Explore MATLAB source code curated for "识别" with clean implementations, documentation, and examples.
Detection and recognition of red traffic signs in complex environments using color-based circular sign localization, feature extraction, and template matching algorithms. Includes image demonstrations and program explanations with code implementation details for HSV color segmentation, Hough circle detection, and feature matching techniques to assist beginners.
Implementation of text detection, localization, segmentation, and recognition in images using MATLAB, featuring practical code examples ideal for beginners to quickly master computer vision techniques.
A MATLAB-based face detection and recognition system implemented using the Eigenface method, featuring principal component analysis for dimensionality reduction and pattern matching algorithms.
Signal processing implementation including modulation type recognition and demodulation debugging procedures, with key algorithmic and functional enhancements.
Implementing Hough transform to detect and annotate parallel road boundary lines with code-based parameter optimization techniques
Sparse representation methodology facilitates standard recognition of M facial samples across N distinct classes
A comprehensive MATLAB-based object detection and recognition program designed for beginners learning MATLAB programming, featuring complete functionality across various applications
A user-friendly speaker recognition system capable of real-time speaker identification from live audio recordings, featuring advanced signal processing algorithms and machine learning models for accurate voice pattern analysis.
Real-time image processing using camera input for traffic sign detection and recognition, incorporating edge detection, color recognition algorithms, and machine learning/deep learning model integration
This MATLAB program implements a comprehensive facial recognition pipeline that first processes face images using Gabor wavelet transformation for feature extraction, then applies PCA (Principal Component Analysis) for dimensionality reduction, and finally employs an SVM (Support Vector Machine) classifier for multi-class recognition. The implementation supports the complete ORL face database and utilizes the libsvm toolbox (version: libsvm-mat-2.89-3[FarutoUltimate3.0]). Key implementation details include Gabor filter parameter configuration, PCA eigenvector computation, and SVM kernel function selection for optimal multi-class classification performance.