Edge Detection and Image Segmentation Using MATLAB

Image Edge Detection and Segmentation Techniques in MATLAB

In the field of image processing, edge detection and segmentation are fundamental and critical operations. MATLAB offers multiple implementation approaches suitable for various application scenarios.

Edge Detection Methods Operator-Based Detection Prewitt operator detects edges by calculating horizontal and vertical gradients of the image, ideal for rapid processing with moderate accuracy requirements. Implementation typically involves using the `edge()` function with the 'Prewitt' option. LoG (Laplacian of Gaussian) operator controls smoothing intensity through σ parameter adjustment - larger σ values result in blurrier edges but better noise resistance. The Canny operator is a classic multi-stage algorithm involving Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold processing, producing more continuous edges. In MATLAB, this can be implemented using `edge(I, 'Canny')` with customizable threshold parameters.

Morphological Processing Morphological gradient enhances edges through dilation and erosion operations using structural elements, particularly effective for binary images. For PCB image processing, morphological opening and closing operations can remove fine current lines while preserving main chip components. This can be achieved using `imdilate()`, `imerode()`, and combination functions like `imopen()`.

Image Segmentation Techniques Threshold Segmentation Global threshold methods (such as Otsu's algorithm) automatically select thresholds to separate foreground from background using `graythresh()` and `im2bw()`. Watershed thresholding simulates flood immersion processes, suitable for images with complex gray-level variations, implemented via `watershed()` function.

Region Decomposition Quadtree decomposition recursively divides image matrices into homogeneous blocks using `qtdecomp()`, useful for compression or simplified analysis. For text/non-text classification, combining texture features or machine learning methods can improve accuracy through feature extraction functions and classification algorithms.

In practical applications, algorithm selection should consider image characteristics (noise, contrast, etc.). For example: low-noise images suit Canny operator, while morphological processing works better for structured binary images. MATLAB's Image Processing Toolbox provides comprehensive functions like `edge()`, `imbinarize()`, and regional analysis tools for optimized implementation.