Explore MATLAB source code curated for "HOG特征" with clean implementations, documentation, and examples.
This program extracts HOG and LBP features from positive and negative sample images, trains a classifier using Support Vector Machines, and implements pedestrian detection. Implementation details include feature vector extraction algorithms, SVM training methodology, and sliding window detection techniques. Experimental results demonstrate effective pedestrian detection with robust performance.
This program provides MATLAB source code for separating pedestrians from background using pre-extracted HOG features. The implementation includes both blocked and non-blocked feature extraction approaches, where blocking involves dividing images into smaller segments for more detailed feature representation. Classification is performed using minimum Euclidean distance method for enhanced pedestrian detection accuracy.
Image similarity calculation utilizing HOG features, invariant moments, and Canny edge factor computation to measure similarity between two images. This method can be effectively applied to image retrieval systems, enabling efficient and accurate image search capabilities through feature extraction and comparison algorithms.
Implementation of HOG Features based on INRIA CVPR 2005 Paper with Code-Level Insights
Comprehensive code implementation for HOG features with gradient computation, suitable for computer vision applications
Comprehensive guide on implementing HOG feature extraction and SVM machine learning in MATLAB for computer vision applications
A detailed implementation walkthrough of the Histogram of Oriented Gradients (HOG) descriptor for computer vision applications