Explore MATLAB source code curated for "梯度" with clean implementations, documentation, and examples.
This implementation focuses on external force selection in active contours-based image segmentation. While conventional algorithms typically rely on gradient-based approaches, this method provides an effective alternative for images with ambiguous gradient information. The code demonstrates how to handle segmentation tasks when traditional gradient methods underperform.
RBF learning methodologies encompassing: k-means clustering, gradient descent optimization, and OLS (Ordinary Least Squares) algorithms with practical implementation approaches.
Process digital images in MATLAB with gradient operations to compute and visualize gradient maps for edge detection and feature analysis.
The Least Mean Square (LMS) adaptive algorithm is an iterative optimization method that minimizes the mean square error between the desired response and the filtered output signal. It estimates the gradient vector during iteration based on input signals and updates weight coefficients to achieve optimal adaptive filtering. As a stochastic gradient descent approach, LMS is notable for its computational simplicity—requiring no correlation function calculations or matrix operations. Typical implementations involve weight updates using a step-size parameter and instantaneous error feedback.
Grayscale image histogram equalization; Gradient enhancement; Histogram specification; Grayscale image histogram data; Direct grayscale transformation: Contrast enhancement techniques
Quantitative assessment of image fusion outcomes using statistical and information-theoretic metrics including mean, standard deviation, entropy, gradient, correlation coefficient, and spectral distortion
An image registration algorithm combining mutual information and gradient information, which incorporates spatial image features for enhanced registration performance
Image Fusion Evaluation Program utilizes multiple metrics including mean, standard deviation, entropy, gradient, correlation coefficient, and spectral distortion to comprehensively assess and compare fused image quality. Implementation involves MATLAB/Python functions for quantitative analysis of fusion performance.
Three watershed segmentation techniques for images: standard watershed segmentation, two-step gradient-based watershed segmentation, and three-step watershed algorithm combining gradient and mask operations
Traditional edge detection algorithms often struggle to establish appropriate thresholds for detecting faint edges with low gradient values in certain images. This implementation addresses this issue through two enhanced approaches: image variance normalization to amplify gradient values of blurred edges, and sigmoid function integration to incorporate regional pixel information into gradient calculations for threshold optimization. The program compares these advanced techniques with conventional edge detection methods, providing comprehensive performance evaluation through practical algorithm implementations using gradient computation and adaptive thresholding.