Rectangle Recognition in Images

Shape recognition in images involves detecting various geometric forms such as rectangles, squares, circles, ellipses, lines, points, and diamonds. Through analysis of image features and geometric properties, we can accurately identify these distinct shapes and perform subsequent processing and analysis. Common implementation approaches include using edge detection algorithms like Canny or Sobel operators to extract contours, followed by Hough Transform for line and circle detection, or contour approximation methods with functions like cv2.approxPolyDP in OpenCV to identify polygons. For rectangle detection, algorithms typically analyze corner points, aspect ratios, and parallel line relationships. Shape recognition finds extensive applications across multiple domains including computer vision, robotics, and image processing. By identifying and analyzing shapes within images, we can extract valuable information and further advance scientific and technological development.