Explore MATLAB source code curated for "灰度直方图" with clean implementations, documentation, and examples.
This application enables clear visualization of grayscale histograms for images while providing smoothing and filtering operations, facilitating programmers' image analysis through histogram equalization and spatial filtering implementations.
In digital image processing, the grayscale histogram stands out as the simplest and most valuable tool. It plays a fundamental role throughout the entire workflow—from analyzing and observing images to developing effective processing methods. In programming terms, histograms can be efficiently computed using functions like `cv2.calcHist()` in OpenCV or `imhist()` in MATLAB, which count pixel frequencies across intensity levels to reveal critical image characteristics.
MATLAB GUI design featuring image cropping and display, grayscale histogram extraction from color images, along with grayscale histogram manipulation techniques including histogram equalization and histogram matching.
Bimodal histogram thresholding segmentation: A traditional segmentation technique ideal for beginners to learn, covering fundamental concepts with practical implementation approaches.
This MATLAB-based image processing implementation avoids using built-in functions and instead creates custom algorithms for: 1) Grayscale histogram visualization and calculation through pixel intensity counting, 2) Geometric transformations including horizontal/vertical mirroring and shearing using coordinate mapping, 3) Smoothing and sharpening filters via convolution operations with custom kernels, 4) Threshold segmentation using intensity-based pixel classification. Execute the main function and follow interactive prompts to demonstrate each feature.
MATLAB digital image processing techniques including grayscale adjustment, gamma transformation, grayscale histograms, and histogram equalization with implementation details
Implementing image processing through grayscale histogram analysis