MATLAB-Based Image Processing Software

This article introduces an integrated image processing software developed using MATLAB, which incorporates the following fundamental functionalities: - Opening, displaying, and saving images in various formats (including grayscale and RGB). Implemented using MATLAB's imread(), imshow(), and imwrite() functions with customizable parameters for format handling. - Histogram analysis and curve fitting processing, along with brightness distribution curve calculations (blur processing). Uses histcounts() for histogram data extraction and fit() function for polynomial curve fitting to analyze pixel intensity distributions. - Histogram equalization through histeq() function, and filtering techniques including Gaussian (imgaussfilt()) and median (medfilt2()) filters with configurable kernel sizes. - Edge detection implementation featuring algorithms like Sobel (edge() with 'sobel' option) and Laplacian (fspecial('laplacian')) operators for gradient-based boundary extraction. Additionally, the software provides advanced capabilities such as: - Image segmentation using watershed or k-means clustering algorithms (via imsegkmeans()) to partition images into distinct regions. - Feature extraction employing techniques like HOG (extractHOGFeatures()) or SURF (detectSURFFeatures()) to identify and describe key image characteristics. - Object detection utilizing template matching (normxcorr2()) or machine learning-based classifiers (trainCascadeObjectDetector()) for automated target identification in images. In summary, this software offers broad applicability in medical image processing, computer vision, and robotics domains, delivering efficient and accurate image processing solutions through optimized MATLAB code implementation. The modular architecture allows easy integration of custom algorithms while maintaining compatibility with standard image processing workflows.