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