Explore MATLAB source code curated for "灰度图像" with clean implementations, documentation, and examples.
Image binarization processing involves setting pixel grayscale values to either 0 or 255, transforming the image into a distinct black-and-white representation. By selecting an appropriate threshold value, a binarized image that preserves both global and local characteristics of the original image can be obtained. This process typically utilizes thresholding algorithms like Otsu's method or adaptive thresholding in implementation.
This database comprises 213 grayscale images representing 7 distinct positive facial expressions from 10 subjects. All images are stored as 256×256 pixel 8-bit grayscale TIFF files, with an average of 2-4 samples per expression per individual. The dataset structure facilitates implementation of facial expression recognition algorithms through standardized image preprocessing and classification techniques.
This MATLAB program converts input RGB images to grayscale format, implementing fundamental image processing techniques through pixel value transformations and weighted color channel combination algorithms.
MATLAB implementation for converting grayscale images to RGB format with detailed code explanations and color mapping techniques
Wavelet threshold denoising technique with MATLAB implementation, providing filtering for grayscale and binary images using wavelet decomposition and thresholding algorithms.
MATLAB source code for calculating the centroid position of a spot in a grayscale image. The algorithm uses the Otsu's method (maximum between-class variance) to accurately determine the (x, y) coordinates of the spot centroid. This implementation includes automatic threshold calculation and weighted centroid computation based on pixel intensities.
Denoising grayscale and binary images using a morphological filter, which employs fundamental morphological operations to enhance image quality.
Methods for boundary tracking in grayscale images, where gradient maps are first computed using differential operators, followed by algorithm application for boundary detection. Implementation typically involves edge detection operators like Sobel or Canny for gradient calculation and contour tracing algorithms.
Fundamental image processing operations: reading images, edge detection in grayscale images, boundary pixel extraction in binary images, blurring operations, frame processing, and image type conversions with code implementation examples.
This source code implements multi-scale Retinex theory with support for both grayscale and color image processing, featuring flexible scale parameter configurations and efficient convolution operations.