Answers.Body: <p>Is the image taken from a constant distance?</p>
  
  <p>If yes, you'd need to scale the images to the same dimensions first of all. For few images say 100-500 images (more the better) you'd need to label the dataset by proper scaling.</p>
  
  <p>Once labeled, use it to train a <a href="https://github.com/gsurma/image_classifier" rel="nofollow noreferrer"><strong>CNN</strong></a> (Although best would be training a <a href="https://github.com/ry/tensorflow-resnet" rel="nofollow noreferrer"><strong>ResNet</strong></a>). Once trained with decent accuracy, test it for the rest of your dataset.</p>
  
  <p>I did something similar for one of my projects, check it out if you want to <a href="https://github.com/Ar9av/Feature-Extraction-from-Facial-Images" rel="nofollow noreferrer">here</a>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-20T03:08:57.977Z
Answers.Text: Thank you for your information and reference. Will check it.
Answers.UserDisplayName: ovezn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-19T09:10:37.010Z
Answers.LastActivityDate: 2019-11-19T15:36:22.150Z
Answers.LastEditDate: 2019-11-19T15:36:22.150Z
Answers.UserDisplayName: Arnav Gupta
Answers.Body: <p>If you have stero pairs, and you can identify the objects in the scene, you do not need a neural network, you can just use <a href="https://en.wikipedia.org/wiki/Triangulation" rel="nofollow noreferrer">triangulation</a>. </p>
  
  <p>If you need to identify which objects in the scene are the same, you have an <a href="https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/" rel="nofollow noreferrer">image segmentation</a> problem. Depending on your problem and the amount of data you have access to, you may be able to use simple techniques like clustering-based segmentation, or you may be able to use NN-based techniques, like <a href="https://arxiv.org/abs/1703.06870" rel="nofollow noreferrer">Mask R-CNN</a>.    </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-19T08:18:47.053Z
Answers.Text: I am supposed to work with NN only. My application is measuring the crack length from the underwater concrete images. I am aware of doing semantic segmentation using Mask R-CNN. But I don't know how to use it to measure the length. Could you please provide any suggestions?
Answers.UserDisplayName: ovezn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-19T15:26:44.783Z
Answers.Text: @ovezn If you have stero images of the same object, taken with a stero camera, you can use triangulation to determine depth (i.e. the distance of the object from the camera). As soon as you know that, you can determine the length of an object in the picture. So the only thing you really need to do here is identify the object in the picture that is a "crack". That last part is an image segmentation task. Although you could try to solve the rest of the task with a neural network too, that's likely to be both harder, and less effective, than doing it properly by exploiting the depth.
Answers.UserDisplayName: John Doucette
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-19T03:00:58.090Z
Answers.LastActivityDate: 2019-11-19T03:00:58.090Z
Answers.UserDisplayName: John Doucette
Body: <p>I have stereo pairs (left, right) images of concrete cracks. I want to measure the length of the crack from those image pairs. Which neural network is appropriate for measuring object dimensions from stereo images?</p>
  
  <p>Note: I am insisted to use the NN-based technique only.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-19T02:59:43.130Z
Comments.Text: Hi and welcome to this site! What do you mean by "images of a site"? Which site? Maybe you can provide an example of a pair of images with the object whose size you want to measure. I think that would clarify your problem.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-19T08:25:27.377Z
Comments.Text: Thanks for the intimation. I have edited my description.
Comments.UserDisplayName: ovezn
Tags: <neural-networks><reference-request><object-detection>
Title: Which neural network is appropriate for measuring object dimensions from stereo images?
UserDisplayName: ovezn
ThreadText: # Which neural network is appropriate for measuring object dimensions from stereo images?
  
  (Asked by: username_0 on 2019-11-19)
  
  I have stereo pairs (left, right) images of concrete cracks. I want to measure the length of the crack from those image pairs. Which neural network is appropriate for measuring object dimensions from stereo images?
  
  Note: I am insisted to use the NN-based technique only.
  
  **Tags:** neural-networks, reference-request, object-detection
  
  > **Comments:**
  > **username_1**: Hi and welcome to this site! What do you mean by "images of a site"? Which site? Maybe you can provide an example of a pair of images with the object whose size you want to measure. I think that would clarify your problem.  
  > **username_0**: Thanks for the intimation. I have edited my description.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2019-11-19. Score: 3
  
  Is the image taken from a constant distance?
  
  If yes, you'd need to scale the images to the same dimensions first of all. For few images say 100-500 images (more the better) you'd need to label the dataset by proper scaling.
  
  Once labeled, use it to train a [**CNN**](https://github.com/gsurma/image_classifier) (Although best would be training a [**ResNet**](https://github.com/ry/tensorflow-resnet)). Once trained with decent accuracy, test it for the rest of your dataset.
  
  I did something similar for one of my projects, check it out if you want to [here](https://github.com/Ar9av/Feature-Extraction-from-Facial-Images).
  
  > **Comments:**
  > **username_0**: Thank you for your information and reference. Will check it.  
  
  ---
  
  ### Answer by username_3 on 2019-11-19. Score: 2
  
  If you have stero pairs, and you can identify the objects in the scene, you do not need a neural network, you can just use [triangulation](https://en.wikipedia.org/wiki/Triangulation).
  
  If you need to identify which objects in the scene are the same, you have an [image segmentation](https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/) problem. Depending on your problem and the amount of data you have access to, you may be able to use simple techniques like clustering-based segmentation, or you may be able to use NN-based techniques, like [Mask R-CNN](https://arxiv.org/abs/1703.06870).
  
  > **Comments:**
  > **username_0**: I am supposed to work with NN only. My application is measuring the crack length from the underwater concrete images. I am aware of doing semantic segmentation using Mask R-CNN. But I don't know how to use it to measure the length. Could you please provide any suggestions?  
  > **username_3**: @username_0 If you have stero images of the same object, taken with a stero camera, you can use triangulation to determine depth (i.e. the distance of the object from the camera). As soon as you know that, you can determine the length of an object in the picture. So the only thing you really need to do here is identify the object in the picture that is a "crack". That last part is an image segmentation task. Although you could try to solve the rest of the task with a neural network too, that's likely to be both harder, and less effective, than doing it properly by exploiting the depth.  
  
  ---
  
  