Explore MATLAB source code curated for "结果" with clean implementations, documentation, and examples.
The file canny.m in the "canny edge detection" folder contains the implementation code for Canny edge detection. compare.m and connect.m are auxiliary functions used within this code. clique.jpg is the original image, while step1.jpg, step2.jpg, step3.jpg, and threshold 80 and 100.jpg represent results from four processing stages. all.jpg displays the final output of the Canny edge detection process.
A comprehensive guide to implementing OFDM in MATLAB with fully-functional code that demonstrates both algorithm understanding and visual results through signal constellation diagrams and spectrum analysis.
After execution, the color image road recognition program extracts a specific region from the image, calculates the mean and variance of color values, and produces analytical results for road identification.
Implementation of Otsu's method for image segmentation has been executed and validated, producing accurate segmentation results
Complete Backpropagation Neural Network implementation featuring provided dataset and results, including detailed code structure and algorithmic explanations for practical application
Divide an image into blocks, compute the mean and variance for each block, and provide visualized results for easy observation and analysis. Includes code implementation details for image segmentation and statistical computation.
This is a test program implementing a Deep Belief Network (DBN) algorithm using stacked Restricted Boltzmann Machines (RBMs). The current implementation employs contrastive divergence for training individual RBMs and includes both pre-training and fine-tuning phases. However, the performance results have been suboptimal, possibly due to parameter tuning challenges or architectural limitations in the neural network layers.
A robust MATLAB implementation for face recognition using sparse coding, derived from Yang's seminal paper, ideal for beginners to systematically learn sparse coding concepts. The algorithm achieves strong performance on both occluded and non-occluded face recognition tasks, featuring sparse representation classification with error tolerance mechanisms.
This MATLAB implementation demonstrates spectral clustering with integrated k-means functionality, showing how spectral clustering typically achieves superior clustering results compared to traditional k-means algorithms through eigenvalue decomposition and graph partitioning techniques.
Implementation of PMSM simulation using SVPWM technique to observe various waveforms and simulation results, featuring MATLAB/Simulink model structure and control algorithm implementation