FCM Algorithm Implementation for Image Segmentation
Implementation of image segmentation using FCM algorithm (supporting grayscale, indexed, and RGB images) with code structure and parameter configuration details
Applying K-means Algorithm for Image Segmentation
Implementation of K-means clustering for image segmentation using grayscale conversion from color input images, with post-processing median filtering to reduce noise caused by illumination variations and improve segmentation accuracy
Techniques for Processing and Analyzing Grayscale Images Using Morphological Methods
Using rice.tif as a sample image, this guide demonstrates key techniques for processing and analyzing grayscale images through morphological operations. The workflow involves: removing uneven background illumination from the grayscale image, converting the processed image to binary using thresholding techniques, performing component labeling to identify object properties, and calculating statistical features of detected objects. Code implementations include background correction algorithms, Otsu's thresholding method, and connected component analysis for feature extraction.
Conversion Program from Grayscale Images to RGB Pseudo-color Images
A comprehensive implementation for transforming grayscale images to RGB pseudo-color images with preprocessing, color mapping algorithms, and post-processing techniques
Total Variation Denoising
Total Variation Denoising Function: J = tv(I, iter, dt, ep, lam, I0) Input Parameters: I - Grayscale image, iter - Number of iterations [Default: 1], dt - Time step size [Default: 0.2], ep - Epsilon enhancement parameter [Default: 1], lam - Fidelity term lambda [Default: 0], I0 - Input noisy image [Default: I0=I] (Values in brackets indicate default parameters)
Converting Input Grayscale Images to RGB Pseudocolor Images for Output
Programmatically convert input grayscale images to RGB pseudocolor images for output using MATLAB implementation with color mapping techniques.
SIFT (Scale-Invariant Feature Transform) Feature Point Extraction from Grayscale Images Using MATLAB
Implementing SIFT (Scale-Invariant Feature Transform) feature point extraction from grayscale images using MATLAB with detailed algorithm explanations and code implementation approaches
Seamless Fusion of Two Color Images with MATLAB Implementation
Implementing seamless fusion of two color images using MATLAB, with the output result being a grayscale image. Key steps include image loading, alignment, feature extraction, fusion algorithms, and grayscale conversion.
MATLAB Code Implementation for Image Blind Deconvolution
This MATLAB code implementation for image blind deconvolution effectively performs blind deconvolution on grayscale images, utilizing advanced algorithms to restore image clarity without prior knowledge of the blur kernel.
Wavelet Multi-Scale Grayscale Image Texture Feature Detection Source Code
Source code for grayscale image texture feature detection based on wavelet multi-scale analysis, featuring customizable wavelet parameters for algorithm optimization