Explore MATLAB source code curated for "图片" with clean implementations, documentation, and examples.
Implementing MATLAB neural network algorithms for personality recognition in images through neural network training, enabling gender identification (male/female) from facial features with implementation insights on network architecture and training processes.
Robot path planning and obstacle avoidance algorithms capable of processing arbitrary images as environmental maps
This program adds multiple types of noise to images, including Gaussian noise, salt-and-pepper noise, additive and multiplicative noise, as well as various mixed noise combinations, for testing other image processing applications.
MATLAB-based gesture recognition project including test code, sample images, and a ready-to-run .fig GUI file. The complete MATLAB source code is provided for learning reference, featuring simpler implementation compared to C++ alternatives. The code demonstrates core gesture recognition algorithms through image processing techniques like segmentation, feature extraction, and pattern classification using functions such as regionprops() and edge detection. Ideal for understanding fundamental principles before advancing to C++/OpenCV implementations.
Source code for circle detection using Hough transform, including sample images and detailed program documentation with implementation insights
Fingerprint enhancement implementation containing fingerprint images and corresponding MATLAB code, performing enhancement based on fingerprint orientation analysis using directional filtering techniques.
Implementing optimal Support Vector Machine algorithms for image segmentation enables effective cell separation imagery with improved precision and computational efficiency.
Original code for various funhouse mirror distortions, implementing image scaling (zoom in/out), horizontal concave, horizontal convex, trapezoidal deformation, triangular, S-shaped, and wave-like distortions with image loading capabilities.
This application enables users to manually click any point on an image and view its subpixel coordinates with precision up to 0.0001 pixels. The implementation involves mouse event handling and interpolation algorithms for coordinate calculation.
A MATLAB implementation of pedestrian detection using HOG (Histogram of Oriented Gradients) features combined with AdaBoost classifier, including extensive training and testing image datasets required for program execution.