Explore MATLAB source code curated for "灰度图像" with clean implementations, documentation, and examples.
This documentation demonstrates grayscale image processing and three-dimensional visualization techniques using MATLAB, including practical code implementation approaches for image analysis and 3D surface plotting.
MATLAB source code implementation of one-dimensional maximum entropy method for grayscale image threshold segmentation, featuring complete algorithm implementation with entropy calculation and optimal threshold selection.
This program implements Bilateral filtering for edge-preserving smoothing, suitable for both grayscale and color images. The implementation handles spatial and intensity domain weighting with configurable parameters.
Construct a Gaussian pyramid for grayscale images with adjustable kernel parameters to enable multi-scale image analysis.
This MATLAB-based simulation for object tracking has been personally validated and verified as functional. The implementation enables target marking with rectangular bounding boxes, converts grayscale images to binary images, and achieves robust object tracking through practical algorithm application.
Implementation of Arnold chaotic scrambling encryption for grayscale images with enhanced security through disorder and diffusion operations
Implementation of MATLAB-based adaptive thresholding algorithm for grayscale image binarization with code optimization techniques
MATLAB source code for implementing two-dimensional Otsu automatic threshold segmentation on grayscale images, featuring efficient execution time calculation and comprehensive 2D histogram analysis capabilities
The input image must be grayscale. Usage: [accumulator_values, circle_centers, circle_radii] = CircularHough_Grd(gray_image, [min_diameter max_diameter]); This method can detect multiple circles within the same image, implementing gradient-based Hough transform for robust circle detection.
Pseudo-color processing refers to the conversion of grayscale (black-and-white) images into color images or the transformation of monochromatic images into images with specified color distributions. Since the human eye can distinguish colors much more effectively than shades of gray, converting grayscale images to color representations improves the ability to detect image details. The fundamental principle involves mapping each grayscale level to a specific point in the color space, enabling the transformation of monochrome images into color images by assigning distinct colors to different gray levels. In code implementation, this typically involves creating a color lookup table (LUT) where grayscale values are mapped to RGB triplets using linear or nonlinear transformation algorithms.