Multiple Approaches for Image Edge Detection with MATLAB Implementation

This article presents multiple methodologies for image edge detection with corresponding implementation approaches. The techniques covered include:

1. Edge detection using Prewitt operator - implementing horizontal and vertical gradient masks through convolution operations to highlight intensity changes.

2. Laplacian of Gaussian (LoG) operator application with varying sigma values - utilizing different Gaussian kernel sizes to control smoothing before edge detection.

3. Canny edge detector implementation - employing multi-stage processing including Gaussian filtering, gradient calculation, non-maximum suppression, and double thresholding for optimal edge detection.

4. Image segmentation via thresholding methods - implementing global and adaptive thresholding algorithms to separate foreground from background.

5. Watershed thresholding segmentation - applying marker-controlled watershed transformation to handle overlapping objects and complex boundaries.

6. Matrix quadtree decomposition - implementing recursive region splitting based on homogeneity criteria for efficient image representation.

7. Text and non-text image classification - developing feature-based categorization using texture analysis and morphological characteristics.

8. Morphological gradient detection for binary images - combining dilation and erosion operations to highlight boundary regions in binary images.

9. Morphological processing case study - employing opening, closing, and area filtering operations to remove PCB current lines while preserving core components through structural element design.

These methodologies provide comprehensive tools for analyzing edge information in digital images. By implementing different approaches, researchers can achieve more accurate and清晰 edge detection results suitable for various computer vision applications.