Edge detection stands as a crucial process within image processing, serving as a cornerstone in computer vision applications by transforming a 2D image into points where rapid changes in image intensity occur.
It employs diverse methods like gradient-based techniques such as the Sobel, Prewitt, and Robert operators, which leverage the first derivative, and the Laplacian approach, utilizing the second derivative, to pinpoint edges. The Canny Edge Detector, despite its trade-offs, stands out for its capacity to accurately detect both strong and weak edges after employing a Gaussian function to smooth the image and establishing thresholds for identifying dramatic and subtle grayscale changes. Corner Detection adds precision by locating corners without prior knowledge of brightness or edge angles. This process typically begins with a grayscale image and plays a pivotal role in object extraction, essential for minimizing data complexity by simplifying the image data for more efficient processing. Adjusting thresholds can significantly impact the clarity and coherence of edge points, with the overarching goal of reducing data for streamlined processing. Mainly this code funtions based on sobel operator