Human Detection Implementation Using HOG and Adaboost Algorithms
Successfully tested human detection with HOG and Adaboost algorithms - achieved promising results! Highly recommended for computer vision applications.
MATLAB Implementation of Pedestrian Detection Combining HOG and LBP Features
A MATLAB-based pedestrian detection system integrating HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns) algorithms for improved feature extraction and classification accuracy.
HoG SVM Method for Face Recognition
Explore the HoG SVM face recognition approach with code implementation insights - valuable for researchers studying facial recognition algorithms and their practical applications.
Feature Sets Computed at Different Scales and Positions
This is a complete implementation version including calling files. The entire video can be represented using feature sets computed at different scales and positions. The Hog3D descriptor, proposed by Alexander Klaser, Marcin Marszałek, Cordelia Schmid, and colleagues, extends the HOG concept from static image feature extraction to video sequence feature extraction, achieving excellent results in pedestrian detection within video sequences. The implementation typically involves 3D gradient computation and spatiotemporal block normalization.
Pedestrian Detection using HOG Features and AdaBoost Algorithm
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.
HOG + LBP + HIKSVM Pedestrian Detection Algorithm
MATLAB implementation of pedestrian detection algorithm using HOG, LBP, and HIKSVM, including the libsvm-mat-3.0-1 package with comprehensive feature extraction and classification code.
Classical Pedestrian Detection Algorithm
Implementation of a classical pedestrian detection algorithm using HOG features and SVM classification, fully debugged and ready for deployment.
Implementation of Dalal's HOG Feature Algorithm
Developed based on Dalal's HOG feature algorithm, this code features simple implementation with clear comments. By modifying the image path parameter, it generates a 36×105 feature vector suitable for image processing applications.
MATLAB HOG Feature Extraction and SVM Machine Learning Practice
Implementation guide for HOG feature extraction using MATLAB's Image Processing Toolbox and SVM classification with Machine Learning Toolbox, including parameter tuning and performance evaluation.
MATLAB Feature Extraction: HOG and SIFT Implementation for Computer Vision
Application Background: This MATLAB-based learning resource focuses on HOG (Histogram of Oriented Gradients) and SIFT (Scale-Invariant Feature Transform) feature extraction methods. The SIFT implementation includes a ready-to-use match function for immediate deployment. Key Technologies: MATLAB programming environment with HOG and SIFT algorithms for image recognition and machine vision applications, featuring practical code implementation examples and algorithm explanations.