Algorithm for Color Image Edge Detection

This article describes a color image edge detection algorithm based on J. van de Weijer's paper "Robust Photometric Invariant Features from the Color Tensor." The algorithm aims to extract edge information from color images and processes it through the color tensor to achieve robustness against illumination variations. The implementation typically involves computing the color tensor matrix from RGB channels, calculating eigenvalues and eigenvectors for edge strength determination, and applying gradient-based operations with photometric invariance properties. This method can be applied to various computer vision tasks such as object detection and image segmentation. Key implementation steps include: computing first-order derivatives using Sobel or Gaussian filters across color channels, constructing the color tensor through outer products of gradient vectors, and analyzing tensor eigenvalues to distinguish between edges and homogeneous regions. The algorithm provides enhanced feature information from color images by leveraging chromatic information rather than just luminance, offering more options and possibilities for subsequent image processing and analysis tasks. Code implementation often involves optimizing the eigenvalue computation using numerical methods like Jacobi iteration or singular value decomposition for real-time performance.