<p>I decided to start learning neural networks by creating a bot for the game. One of the intermediate steps is to create a global map from a series of inaccurate overlapping sub-maps. This task can be solved using OpenCV, but this solution will be too limited and sensitive (in the future I intend to complicate the task and work directly with the map image, instead of binary masks).</p>
<p>I've tried the following options:</p>
<ul>
<li><p>predict the position of a new map area within the global map. (as a probability distribution)</p>
</li>
<li><p>predict the new state of the global map from the old and new minimap.</p>
</li>
</ul>
<p>I've tried a lot of options formulation of the problem of network architecture, including the idea of conjoined networks, but nothing gave any relevant results.</p>
<p>Some articles about solving similar problems:</p>
<ul>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1877050918315278" rel="nofollow noreferrer">Automatic Field-of-View Expansion using Deep Features and Image Stitching</a></p>
</li>
<li><p><a href="https://arxiv.org/abs/1809.04137" rel="nofollow noreferrer">JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition</a></p>
</li>
<li><p><a href="https://www.mdpi.com/1424-8220/19/23/5310/htm" rel="nofollow noreferrer"> Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization</a></p>
</li>
</ul>
<p>Here is an example of one of the options statement of the problem:</p>
<p><a href="https://i.sstatic.net/VCITr.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/VCITr.jpg" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2020-08-02T20:46:26.070Z
Can you please put your main question in the title?
nbro
CC BY-SA 4.0
2020-08-02T22:07:31.263Z
@nbro, thank you for your feedback! Is it better now?
Green_Wizard
<convolutional-neural-networks><image-processing>
How to choice CNN architecture for stitching images
Green_Wizard
# How to choice CNN architecture for stitching images

(Asked by: username_0 on 2020-07-31)

I decided to start learning neural networks by creating a bot for the game. One of the intermediate steps is to create a global map from a series of inaccurate overlapping sub-maps. This task can be solved using OpenCV, but this solution will be too limited and sensitive (in the future I intend to complicate the task and work directly with the map image, instead of binary masks).

I've tried the following options:

* predict the position of a new map area within the global map. (as a probability distribution)
* predict the new state of the global map from the old and new minimap.

I've tried a lot of options formulation of the problem of network architecture, including the idea of conjoined networks, but nothing gave any relevant results.

Some articles about solving similar problems:

* [Automatic Field-of-View Expansion using Deep Features and Image Stitching](https://www.sciencedirect.com/science/article/pii/S1877050918315278)
* [JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition](https://arxiv.org/abs/1809.04137)
* [Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization](https://www.mdpi.com/1424-8220/19/23/5310/htm)

Here is an example of one of the options statement of the problem:

[![enter image description here](https://i.sstatic.net/VCITr.jpg)](https://i.sstatic.net/VCITr.jpg)

**Tags:** convolutional-neural-networks, image-processing

> **Comments:**
> **username_1**: Can you please put your main question in the title?  
> **username_0**: @username_1, thank you for your feedback! Is it better now?  

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## 0 answers

