Abstract
The primary goal of computer vision is to interpret the contents of an image, which can be achieved through image segmentation. This technique involves dividing an image into meaningful regions based on the intended application. By detecting and outlining the edges of objects, we can identify them within the image. Edges refer to the boundaries between objects and the background, as well as the boundaries between overlapping objects. Through image segmentation, we can separate the image from the background and extract valuable information. Edge detection is a crucial step in image segmentation, as it involves identifying and locating abrupt changes in the image. This article analyzes various edge detection techniques, such as Sobel, Prewitt, Roberts, Canny, and Laplacian Gaussian (LoG), using an esophageal image in Python.
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