilibrium of costs, increasing the ability of the model to generate data that is more accurate. In the following, we explain our proposed ForeGAN\_RGBD model in detail.
\begin{figure}[t]
	\centering
	\includegraphics[scale=0.28]{Figure_1.png}

	\caption{Workflow of the proposed ForeGAN\_RGBD model. Phase 1: Training has two independent models one to learn color information of different scenes, called ForeGAN\_RGB model and the other to learn depth information which is called ForeGAN\_D model. Phase 2: Testing also has two independent trained models for evaluation. (1) Input test RGB image sample, (2) Motion masks of test sample via optical flow, (3) Input RGB image sample multiplied by motion mask, (4) Generated RGB background image sample, (5) RGB foreground detection by equation \ref{FDL}, (6) Input test depth image sample, (7) Generated depth background image sample, (8) Depth foreground detection also by equation \ref{FDL}, (9) Motion mask, (10) Depth foreground detection after masking to eliminate noisy background pixels, and (11) Final output by fusion of results from (10) and (5) which is simply pixel by pixel addition. The samples presented in this Figure are from SBM-RGBD dataset \cite{camplani2017rgb} video sequence 'genSeq2' category 'Shadows'.}
	\label{fig_framework}
\end{figure}
\subsection{ForeGAN\_RGBD Phase 1}\label{foregan}
A GAN model has two adversarial modules, a discriminator $D$ and a generator $G$. The main objective of generator $G$ is to learn a distribution $p_{gen}$ over input data $X_i$ via mapping of $z$ samples through $G(z)$. This mapping facilitates the $1D$ vectors of input noise which is uniformly distributed and sampled from latent space $Z$ to the $2D$ image representation. In a basic GAN model discriminator, $D$ is a Convolutional Neural Network (CNN) model that maps a $2D$ image representation to a single value $D(\cdot)$. This single value $D(\cdot)$ of discriminator's output can be explicated as the probability that whether the input given to the discriminator $D$ was a fake generated image $G(z)$ by the generator $G$ or a real image $X$ sampled from training data $X_i$. The discriminator and the generator are simultaneously optimized via cross entropy loss functions in a following two-player minimax game with $\Gamma (D, G)$ as a value function:
\begin{equation} \label{eq_fullgan}
\begin{split}
\min\limits_{G}\max\limits_{D}~\Gamma(G, D)= \mathbb{E}_{x\sim p_{data}(x)}[log(D(x))]\\
+ \mathbb{E}_{z\sim p_{z}(z)}[log(1 - D(G(z)))].
\end{split}
\end{equation}
The discriminator in GAN model is a decision maker entity which is trained to maximize the probability of assigning real training sample to actual input data and samples from $p_{gen}$ to the fake generated data. The purpose of the generator $G$ is to fool $D$ via minimizing the cost function $\Gamma(G)$ = $log(1 - D(G(z)))$, which is basically to maximize the following equation:
\begin{equation}
    \Gamma(G) = D(G(z)).
\end{equation}
During the training process, the generator tries to improve itself by generating realistic images, and the discriminator tries to identify the real and fake generated images. Once the training is done then the next step is testing which is discussed in detail in the next section.
\subsection{Inverse Mapping of Test Images to Latent Space Representation, Phase 2}
During the phase of adversarial training, the generator learns the mapping from latent space representations $z$ which is random noise to more realistic images, $G(z) = z\mapsto x$. However inverse mapping $\theta(x) = x\mapsto z$ in GAN is not a straightforward process, rather we need a different mechanism for this purpose. To achieve inverse mapping Yeh \textit{et al.} \cite{yeh2017semantic} presented a back-propagation method to input data. This technique was already used by Google's DeepDream to create dreamlike images \cite{mordvintsev2015inceptionism}. Moreover, the back-propagation method has also been used to understand and visualize neural network's learned features by inverting the network by updating gradients at the level of input layer \cite{yeh2017semantic},  \cite{mahendran2015understanding}, \cite{linden1989inversion}, \cite{dosovitskiy2016inverting}. Every back-propagation based method requires specific loss functions for inverse mapping process in a neural network. Therefore our method has two loss functions as well. The purpose of the inverse mapping in our proposed method is to generate the specific kind of data during testing via back propagation strategy.
\subsubsection{Foreground Object Segmentation Loss}
Given a test image $x$ we aim to find that particular random noise $z$ in the latent space that was mapped to generate image $G(z)$ via back-propagation method. To find that specific $z$, we have to select an initial random sample $z_o$, from the latent space and reinforce it to the trained generator network to generate $G(z_o)$. The loss functions are designed by the generated image $G(z_o)$, which provides significant update information of the coefficients of $z_o$ leading our back-propagation step to be shifted from $z_o$ to $z_1$ in the latent space distribution. The most similar generated image $G(z_{\beta})$ can be found by various back-propagation steps $\beta = 0, 1, 2, ... ,\phi$ by minimizing the following loss function:
\begin{equation}\label{FDL}
 \Upsilon_{F}(z_{\beta}) = \sum |x - G(z_{\beta})|,
\end{equation}
$\Upsilon_{F}(z_{\beta})$ function measures the visual dissimilarity between the test image $x$ and generated image $G(z_{\beta})$ via various back-propagation steps. In an ideal scenario if test and generated images are identical, $\Upsilon_{F}(z_{\beta})$ will be zero.
\subsubsection{Feature Matching Loss}
Now the next issue is how to generate those images $G(z_{\beta})$ which exactly makes the best match with test image $x$. To solve this problem, we need another loss term which helps the generator to generate similar images as test images and minimize the loss function in equation $\eqref{FDL}$. In contrast to the loss functions for best matching technique defined in \cite{yeh2017semantic}, Thomas Schlegl \textit{et al.} \cite{schlegl2017unsupervised} adopted an interesting method to lead generator to generate the specific images as test images. This method addresses the problem arises due to over-training of the discriminator, which causes the instability of GANs. To improve the inverse mapping of test image $x$ to that specific random noise $z$, feature matching technique is designed to force the generator to generate the data with similar statistics as test data. Since discriminator feeds its gradients to the generator, it is best to design the loss function on discriminator. This loss function is defined in such a way that the intermediate feature layer of the discriminator is feed with the generated image $G(z_{\beta})$:
\begin{equation} \label{ML}
    \Upsilon_{M}(z_{\beta}) = \sum |l(x) - l(G(z_{\beta}))|,
\end{equation}
Where $l(\cdot)$ represents the output of the intermediate layer of the discriminator, which describes the test image $x$. Based on this loss function, the discriminator is now used as a feature extractor rather than a decision maker for real or fake image representations during testing process only.\\
The overall loss functions can be represented as the weighted sum of both loss terms defined in equations \eqref{FDL} and \eqref{ML}:

\begin{equation} \label{final}
    \Upsilon_{M}(z_{\beta}) = (1 - \eta) \Upsilon_{F}(z_{\beta})+ \eta \Upsilon_{M}(z_{\beta}).
\end{equation}
The back-propagation method is only applied to the coefficients of $z$, while other hyper parameters of the trained GAN model remain unchanged in phase 2 which is Testing.

\subsection{Motion Masks via Optical Flow} \label{motion_mask_method}
To identify fast moving objects in video frames, we use optical flow \cite{liu2009beyond} which creates a motion mask to capture moving objects. This technique calculates motion between each pair of consecutive frames in the given input video sequence $I$. Motion mask $M$ is estimated by using motion information from consecutive frames of video sequences. Suppose $I_{t}$ and $I_{t-1}$ be the two consecutive video frames in $I$ at time any instant $t$ and $t-1$, respectively. Taking $U^{y}_{t,p}$ be the horizontal component and $V^{x}_{t,p}$ be the vertical component of the motion vector at position $p$ computed between consecutive frames, the corresponding motion mask, $M_{t}\in\{0,1\}$ will be estimated as:
\begin{equation} \label{eq_motion}
M_{t,p}=\begin{cases}
1,~~if~~\sqrt {(U^{y}_{t,p})^2 + (V^{x}_{t,p})^2 }< T,\\
0,~~otherwise.
\end{cases}
\end{equation}
In the above equation, $T$ is a threshold of motion mask magnitude, and it is computed by taking the average of all pixels in the estimated motion field.
\section{Implementation} \label{implemention}
Our work is inspired by \cite{yeh2017semantic} and we have adapted the network \cite{schlegl2017unsupervised} which is based on DCGAN-tensorflow implementation \cite{radford2015unsupervised}. Our proposed ForeGAN\_RGBD model has a generative network, G, takes a random noise vector drawn from a uniform distribution and generates an image with dimension $64\times64\times3$. The discriminator model, however, runs in reverse order of the generator. The input images are fixed at $64\times64\times3$ running through a series of five convolution layers with down sampling and the number of channels are double the size of the previous convolution layer. Furthermore, the last layer of the network is a two class softmax. For training the ForeGAN\_RGBD model, we use Adam \cite{kinga2015method} for optimization. The back-propagation steps in our model are $2000$ during testing to generate the background image similar to the test image. The hyper-parameters are discussed in detail in section \ref{training} and \ref{testing}. Experiments are conducted using TitanX GPU with fixed input image samples of size in training as well as testing. Since all the categories have a limited number of video sequences, we have increased the training data by data augmentation technique which is translation and rotation of all the training images. Note that only those samples are considered in training whose ground truth information is not available in the dataset. Similarly only those video frames are considered for evaluation/testing purpose whose ground truth information is available in the dataset.
\section{Experiments} \label{experiments}
We have evaluated our proposed approach on $6$ different categories of SBM-RGBD dataset \cite{camplani2017rgb} containing $27$ video sequences.
The video sequences are very challenging as they contain indoor and outdoor background scenes for foreground detection in the presence of color as well as depth information. The experimental process is divided into two phases, training and testing.
The detail explanation of both phases is presented as follows:
\subsection{Phase 1: Training}\label{training}
The training is performed on $6$ categories of the SBM-RGBD dataset using the proposed technique.
We have trained two models independently one to learn of color information (\textit{ForeGAN\_RGB}) of the video sequences and the other to learn the depth information (\textit{ForeGAN\_D}) with $200$ epochs each.
During the training phase, the goal of our GAN network is to learn the color as well as depth information of the background scene. However, the only difference between two models is that the RGB model is trained on all kinds of video sequences regardless of the presence of foreground objects while the depth model of GAN is trained with the video sequences containing only depth information of background scene. Both of the models are trained individually on scene-specific information based on each category in SBM-RGBD dataset.
\begin{figure}[t]
	\centering
	\includegraphics[scale=0.32]{Figure_2.png}

	\caption{Visual results of each step of our propose ForeGAN\_RGBD method. (a) Input test RGB sequences $I_{t}$ as mentioned in section \ref{motion_mask_method} (b) Input test RGB sequences $I_{t-1}$ (c) Input test RGB sequence $I_{t-1}$ multiplied by motion masks to identify moving objects (d) generated background samples by our $ForeGAN\_RGB$ model (e) Foreground objects detected by subtracting (b) and (d) which is equation \eqref{FDL}, (f) Ground truths (g) Test depth sequences (h) Generated background depths by $ForeGAN\_D$ model. The sequences represented in $row~1$ is from 'Bootstrapping\_ds' category 'Bootstrapping', $row~2$ is from 'fall01cam1' category 'Shadows',  $row~3$ is from 'Hallway ' category 'Color Camouflage' and  $row~4$ is from 'Despatx\_ds' category 'Depth Camouflage'.}
	\label{stepby}
\end{figure}
\subsection{Phase 2: Testing}\label{testing}
The testing of the proposed models is performed independently on both trained models, and the output of both models is fused to get the final results.  All the testing samples are also fixed as mentioned in section \ref{implemention} and given as input to the models individually for validation with back-propagation steps. We have evaluated our proposed model by using $F$-measure score which is calculated as follows:
\begin{equation}
F =  2\frac{P\times R}{P + R},
\end{equation}
\begin{equation} \label{pre}
P =  \frac{T_{p}}{T_{p} + F_{p}},
\end{equation}
\begin{equation} \label{re}
R =  \frac{T_{p}}{T_{p} + F_{n}},
\end{equation}
where $T_{p}$ is True positives, $T_{n}$ is True negatives, $F_{p}$ is False positives, $F_{n}$ is False negatives, $F$ is $F$-Measure, $P$ is Precision, and $R$ is Recall.
For better foreground detection, the aim is to maximize $F$ score.
Since the two models, $ForeGAN\_RGB$ and $ForeGAN\_D$ are trained in way different way; they are also tested discretely, to improve the performance of the foreground object detection.
The trained model for color information $ForeGAN\_RGB$ is tested in such a way that the input test sequences are first multiplied with motion mask to eliminate the moving foreground object information (see Figure \ref{stepby} (3)).
This step helps our trained network to generate the background image sample with similar information by back-propagation steps.
Later this generated background image is subtracted with input test sample to extract the foreground objects (Figure \ref{stepby} (a), (c) and (d), Figure \ref{fig_framework} (5)).
The $ForeGAN\_D$ is also evaluated in the same way, but the only difference is that the testing sampling is given as it is to the trained model which generates the similar depth information as test sample but it will be a background scene.
After that pixel by pixel subtraction of generated background depth information and testing sample gives us foreground object segmentation with a lot of noise in it mostly as shown in Figure \ref{fig_framework} (8).
So motion masks are multiplied with foreground objects detected via depth information to eliminate the noisy background depth pixels as shown in Figure \ref{fig_framework} (8), (9) and (10) respectively.
The last step is to fuse the information of foregrounds detected by following fusion technique from both models to get the final output
$$
ForeGAN\_RGB + ForeGAN\_D = ForeGAN\_RGBD
$$
\begin{table}
\centering
\caption{Comparison of average $F$-measure score on SBM-RGBD dataset \cite{camplani2017rgb} concerning depth and color features. The first and second best performing methods are shown in red
and blue colors respectively.}
\scalebox{0.9}{
\label{f_table_rgbd}
\begin{tabular}{l*{6}{c}r}
\hline
Methods       & Depth features ~~~& RGB features  ~~~~&\\
\hline
BS-GFL         & 0.21      & 0.63 & \\
DECOLOR        &  0.20     & 0.56 &  \\
cwisardH+      & --- & --- & \\
RGB-SOBS        & --- & \textcolor{blue}{0.70} & \\
SRPCA           & \textcolor{blue}{0.24}  &  \textcolor{red}{0.71} & \\
ForeGAN\_RGBD   & \textcolor{red}{0.45}   & 0.51 & \\
\hline
\end{tabular}
}
\end{table}
\begin{table}
\centering
\caption{Comparison of average $F$-measure score on SBM-RGBD dataset \cite{camplani2017rgb}. Figure \ref{comp_fig} represents visual comparisons with other methods. The first and second best performing methods are shown in red and blue colors respectively.}
\scalebox{0.9}{
\label{f_table}
\begin{tabular}{l*{6}{c}r}
\hline
Categories       & BS-GFL & DECOLOR & cwisardH+ & RGB-SOBS & SRPCA & ForeGAN\_RGBD &\\
\hline
Illumination Changes  & 0.2991 & 0.4861 & \textcolor{blue}{0.4581} & 0.4527 & 0.4454 & \textcolor{red}{0.8158}\\
Color Camouflage      & 0.7333 & 0.7030 & \textcolor{blue}{0.9510} & 0.4864 & 0.8329 & \textcolor{red}{0.9635}\\
Depth Camouflage      & 0.7540 & 0.7252 & 0.7648 & \textcolor{blue}{0.8935} & 0.8083 & \textcolor{red}{0.9360}\\
Out of Range          & 0.7182 & 0.5874 & \textcolor{red}{0.8987} & 0.8527 & 0.8011 & \textcolor{blue}{0.8726}\\
Shadows               &0.5869 & 0.9051 & \textcolor{blue}{0.9264} & 0.9218 & 0.7591 & \textcolor{red}{0.9271} \\
Bootstrapping         & 0.5711 & 0.7601 & 0.5669 & 0.8007 & \textcolor{blue}{0.8098} & \textcolor{red}{0.8646} \\
\hline
Average               & 0.6104 & 0.6482 & 0.7609 & 0.7075  & \textcolor{blue}{0.7698} & \textcolor{red}{0.8966} \\
\hline
\end{tabular}
}
\end{table}
\subsection{Foreground Object Segmentation by ForeGAN\_RGBD}
To highlight the significance of our proposed GAN model, we compared it with $5$ state-of-the-art methods by $F$-measure as shown in Table \ref{f_table}. By using original implementations of the authors, we have compared our proposed method with BS-GFL \cite{xin2015background}, DECOLOR \cite{DECOLOR}, cwisardH+ \cite{de2017cwisardh}, RGB-SOBS \cite{maddalena2012sobs} and SRPCA \cite{javed2017moving}. Qualitative comparisons of our proposed method are presented in Figure \ref{comp_fig} and $F$-measure score comparison with $5$ state-of-the-art methods are presented in Tables \ref{f_table_rgbd} and \ref{f_table}. It can be seen in Table \ref{f_table_rgbd} that our proposed method has performed well regarding foreground object segmentation by using depth features as compared to all methods. However, by using only RGB features the $F-measure$ is quite low which means our ForeGAN\_RGB model does not detect the foreground objects very well. The main reason behind this fact is that sometimes there are too much background noisy pixel values in frame difference detection of background-foreground images. Nevertheless, this problem is eliminated in ForeGAN\_D model because after frame difference of background depth image generated by our network and test sample; it is masked with optical flow motion information as shown in Figure \ref{fig_framework}. But upon combining the features of depth as well as color information the $F-measure$ is increased significantly in the case of our proposed algorithm as compared to all methods as shown in Table \ref{f_table_rgbd}. \\
The detail explanation of category wise comparison of our proposed method with $5$ state-of-the-art methods is as follows:\\
\textbf{Category: Illumination Changes} contains 4 video sequences in SBM-RGBD dataset \cite{camplani2017rgb}. The average $F$-measure scores among all four video sequences are shown in Table \ref{f_table}. It can be seen in Table \ref{f_table} that our proposed method ForeGAN\_RGBD has achieved maximum $F$-measure score among all the compared methods. However, the second best performing method is cwisardH+ among all the compared methods. Since this category represents challenging video sequences with several illumination variations it can be seen in Table \ref{f_table} that all compared methods except our proposed method have a very low $F$-measure score. It is because illumination variations pose a lot of challenges to all compared methods, but our proposed method can generate the background sequence with exact illumination condition. This aspect of our proposed method favors precise foreground detection with challenging illumination conditions. Visual results are represented in Figure \ref{comp_fig}.\\
\begin{figure}[H]

	\includegraphics[scale=0.39]{Figure_3.png}

	\caption{Performance comparison of our proposed method with 5 state-of-the-art methods.  From left to right: each input sequence is selected from different categories (a) 'fall01cam0' category 'Bootstrapping', (b) 'colorCam1' category 'Color Camouflage', (c) 'Wall' category 'Depth Camouflage', (d) 'genSeq1' category 'Illumination Changes', (e) 'MultiPeople1' category 'Out of Range' and (f) 'shadows1' category 'Shadows'. $row~1$: 6 images from input test video sequences $row~2$: Ground truth, $row~3$: foreground detection by our proposed ForeGAN\_RGBD method, $row~4$: SOBS-RGB, $row~5$: DECOLOR, $row~6$: SRPCA and $row~7$: BS-GFL.}
	\label{comp_fig}
\end{figure}
\textbf{Category: Color Camouflage} also contains 4 video sequences including foreground objects that are very close in color to the background in SBM-RGBD dataset \cite{camplani2017rgb}. The average $F$-measure scores among all four video sequences are shown in Table \ref{f_table} and it can be seen that only our proposed method and cwisardH+ method has performed well. This category contains challenging video sequences in which foreground objects are very close in color to the background. Still, our proposed method achieved the highest $F$-measure score. The main reason is that as our algorithm extracts foreground objects from RGB and depth models independently, so if even one of the model suffers performance degradation the other model can detect the foreground objects. Qualitative results are shown in Figure \ref{stepby} and \ref{comp_fig} in comparison to ground truths and state-of-the-art methods respectively. \\
\textbf{Category: Depth Camouflage} also contains 4 videos including foreground objects very close in depth to the background in SBM-RGBD dataset \cite{camplani2017rgb}. It can be seen Table \ref{f_table} that our proposed method has outperformed all the compared methods and  RGB-SOBS method has achieved the second best score. The top performance of our proposed method is due to the same reason mentioned previously in color camouflage challenge discussion. The visual results are represented in Figure \ref{stepby} (d) row 4 and Figure \ref{comp_fig} (c). It can be seen in the qualitative analysis that our proposed method has better results than all the compared methods except RGB-SOBS method which has performed second best in this category. \\
\textbf{Category: Out of Range} has 5 videos sequences including foreground or background objects that are too close to/far from the sensor \cite{camplani2017rgb}. It can be seen in the Table \ref{f_table} that our proposed method has achieved second best $F$-measure score 0.8726 however cwisardH+ method achieved best score 0.8987. The reason is that sometimes our proposed method cannot generate the perfect background sequence for foreground objects which are too far from the sensor. Visual results presented in Figure \ref{comp_fig} (e) which shows the comparison of our proposed method with all compared methods and ground truth information as well.\\
\textbf{Category: Shadows} also contains 5 videos showing shadows caused by foreground objects. These can be visible-light shadows in the RGB channels or IR shadows in the depth channel \cite{camplani2017rgb}. In this challenging category, our proposed method has also achieved the highest $F$-measure score 0.9271 but the cwisardH+ method also achieved the best score with a minimal difference as shown in Table \ref{f_table}. The visual results shown in Figure \ref{stepby} (d) row 2 and Figure \ref{comp_fig} (f) represents that our proposed method can detect foreground objects even in the presence of shadows.\\
\textbf{Category: Bootstrapping} also contains 5 video sequences including foreground objects in almost all their frames \cite{camplani2017rgb}. It can be seen in Table \ref{f_table} that our proposed method ForeGAN\_RGBD has also achieved highest $F$-measure score and SRPCA has achieved second highest $F$-measure score. It is a very challenging category for all compared methods because it contains video sequences including foreground objects in almost all their frames but still our proposed method outperformed all compared methods. The main reason behind this fact is that our proposed method can learn the background scene information even if the foreground objects have left the scene once. This aspect of our proposed GAN network improves its efficiency in foreground object detection with challenging scenarios like bootstrapping. For instance visual results are shown in Figure \ref{stepby} (c) and (d) row 1 and Figure \ref{comp_fig} (a).
\section{Conclusion}
In this study, we present the foreground detection algorithm based on generative adversarial network (GAN) exploiting color as well as depth information. Our goal is to do foreground segmentation in the presence of various major challenges in background scenes in real environments. To handle these challenges we present a fusion based foreground detection algorithm which exploits color as well as depth information, with the help of Generative Adversarial Network (GAN). Our proposed GAN model is trained in an unsupervised fashion on color and depth information independently with various challenges.  After that for testing, the GAN model has to generate the same background samples as test samples with similar statistics via back-propagation technique. The generated background samples are then subtracted from the given test samples to detect foreground objects from color as well as depth information, and final results are calculated by the fusion of both model's segmented outputs. The comparison of our proposed method with five state-of-the-art methods highlights the strength of our algorithm for foreground object segmentation in the presence of various challenging conditions.
\section{acknowledgements}
This research was supported by Development project of leading technology for future vehicle of the business of Daegu metropolitan city(No. 20181030).

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Web sites depend on a lot of images, and with higher resolution displays, their size will only increase. Yet website performance is critical as well; you want a great looking image in as small a file size as possible.
That's where TinyPNG comes in handy. Cut your png of choice and use the site's drag and drop uploader. TinyPNG will strip colors and compress pngs down to 8 bits while still retaining 100% transparency. I've used it on a few recent projects, and every thing I send to it reduces the file size by at least 50% with no noticeable image degradation.


Nov 29th - CHOOSING A VIDEO CARD GUIDE Complete! Click here!
Nov21st - Infix EVAL is COMPLETE! Check it out! Plus , added more video card reviews!
Nov 8th - I changed the name of the page (still lame though) plus HARDWARE section is UP!
Come and check it out! I am working on the Music page and that should be up by monday.
Hardware page right now. So it should be up later on today or tommorow.


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For any proposals or writing assignments, contact me on gauravbhan@gmail.com or via my social media channels.
Amazingly great work in a short span of time! You've milked the opportunity well and presented your experience impressively. Congrats! I'd like to follow you and be in touch. I'm learning the alphabet of bloggong.
Thank you very much Dinesh. Your words are a real encouragement. I hope to see you again on my blog.


A glamorous accessory is the ideal way to transform your Christmas party look – add a pop of sparkle, burst of colour or flash of embellishment to a little black dress for red-carpet luxury.
Is there a more iconic clutch bag than the Lulu Guinness Lips Clutch? In shiny, lipstick-red Perspex, the lip-shaped clutch from Lulu Guinness is a Christmas party staple. Loved by celebrities, bloggers and party-goers everywhere, this statement clutch needs pride of place in your wardrobe.
Oversized, shaggy and made from tri-coloured Mongolian fur, the House of Holland Fur Clutch is most certainly a standout piece. Wear as a cross-body thanks to the delicate chain or simply as a clutch bag – either way, this clutch is guaranteed to turn heads this festive period.
Unleash your inner animal with the leopard-print Ted Baker Valerie Exotic Cross Body. In soft, animal printed leather, this Ted Baker bag features a detachable gold chain and lined interior, and looks beautiful paired with an all black ensemble.
Inject a pop of colour into your Christmas party outfit with the Aspinal of London Letterbox Saddle Bag in cobalt blue. In smooth Italian calf leather featuring gleaming silver hardware, this Aspinal of London handbag is quintessentially British and the epitome of luxury.
Fun, fresh and always eye-catching, a pop of glitter is the perfect way to inject some playfulness into your winter wardrobe this party season. A statement sparkling clutch bag should be complemented with minimalist accessories to remain the star of the show – we're loving the Lulu Guinness Taped Face Glitter Clutch this Christmas!


Rich Ortiz, an acoustic one man band, singer/songwriter. Talented musician Rich Ortiz style is truly his own and his music is his soul. Since the age of seventeen Ortiz has focused all of his talents and time on his acoustic solo act, if you can call it that. Rich plays guitar, harmonica, and peddle bass (with his feet), and he turns heads with his vocals!
He has opened shows for Ludacris, Hoobastank, Jeffrey Gaines, Ryan Montbleau Band, The Wood Brothers, Tea Leaf Green, Kracker, Merl Saunders, Melvin Seals, Jim Weider and the Drifters.
$10 general admission. Tickets available at the door. 210 Main St, Hudson Falls, NY 12839. Municipal parking available. For more information or reservations, please call 518-832-3484.


text:
Daniël Jean Sanches (↑1912), Amsterdams gemeenteambtenaar
 Francisco Sánches de Scepticus (ca. 1550-1623), Portugees filosoof
 Renato Sanches, Portugees voetballer (1997)
 Simon Sanches (1915-2002), creool die poogde een staatsgreep te plegen
 Stacy Sanches (1973), Amerikaans model en actrice

Zie ook
 Sánchez (doorverwijspagina)

redpajama_set_name: RedPajamaWikipedia


Modern Luxury asked us to create a styled shoot that evoked a moodier and more dramatic color palette. We took our inspiration directly from this brand new venue located just south of Atlanta. With rolling hills as far as the eye can see, cozy cobblestone patios and lush lawns under canopies of ancient trees, The Venue at Murphy Lane conjures feelings of the French Countryside. We aimed to highlight these charming features with details such as the Fleur de lis table and cane back chairs, lavender lilac and cherry blossoms, and a regal off-the shoulder beaded gown by Anne Barge.
evoking a feeling of warmth that could be used in any season. For this styled shoot, we chose to use copper table numbers and wedding invites. We love how the color itself is a great accent to the rest of the colors found throughout the wedding. The cake has a modern geode design which we love and even uses a little copper colored accents to really bring this theme all together.
Let us know what you think of this styled shoot! Below is a list of vendors that helped make our vision come true and we're confident they can do the same for you!
Follow us on social media to keep up with what we are working on!


Folders are made from either 280 gm Gloss or 400 gm Silk Art card, they are printed on one or both sides in either Spot Colour or Full Colour.The standard folders can be specified for eight A4 or A5 size inserts. We also have a 'Design Your Own Folder' section where any other folder can be priced.
Lamination. Folders can be laminated with Gloss or Matt laminate on one or both sides.We are also able to Spot UV Varnish(at an extra cost), please ask for details.
Assembly. Folders are supplied flat with interlocking pockets or apply double sided tape for easy assembly at time of use.Alternatively they can be supplied fully assembled if preferred.
Please note:Assembled folders are very bulky.If you require assembled folders to be delivered we may have to revise our carriage cost to reflect volume rather then weight.
Gussets. All folder designs can be specified as Gusseted or Ungusseted.Ungusseted folders are perfect for holding the equivalent of around 10x80 gm sheets of paper. If substantially more inserts are proposed then gusseted folders are recommended. We can supply 2 mm or 4 mm gussets, please specify at time of ordering.


Up and coming Washington,D.C. based hip hop artist Antonio "OG Man Man" Leach was murdered Sunday in Southeast DC. The 23 year-old rapper was shot and killed after dropping the video for his dis record Truth.
Related Items:celebrity gossip, murdered, OG Man Man, Washington D.C.


The word "Extraction" can mean many different things. If you're a dentist, it means pulling a tooth; if you're a soldier, it means removing someone from a hostile area. And if you're a TimePilot user, it means something totally different, but equally important.
As employees clock in and out of your system, their clock-ins and clock-outs accumulate in TimePilot's Current Transactions database on your PC or server. They build up fast: If you have 15 employees, all of whom clock in or out four times a day ("in" to start work, "out" for lunch, "in" after lunch and "out" at the end of the day), that's 60 transactions a day, 300 a week, 1,200 a month, more than 14,000 a year.
That's why we strongly recommend extracting your transactions at the end of your pay period. When you do this, you remove the transactions that occurred between two times and dates (usually the start and the end of your pay period) from Current Transactions and place those transactions in a separate database. From there, you can run reports, make corrections to individual transactions, etc.
To visualize the concept, imagine the database is a huge pot. Every day, the TimePilot timeclocks dump more clock-ins and clock-outs into the pot. Now it's the end of a two-week pay period, so you want to remove just clock-ins and clock-outs for the last two weeks from the pot. With the TimePilot Central software, you "extract" just those transactions	and move them to a separate, smaller pot. In the smaller pot, it's much easier to see what you have and it keeps the big pot from eventually overflowing.
Along with the saved transactions, it also saves all the software settings—overtime policy, shifts, employee profiles, etc.—that were in effect at the moment you extracted the period. This is important because if you later make changes to your company's work policies while working in Current Transactions, those changes will not affect any extracted periods. For instance, the software will prevent you from deleting an employee until that employee's transactions have been extracted from Current Transactions. If the software didn't do this, and you deleted an employee, you'd have "orphan" transactions—transactions without an owner.
It enables you to save those transactions to a CD or backup drive.
Q. How do I view an extracted pay period?
A. Start TimePilot Central, click the "Periods" menu, then click "Open Past Time Period…" Choose your period (they're listed by date) and click OK.
Q. How do I know whether I've opened my Current Transactions or an extracted pay period?
A. If you're in Current Transactions, you'll see a green bar across the top of the transactions in TimePilot Central; if you're in an extracted pay period the bar will be yellow. Also, the title bar at the top of the TimePilot Central screen will contain the words "Current Transactions" or the dates of the extracted pay period.
Q. I extracted a pay period, but I got the dates wrong. What can I do?
A. You can return the extracted transactions to the database and re-extract with the correct dates. It's called a "reverse extraction" and, using the concept mentioned above, it's like dumping those incorrectly extracted transactions back into the soup pot and starting over. To perform a reverse extraction, open the incorrect extracted pay period in TimePilot Central, then click the "Administrative" menu and choose "Reverse Extraction." When that's complete, extract again, this time using the correct dates.


text:
Centuries: 7th century - 8th century - 9th century
Decades: 730s 740s 750s - 760s - 770s 780s 790s
Years: 765 766 767 - 768 - 769 770 771
768 in other calendars
Gregorian calendar 768
Armenian calendar 217
ԹՎ ՄԺԷ
Bahá'í calendar -1076 – -1075
Coptic calendar 484 – 485
Ethiopian calendar 760 – 761
- Vikram Samvat 823 – 824
- Shaka Samvat 690 – 691
Iranian calendar 146 – 147
Islamic calendar 150 – 151
Japanese calendar
Julian calendar 813
By Place[]
Europe[]
Death of Pepin the Short (714 — 768), king of the Franks since 751. He was succeeded by his sons Carloman (eastern Franks) and Charles aka Charlemagne (western Franks).
Deventer is founded (approximate date).
Aurelio succeeds Fruela I as king of Asturias.
By Topic[]
Religion[]
The date, according to the Annales Cambriae, when Wales began to celebrate Easter on the Roman date, at the teaching of Elbodugus.
August 7 — Pope Stephen III succeeds Pope Paul I as the 94th pope.
Han Yu — philosopher, essayist, and poet in Tang Dynasty China (d. 824)
Xue Tao
Pepin the Short, King of the Franks, father of Charlemagne (b. 714)
Fruela I of Asturias
This page uses content from the English language Wikipedia. The original content was at 768. The list of authors can be seen in the page history. As with this Familypedia wiki, the content of Wikipedia is available under the Creative Commons License.
People of the year 768 at Familypedia
2 people were born in 768
Theoderata (768-788)
Matthat ben Levi (c768 BC-) Levi ben Simeon
1 children were born to the 1 women born in 768
1 people died in 768
Pepin the Short (714-768) Charles Martel (686-741) Rotrude of Treves (690-724)
26 people lived in 768
Angilbert (c750-814) Nithard Richarde de Saint-Quentin (?-?)
Gerold of Vinzgouw (c725-799) Agilolf (690-)
Hnabi (c700-c775) Huoching (675-709)
Luitfrid II of Alsace (740-780) Luitfrid I of Alsace (707-750) Edith (719-?)
Tassilo III. von Bayern (c741-c796) Odilo von Bayern (c705-748) Hiltrud (-754)
Carloman (751-771) Pepin the Short (714-768) Bertrada of Laon (720-783)
Bernard (bef732-787) Charles Martel (686-741) Ruodhaid (c700-)
Charlemagne (747-814) Pepin the Short (714-768) Bertrada of Laon (720-783)
Gisela (757-810) Pepin the Short (714-768) Bertrada of Laon (720-783)
Nivelon II de Madrie (755-805) Nivelon I de Bourgogne (c715-c778)
Aaltje Stam (1811-1860) Pieter Stam (1775-1858) Sijbregje Leek (1777-1847)
Adrian d'Orléans (c755-bef821) Gerold of Vinzgouw (c725-799) Emma of Alamannia (730-789)
Hildegard (758-783) Gerold of Vinzgouw (c725-799) Emma of Alamannia (730-789)
Events of the year 768 at Familypedia
0 people were married in 768.
There were 0 military battles in 768.
0.076923076923077 1 0.038461538461538
... more about "768"
0.08 +

redpajama_set_name: RedPajamaCommonCrawl


This time last week I was in Austin with one of my best friends exploring the city. Yesterday I woke up in San Francisco in the Castro district to get coffee with a friend from UNC followed by brunch at a previous coworker's house. A month ago I was preparing for my cousin's wedding in Orlando, Florida. Today, I find myself in Seattle wandering around trying to find coffee. Up until this point in my life I've always had somewhere to be. A place where I was either required to be or a place where social obligations made it so. In a very odd sense, I now have nowhere in particular to be. It's freeing and frightening all at the same time. I could literally end up anywhere – Dublin or Denver.
Most people perk up at this idea of pure freedom. It is an incredibly luxury and I've been able to see so many wonderful people as a result that I probably wouldn't see for years. The conundrum is I don't know where to be or where I even want to be. If I think too much about this, I find myself craving a job where they would force me to move somewhere and stay. At least then the struggle would be to adapt to my surroundings which seems less looming than trying to adapt to the whole world. That's how it feels right now.
"What are you looking for?" I get asked this a good bit – community is the easy answer. I love pick up soccer and even jumped into touch rugby recently which was an absolute blast. I need overall good weather since I am a Florida person through and through. I want to live in an active and thoughtful place. Although it may seem I'm living haphazardly right now, I'm really am not.
So here I am faced with my very own "Choose your adventure" scenario. It's a blessing to have this decision. It's a curse to have to make it.
The feeling of having nowhere to be is two pronged – FREEEDOMMMMM & wait what is wrong with me where I don't have anywhere to be? I feel like whatever place I move to will affect everything about me – who I fall in love with, what hobbies I develop, how active I'll be outdoors, etc. It's like a mini way of playing God with a sliver of your own life. I bounce back and forth from feeling powerful in choosing the direction of my life and sad that I don't already have that direction.
Most of my life has been spent living as though where I am is a constant that would never alter. I could firmly place two feet on the ground and know that I could trust that feeling. People I met randomly could easily be friends as I very well might meet them again. Last week in Austin I played touch rugby and dragged my best friend into it. At the end, the folks from the meet up wanted to keep in touch. Without skipping a beat, I informed them I wouldn't be in Austin in a couple of days. My friend who has known me for years remarked how odd it was that I didn't jump to make that connection – I think he was surprised at how okay I was at just letting it be. I've gotten good at this – at investing wholeheartedly wherever I am yet knowing I will soon be on to the next adventure. I now plant my feet somewhere new and can feel how temporary the sensation is. Something like your surroundings and where you live has gone from being consistent to being consistently unpredictable. It's beautiful – the one off conversations and the wonderful rendezvous with loved ones. It's disorienting – I wake up everyday unsure of what to expect and having to remind myself of where I am. Its changed my relationship drastically with my environment. At no point in time does anything around me feel like "mine". I think this is a wonderful thing as nothing we own is ever really ours.
I'll be honest and say I don't know which adventure to choose. In the last week, I've talked about moving to Dublin, Vancouver, Austin, San Diego, Seattle, and Oakland. I read that list through and practically shrug my shoulders thinking about where I might end up. After having so many aspects of my life planned out, I'm letting go more and more of that desire to have that sense of control and consistency. I don't have plans for the entire month of May – I don't know where I'll live, what I'll be doing, who I'll be with, etc. After July 4th, I have literally nowhere to live or be. That's as far as my plans go. After spending too much time letting society dictate where to be (think college) or people in my life (parents or significant others), I'm having a very hard time figuring out where it is that I want to be.
I say this often but we have to live with the choices we make not others. We have to live with the life we create. I say this because it helps recenter me from making decisions for the wrong reasons and because it reminds me how much sway we can have with other's decisions. I've never had to ask myself where I want to go or be without limitation. Even with college, I could at least narrow things down to where I got in and where I could afford to go. It's this lack of direction that already has me clinging to where others might move to make my decision easier. I can't tell if this is good or bad. For now though, I refuse to make a decision. There's too much I'm learning about myself and others to take that opportunity away from myself. I'm seeing the value in not making a decision and in letting life play out in whatever way it will.
It's a journey. Enjoy the trip!


3/4: Wersinger & Cerruti Medal in AC!
Peter Wersinger & Kevin Cerruti became the Green Wave's first State Medal Winners since 2010 as Wersinger placed 8th at 195, and Cerruti finished 7th at heavyweight. For Wersinger, it caps a career that saw him finish with 106 wins, and a pair of Region medals, and three District medals, as well as a championship in both, to go along with his State Medal.
Cerruti won four bouts to finish in 7th place in his 2nd trip to the State Tournament. He is the first non-senior to reach the podium for Long Branch since 2009.
Peter Wersinger & Kevin Cerruti became the Green Wave's first Region Champs since Luis Filipe in 2011, and Ryan Zimmerman finished 2nd, as the Green Wave advances three wrestlers to the State Tournament in Atlantic City. It will be the 2nd trip to Boardwalk Hall for all three, who all qualified last season as well.
Eleven starters, including 8 Region qualifiers and 3 State qualifiers, return from last season's 20-win, District 23 championship team, setting the Green Wave up, once again, for the potential for a big season.
With the eight returning Region participants spread all throughout the lineup, the strength of the lineup is its balance. LB has a great stretch down low with returning State qualifier Ryan Zimmerman, who will be one of the top 106-pounders in the Shore, 3-time District medalist Chris Dean, and District medalist Rey Guzmon. That trio will be complimented by a healthy amount of newcomers with solid potential, giving the Wave great depth and flexibility.
Isaac Shohet, a D23 runner-up in 2017, enters his 3rd season as a starter and is joined by Nick Luhn, Louie Saiez, and Christian Gonzalez to provide a strong senior presence in the middle of the lineup. Among the group joining them are Ryan Carey and Stivini Silva, who have gotten plenty of valuable experience as underclassmen, and another of LB's frosh with lots of potential in Jack Friedman.
From 160-up, the Wave are going to be tough. Long Branch has always been able to produce strong upperweights from seemingly out of nowhere. If that trend continues this year, in addition to all the talent they have coming back up top, this is going to an extremely tough and deep group. Two State qualifiers are back in senior Pete Wersinger at 195, and junior Kevin Cerruti at heavyweight. They're joined by Luke Arnold and Dan Santos-Silva, who both finished 2nd in District 23 in 2017. We'll keep an eye on those sophomores expected to see time in the lineup as well, as we look to see if LB pulls more magic with the big bodies.
This is a very balanced, very deep, and very talented team. The amount of the experience they have is very beneficial as there is a big test early in the season as the Wave travel to Middletown North on Friday, December 22nd for a big B-North showdown against the defending division champs. In addition to the other B-North dates, circle January 10th on the calendar as Long Branch hosts Raritan in what is always a great atmosphere. This has the makings of a very strong dual team that will be in the hunt for both B-North and the Sectional Titles. Individually, there should be plenty of LB green qualifying for the Shore Conference Tournament, and they're a safe bet to have another strong individual campaign come the post-season. All said, it should be another big year for head coach Danny George and his Green Wave.


Become an Individual Member of CPF Today!
Whether you're a parent, grandparent, teacher, student, or any other supporter of FSL education, you can take out a membership in CPF. Individual memberships include one (1) voting adult members per household and can grant you access to all kinds of benefits and member-only resources! Children of your own are no requirement – so long as you are 18 years old, you can join CPF.
Uppercase letters and certain punctuation are not allowed in a Username. Use only lowercase letters, numbers, periods, hyphens and/or underscores.
Occasionally, the CPF membership list may be made available to other groups/agencies to offer members special benefits or education-related information. Use of the list will be carefully regulated and only permitted under a contract specifying confidentiality and one-time authorization.
Please enter you school in the dropdown menu below.
If your school does not appear on the list, please contact Cheri Nixon to have your school added. This is not a mandatory field and can be left blank.
Please choose your School Jurisdiction (SJ) from the dropdown menu below.
SJ refers to the name of the school board or district where your child attends school. You can search by name to find your school jurisdiction. Choosing this will automatically populate the Branch you belong to. If you need assistance with choosing which SJ your school belongs to please check the following list of SJ's or contact Debbie Murphy at dmurphy@cpf.ca or 613-235-1481 ext 236.


Check out information about the Achilles Tendon, related problems, and solutions.
Share your experiences and help others who are going through the same thing you've experienced.
Join the community to give and get support. You can also gain rewards to cheer you up as you recover.
Get the latest news and views on Achilles tendon health. An informed patient is an empowered patient.
More than 200,000 sports-related Achilles tendon injuries are reported each year, along with another 100,000 that aren't sports-related. While Achilles tendon injuries are more common in men over 30, women and children also experience these injuries. Causes range from a bad start on a sprint to overuse over a period of years to wearing high heels. Treatments include rest, ice, surgery, and electric shock therapy.
The Achilles tendon is the longest tendon in the body and one of the most vulnerable, so it's no surprise that there are lots of Achilles tendon injuries or even that these injuries vary a lot. But it can be very frustrating when you're in pain and there is so little in the way of firm answers.
Should you consider surgery, or stick with one or many of the popular home treatments? How long will you be out of commission? What activities should you avoid?
Should you go to a doctor? If so, what kind of doctor? And when that doctor tells you to take it easy and let your tendon heal — which they often do — what exactly does that mean? We're not providing medical advice here, but many of us have experienced the frustration of getting that very simple, common answer. Fortunately, we can also share our experiences and our insights. An informed patient is an empowered patient. Knowing the common experiences of people who've lived with Achilles tendon injuries helps you ask the right questions when you visit your doctor.
We plan to be the best source of information about Achilles tendon injuries, surgery, and recovery on the web, and to provide the most supportive community. Join us as we get started and grow along with us.


This procedure should be used with care!
Warning – getting this wrong could well mess up your printer, so do it at your own risk. This info is from Lyson and they won't take responsibility for damage either!
Press and hold the < + ^ + > buttons and power up the machine.
When the LCD display shows SELF TESTING, release all buttons.
Press ¥ until you see SERVICE CONFIG on the display.
Press > once, the display will read CHG DEVICE-ID.
Press ¥ until the display shows ED MODE.
The printer will power up and show 'Ready' with (0XXXXXXXX) at the ink cartridge status bars.
This setting is written to the printer eprom and won't change when the power is switched on and off.


text:
Theatre Chat: Nina Does Cbus
Lisa Much
Andrew Levitt never planned to call Columbus home. Rather, he thought he would hang around for a bit and test out a drag character, Nina West, for a few months before heading to New York. That was 2001. He never left.
Levitt received a BFA in Theatre from Denison University in the spring of 2001; during that summer, per the encouragement of queen Virginia West, Levitt tried out the theatricality of Nina West. He imagined he would go to New York in the fall, but with September 11th his plans were postponed. Not quite a year ago, the now famed Columbus arts advocate Levitt and drag celebrity Nina West thought about taking the plunge to the eastern city, and decided to devise a farewell show.
Nina Does Cbus came about when Levitt reached an impasse in his life about whether to really lay down roots in the capitol city or to make that transition. During the six month creation and writing process, he realized that he had to stay. "Columbus has always been like this love affair for me. I really love this city. It always comes back to the people for me."
Thus spoke Levitt, and thus will speak Nina as she celebrates Columbus starting this weekend at Nina Does Cbus. Do not expect a typical Nina show; the usual twenty person cast downsized to seven and the writing flows a bit more introspectively. Levitt found a new challenge drawing from his experiences in the city to translate them to Nina's perspective. He tries to combine reality with the colorful, fun, brightness that people love with a drag show.
"People expect the unexpected and expect to be entertained. I really think I'm delivering on that." The high production value remains, with video, boisterous costumes, and other refined technical elements that Columbus audiences expect of their drag shows, but the piece offers a slightly different feel than usual. "[The audience] will like it or they'll hate it, but they're still going to be entertained."
The collaborative nature of the show does a lot to shape the story. All of the other drag queens and kings brought something different to the production process and added their favorite spots to the mix, including the Columbus Zoo; Hollywood Casino; Jeni's; Late Night Slice; and Red, White, and Boom. Collectively, the performers create a list of the finest attributes and quirks of the city, while highlighting the uniqueness of the city's drag scene. "People walk away having connected to the show and to the entertainers and the LGBT community in a different way. We're really lucky here. That's something I'm celebrating."
Levitt interviewed many Columbusites to prepare this show, and it always came back to the same idea. No one anticipated calling Columbus home, but one thing or another occurred, and they all ended up staying. That holds true for Levitt and Nina, as they love their home base of Columbus and nurture their roots. "In Columbus, you can do anything and you can be anybody."
While he has no immediate plans for the next step, he never wants for work and likes to keep his options open for creative opportunities. As Columbus grows, Levitt and Nina both grow, and all three shape each other. "I'm really thrilled that I stumbled upon this city. [Staying here has] become one of the greatest things I've ever done."
Nina Does Cbus opens Friday, March 29 and runs through April 7 at Axis Night Club, 775 North High Street. Show times vary, but the doors open one hour prior to the performance. Tickets available at axisonhigh.com.
Photo by Elizabeth Lessner.
Lisa Much is a recent transplant to Columbus as of June. She comes from Chicago having freelanced as a stage manager and prop designer. She holds a BA in Sustainable Theater from Green Mountain College in Vermont.
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Desert Storm Command
Desert Storm Command Deluxe (full version title)
Pod Bay Enterprises
Official game website, MobyGames
modern warfare, Gulf War
You can play Desert Storm Command on this website so you don't need to download and install the game on your computer. We recommend to use Google Chrome when playing DOS games online.
Choose the file below to download this game.
dsc2mvp.zip
executable: DSC.EXE
737 kB (0.72 MB)
What do you think of Desert Storm Command? Please rate the game below on a scale of 1 to 10, where 1 is the lowest and 10 is the highest score.
Desert Storm Command is a collection of four arcade minigames themed after the Gulf War.
The game was sold as shareware, and the unregistered version (simply called Desert Storm Command, as opposed to the Deluxe registered game) only includes two minigames. In "Combat", the player needs to provide cover for friendly troops that try to cross an area while being sniped by the enemy. In "Scuds", the objective is to defend a city from missiles and bombs raining upon it using a stationary missile defence system. Registering the game would allow access to two more scenarios, "Tanks" and "Tomahawks".
Desert Storm Command includes a practice mode, allowing the player to familiarize with each scenario. All minigames are played with the mouse.


The little town of Tenno with his castle is founded in the middle age, one of the most lovely village of Trentino where rural houses are huddled together and joined by porticoes, halls, courtyards and ancient vaulted arches.
The castle overlooks the ancient village of Frapporta and the little church of San Lorenzo, one of the most notable examples of Romanic art in the Trentino region.
Tenno is only 2 Km from the lake Tenno, one of the most cleanest stretch of water in Trentino, characterized by its intense turquoise colour and small islet.
From here you can walk throw wonderful paths always with a wonderful view to Riva del Garda, Arco, the lake Garda and behind it the imposing mountains. You can also do mountain bike and nordic walking, swimming and fishing in the little lake of Tenno, 2 Km far away, it is characterize for his turquoise color.
with its crystal clear water and uncontaminated surroundings, is an island of tranquillity, and ideal for swimming. It is called "the blue lake" because the surrounding forests and the white stones of its bed give it a blue-green hue. The Tenno lake can be reached only on foot, after a short walk down a flight of steps dating back from medieval times. And if you need only relax this is the best place to enjoy the colors of the nature!
Tenno is only 5 Km from Riva del Garda and the lake Garda, where you can practise water sports like windsurfing and sailing.
5 Km there is Arco very important for free climbing and mountain climbing. Other sposts like horse riding, paragliding, canyoning, trekking are possible to do.
To visit there some little town like Canale, declared one of the most beautiful village of Italy, a precious mountain village immersed in the soft Mediterranean atmosphere of Lake Garda.
The Casa degli Artisti (Home of the Artists), dedicated to the artist Giacomo Vittone, has its seat here and is a residence and meeting place for artists from whole Europe.
We're also close to: the water fall of Varone and the lake of Ledro.


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The acronym "SEO" can refer to "search engine optimizers," a term adopted by an industry of consultants who carry out optimization projects on behalf of clients, and by employees who perform SEO services in-house. Search engine optimizers may offer SEO as a stand-alone service or as a part of a broader marketing campaign. Because effective SEO may require changes to the HTML source code of a site, SEO tactics may be incorporated into web site development and design.


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UIC, DePaul halt in-person classes, but other Illinois colleges take different tack as coronavirus cases spike
The University of Illinois at Chicago and DePaul University suspend in-person activities, but other schools maintain on-campus activities.
By Adam Mahoney@AdamLMahoney Nov 13, 2020, 2:58pm CST
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Southern Illinois University Chancellor Austin Lane (left) speaks with students on campus in September. This week SIU joined the University of Chicago and the Universities of Illinois at Springfield and Urbana-Champaign in allowing in-person activities despite a COVID surge.
The meteoric rise in coronavirus cases has been met with different responses from Illinois colleges and universities.
This week the University of Illinois at Chicago and DePaul University chose to suspend in-person student activities, while the University of Chicago, Southern Illinois University Carbondale and the Universities of Illinois at Springfield and Urbana-Champaign will continue in-person activities under their individual protocols.
Lightfoot issues stay-home 'advisory,' puts 10-person limit on social gatherings
Statewide stay-at-home order looms as Illinois shatters record again with 15,415 more coronavirus cases
In an update shared by the University of Illinois at Chicago on Wednesday, the school announced that all in-person classes would be suspended by the end of this week and moved to online platforms until the end of the fall semester. The school had 135 employees and students test positive last week, according to its COVID-19 dashboard.
"Given our location and the continuous interactions with the community at large, we cannot completely mitigate these effects, as indicated by the most recent results of our surveillance testing," Chancellor Michael Amiridis said in a statement. "And while our contact tracing efforts clearly demonstrate that there is no transmission in our classrooms or our labs, out of an abundance of caution, we must now implement stricter measures to protect the health and safety of our community members and curb the spread of this disease."
DePaul cited Mayor Lori Lightfoot's stay-at-home advisory as a factor in announcing that all face-to-face classes and exams will move online after the school had six positive cases last week, according to their COVID-19 dashboard.
Further south, the University of Chicago urged members of the school community to stay cautious but maintained that limited in-person activities would continue for the rest of the term. Last week the campus had 15 positive cases.
Last month, the university announced a plan to increase its number of in-person classes during the winter.
On Wednesday, officials from Southern Illinois University Carbondale announced that COVID-19 mitigations in Carbondale would not impact campus housing, office hours or in-person courses for the next two weeks. However, the school plans to finish the semester remotely after the Thanksgiving break, according to university Chancellor Austin Lane.
Last week SIU reported 37 cases.
COVID-19 to cost University of Illinois campuses $270M this year, budget projects
Loyola, Northwestern plan to allow more students on campus in January despite COVID spike
After the University of Illinois at Springfield experienced its highest number of one-day positive cases from testing on Monday, the university issued a two-day pause on all in-person activities before reinstating all activities on Thursday. In-person instruction is planned to continue for the rest of the term.
Six positive test results were reported on Thursday after reopening the campus.
On Tuesday, the University of Illinois at Urbana-Champaign ordered faculty, staff and grad students to test twice weekly, ramping up its testing protocol which is already considered one of the most robust in the country. Since July, the school has administered nearly 800,000 COVID-19 tests.
Because of the testing system, the school plans to continue all in-person classes and activities for the rest of the term, but students who travel away from the Champaign-Urbana area over Thanksgiving are urged not to return to campus.
The school has had 315 positive cases in the last seven days.
Next Up In Education
CPS sees huge spike in students signed up for COVID testing since teachers walked out
Volunteers beautify West Side elementary school on day of service honoring King
CPS students walk out of class, rally for better COVID-19 safety measures, remote learning option
AG Kwame Raoul announces student debt settlement with Navient
Stalking the elusive present participle
Chicago teachers approve deal to end walkout over COVID safety protocols


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I'm not Casey and this isn't Mudville.™ A mostly Yankees blog, created By Bernadette Pasley
At Bat Since 2007
Our 12th season!
Lady At The Bat (LATB) is a (mostly) Yankee blog, published by Bernadette Pasley. Feedback, questions, suggestions? Advertising inquiries? Contact ladybatting@gmail.com.
Not Like Father, Not Like Son?
Last night Prince Fielder became the youngest player ever to hit 50 home-runs. Unfortunately, he won't be happy until he hits two more homers and is NL MVP, two things his father Cecil Fielder never did.
Most people know that this isn't joking or good-natured ribbing on Prince's part. He and his father are not on speaking terms, at least, not face-t0-face. They communicate "at" each other through the media. Cecil is on record as saying, "I don't think he's grown up yet. Until he can move on and talk to me like he's my son, we don't need to talk." The breakdown in the relationship appears to stem from the fact the Cecil gambled away all the money he made as a Major League player. This caused a lot of legal problems which Prince was subpoenaed for. He was actually served as he left the the ballpark one day when he was a minor leaguer.
I don't claim to know all the details of this family feud. But based on what I do know, when I put myself in Prince's shoes, I can understand how he feels. I can also understand how Cecil feels: he did some bad things in the past and he's being treated as if he's still doing them. I hope that the Fielders will be able to patch things up before one of them is no longer with us and the other one lives to regret it.
[Photo: Morry Gash/AP]
by Bernadette Pasley at 10:28 AM
Tags: National League
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Jeff Glor says his last 'CBS Evening News' set for Friday: report
By Joe Concha - 05/08/19 05:38 PM EDT
© CBS
Outgoing "CBS Evening News" anchor Jeff Glor reportedly announced in a Wednesday staff meeting that his last broadcast on the program will be Friday.
The Daily Beast report on Glor's exit comes less than 18 months after he was named lead anchor of the network's flagship newscast.
The program has struggled from a ratings perspective, however, regularly finishing in third place behind ABC's "World News Tonight" with David Muir and "The NBC Nightly News" with Lester Holt.
CBS announced on Monday that former "CBS This Morning" co-anchor Norah O'Donnell will replace Glor.
It is not clear what his role with the network will be moving forward.
O'Donnell, who has served as co-anchor of "This Morning" since 2012, will also serve as managing editor and as a contributor to "60 Minutes."
The network also said Monday that John Dickerson will be departing its morning show for "60 Minutes." Dickerson will also have a role in 2020 election specials.
Tags CBS CBS Evening News