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Python Code || Path Planning with Grey Wolf Optimization (GWO) ~xRay Pixy

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Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
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Multi-Block Local Binary Pattern || Calculate LBP Corner Pixel Values || 
https://youtu.be/o8qfJWQ_FG0

 Local Binary Patterns (LBP) is a simple and efficient technique used in image processing to describe the texture or patterns within an image. LBP is widely used for applications like face recognition and texture classification since it is easy to compute and very effective at capturing the texture in photos. Step How LBP WORKS:

  1.  For each pixel in the image, LBP looks at the pixel’s neighbors, typically the 8 pixels surrounding it in a 3x3 grid.
  2. LBP compares each of these neighboring pixels with the center pixel. If the neighboring pixel has a value greater than or equal to the center pixel, it's marked as 1; otherwise, it's marked as 0. This comparison forms a binary number for the pixel.
  3.  The binary number is then converted into a decimal value. This value represents the texture pattern at that pixel.
  4. By doing this for every pixel in the image, LBP creates a new image that highlights the texture information.
Difference Between LBP and MB-LBP 

  • LBP compares individual pixels to a center pixel in a small neighborhood (usually 3x3).
  • MB-LBP compares the average intensity of blocks (groups of pixels) to the average intensity of a center block. (Divide into Blocks, Compare Block Averages, Create Binary Pattern and Convert to Decimal)

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