Scene text detection and recognition based on Extremal Region(ER)

Factor graphs and loopy belief propagation implemented in Python

Local Binary Patterns implementation using Python3 and OpenCV

Python实现提取图像的纹理、颜色特征，包含快速灰度共现矩阵（GLCM）、LBP特征、颜色矩、颜色直方图。

LBP-based segmentation of defocus blur

Android application for gender, age and face recognition using OpenCV and JavaCV libraries

The goal of this project was to develop a Face Recognition application using a Local Binary Pattern approach and, using the same approach, develop a real time Face Recognition application.

👨 使用 OpenCV 和 Qt 实现人脸（刷脸）登录

Boosted multi-task learning for face verification

Image feature selection performed by using white box algorithms

Convolutional Neural Networks based Deep Learning Model

Local Binary Pattern algorithms.

LBP Face Recognition 📷

Face Identification In Artwork

A Multimodal Approach in Content Based Image Retrieval

Reformulation of LBP through Convolutional Filters in Python using the TensorFlow framework

this programme use a test picture and webcam to know if the person who use webcam is the same in the test using HOG and LBP

Canon CAPT Drivers for Solus and others

Multiprocessing LBP, GLCM and WLD for the Digital Image Processing classes @ UNESP Bauru.

Small library for Local Binary Pattern Matching

Optical flow for visual tracking,achieved by matlab

Мой вариант ЛБП от Alexgyver

Project implemented using MatLab and Computer Vision algorithms during Computer Vision subject

Facial Expression Prediction using Information Theoretic Learning

Multi-resolution local binary patterns for biological sequence data in Go

Where Is (the) Ball /Ball Recognition with OpenCV

Implement object detection and facial recognition techniques on input images and videos.

Métodos clássicos de aprendizagem de máquina são utilizados para realizar a classificação da emoção raiva e alegria em imagens da base de dados RafD. Viola-Jones e o LBP são utilizados para extrair as regiões dos olhos e da boca e obter seu vetor de características. A métrica utilizada para medir o desempenho dos classificadores é a acurácia.