Five Mini-Programs for LBP Implementation

LBP (Local Binary Pattern) is an effective method for image texture analysis and feature extraction. The following five mini-programs demonstrate various LBP implementations and applications, providing valuable references for learners and developers in this field.

Basic LBP Feature Extraction This program implements the core LBP algorithm by comparing each pixel with its neighboring pixels to generate binary patterns, which are then converted to decimal values. The implementation typically involves using a 3x3 neighborhood window and applying bitwise operations to create the LBP code for each pixel center, effectively capturing local texture features through binary pattern encoding.

Circular LBP Improvement While traditional LBP uses square neighborhoods, Circular LBP allows sampling pixels on circles of arbitrary radii, providing more flexibility for multi-scale texture analysis. The program demonstrates how to use bilinear interpolation to sample points at different radii and angles, showing parameter adjustments for optimal feature extraction across varying texture scales.

Rotation-Invariant LBP Since standard LBP features are sensitive to image rotation, this program introduces rotation invariance by applying circular bit shifts to find the minimum binary pattern value. The algorithm involves generating all possible rotations of the LBP pattern and selecting the smallest decimal equivalent, ensuring consistent feature representation regardless of image orientation.

LBP in Face Recognition Applications LBP has shown excellent performance in facial recognition systems. This program demonstrates how to extract LBP histogram features from facial images and integrate them with classifiers like Support Vector Machines (SVM). The implementation typically involves dividing the face image into regions, computing LBP histograms for each region, and concatenating them to form a robust feature vector for identity recognition.

Multi-Feature Fusion: LBP Combined with HOG To enhance robustness in image analysis, this program combines LBP with HOG (Histogram of Oriented Gradients) to create a multi-feature fusion strategy. The implementation involves extracting both LBP texture features and HOG shape features, then using feature concatenation or early/late fusion techniques to improve performance in complex scenarios like object detection and classification.

These mini-programs cover fundamental LBP theory and practical applications, providing researchers with starting points for experimentation and optimization in texture analysis and computer vision tasks.