Explore MATLAB source code curated for "二值化" with clean implementations, documentation, and examples.
Comprehensive image processing workflow for glass bottle analysis including color-to-binary conversion, filtering, intensity enhancement, threshold segmentation, edge detection, and Hough circle detection with implementation approaches
This program performs interference fringe processing through filtering, binarization, and skeletonization operations to ultimately generate a single-pixel-width image for accurate fringe spacing measurement.
Development of an image-based particle analysis system implementing preprocessing, filtering, binarization, segmentation, and final statistical analysis of particle size distribution and quantity counting
A fingerprint recognition program implemented in MATLAB that performs Gabor filtering, binarization, erosion and other preprocessing operations on fingerprint images, utilizing SVM algorithms to determine whether new fingerprint images exist in the original database. Suitable for demonstrating small-scale fingerprint identification systems with detailed implementation approaches for image processing and machine learning components.
Binarize cell images using the bimodal thresholding method, followed by segmentation and cancer cell identification
Implementing Otsu's thresholding algorithm twice to separate foreground from background and isolate white regions from the background, followed by binarization processing.
Implementation of MATLAB-based adaptive thresholding algorithm for grayscale image binarization with code optimization techniques
A comprehensive MATLAB-based license plate recognition system featuring complete source code implementation, including plate localization, image binarization, noise filtering and removal, character segmentation, and BP neural network-based character recognition algorithms.
This implementation processes 24-bit RGB color images by converting them to grayscale, applying Otsu's thresholding method for binarization, and displaying the calculated threshold value. The algorithm automatically determines the optimal threshold that maximizes inter-class variance.
Image binarization is a fundamental image segmentation technique where pixel intensities above a critical threshold are set to maximum grayscale value, while those below are set to minimum value. This process converts grayscale images into binary images. Based on threshold selection methods, binarization algorithms are categorized into fixed threshold and adaptive threshold approaches. Commonly used techniques include bimodal method, P-parameter method, iterative method, and OTSU method, each with specific MATLAB implementation considerations.