Real AdaBoost-Based Face Detection Implementation
A MATLAB face detection program utilizing Real AdaBoost algorithm, featuring comprehensive source code with detailed implementation of Haar feature extraction and weak classifier training processes.
MATLAB Source Code for License Plate Localization Based on Adaboost Algorithm - September 10 Modified Version
Modified MATLAB source code for license plate localization using Adaboost algorithm (updated September 10). This implementation includes integral image calculation, Haar-like feature generation, and enhanced feature selection mechanisms. Suitable for researchers studying machine learning applications in computer vision.
MATLAB Source Code for License Plate Localization Using Adaboost Algorithm
Updated October 10th Adaboost-based license plate localization MATLAB source code with improvements from the second upload. The implementation still requires substantial training time but includes integral image computation, Haar feature generation modules, and Adaboost classifier training. MATLAB experts are welcome to optimize the feature extraction and training pipeline for better performance - interested users can download and contribute enhancements.
Extracting Haar Features from Images in MATLAB Environment
Implementing Haar feature extraction for images within MATLAB, covering algorithm principles and practical code implementation approaches
Face Recognition Application Using Haar Feature-based Adaboost Cascade Classifier
A ready-to-run face recognition implementation utilizing Haar feature-based Adaboost cascade classifiers with complete code integration
Preprocessing and Feature Descriptors for Facial Micro-Expression Recognition
Facial micro-expressions reveal crucial insights into human emotions, even when individuals attempt to conceal their feelings. Historically, limited research has been conducted on detecting and recognizing micro-expressions using computer vision techniques. This implementation processes spontaneous micro-expression databases through preprocessing and Haar feature-based image cropping, followed by feature extraction using Local Binary Patterns on Three Orthogonal Planes (LBP-TOP) and Local Gray-Coding Patterns on Three Orthogonal Planes (LGCP-TOP) descriptors. The system employs Support Vector Machines (SVM) for detection and classification, achieving accuracy comparable to existing state-of-the-art methods.
Pedestrian Detection using Haar Features and SVM
A MATLAB-based pedestrian detection code collection utilizing Haar features and SVM, including comprehensive dataset for testing and training.
MATLAB Face Recognition Program with Haar Features and Adaboost Algorithm
MATLAB face recognition implementation using Haar-like features and Adaboost algorithm for robust detection and classification
Integral Image Computation with Haar Features and AdaBoost Algorithm Implementation
MATLAB code for integral image calculation, featuring Haar-like feature extraction and an AdaBoost classifier learner. This implementation provides an excellent educational resource for understanding computer vision algorithms with practical code examples.
MATLAB Implementation of Integral Image for Efficient Haar Feature Computation
MATLAB-based integral image implementation for accelerated Haar feature calculation, featuring optimized rectangular region sum computations with preprocessing techniques