Edge Detection (Canny) Edge detection is the process of identifying the boundaries and edges of objects within an image. One of the most widely used edge de...
Edge Detection (Canny)
Edge detection is the process of identifying the boundaries and edges of objects within an image. One of the most widely used edge detection methods is the Canny edge detection algorithm.
The Canny algorithm works by applying a filter to the image that emphasizes edges and suppresses other image features. The algorithm consists of two main steps:
The image is converted to a binary image, where pixels are either black or white.
The image is filtered to highlight edges.
Corner Detection (Harris)
Corner detection is another important feature extraction technique that identifies the corners of objects in an image. Corners are typically more robust and distinctive than edges, and they are often used for object recognition and tracking.
The Harris corner detection algorithm works by computing a measure of the image gradient at each pixel. The measure is a function of the difference between the image gradient in two adjacent pixels. Corners are typically identified as pixels where the gradient difference is significant.
Comparison
The Canny and Harris edge detection algorithms are both effective for identifying edges and corners in an image. However, they have different strengths and weaknesses. The Canny algorithm is more sensitive to noise in the image, while the Harris algorithm is more robust to noise and can be applied to images with different color distributions