Explore MATLAB source code curated for "LBP特征" with clean implementations, documentation, and examples.
Implementation code for extracting Gabor features, Local Binary Pattern (LBP) features, and Local Gabor Binary Pattern (LGBP) features with algorithmic explanations
A face recognition program implementing LBP feature calculation - providing simple yet effective statistical histogram features with practical implementation guidelines for feature extraction and analysis.
LBP features, or Local Binary Patterns, provide a convenient method for texture analysis. After computation, these features are typically described using histograms of local regions, enabling efficient texture classification and pattern recognition in image processing applications.
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.
MATLAB program for extracting LBP features from grayscale images, featuring two LBP operator implementations with code-level algorithm descriptions
LBP Feature Representation and Classification Principles for Texture Patterns: This method involves defining a template that operates on each pixel in the original image. The template compares pixel values in the neighborhood region surrounding the center point with the center pixel value itself, assigning different weights to comparison results from different positions. Consequently, the transformed value for each pixel is represented by the weighted sum of comparison results from all template positions.