MATLAB implementation of a basic HOG + SVM pedestrian detector.

Recognize traffic sign using Histogram of Oriented Gradients (HOG) and Colorspace based features. Support Vector Machines (SVM) is used for classifying images.

This research uses computer vision and machine learning for implementing a fixed-wing-uav detection technique for vision based net landing on moving ships. A rudimentary technique using SIFT descriptors, Bag-of-words and SVM classification was developed during the study.

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

Computer Vision - Object Detection

Multiple Moving objects in a surveillance video were detected and tracked using ML models such as AdaBoosting. The obtained results were compared with the results from Kalman Filter.

Matlab code in order to do FaceRecognition with PCA Eigenfaces and HogFeatures

Lab exercises of computer vision course in NTUA

基于swt和mser算法的文本检测，用到了SVM分类器

Points matching in two images by using HOG Algorithm

Tools to process bidimensional signals or images.

Feed-Forward Neural Network-Based Face Classifier Model with Histogram of Oriented Gradients Feature Extraction

Persian Digit Classification with Multi Layer Perceptron(MLP)- using Block Mean as feature...

Medical image analysis, to find a nerve tissue and get its location in an image.

Implementing a code book method for face feature extraction using HoG attribtues and in real-time face swapping source with a target face

HOGfeature extraction for Pedestrian detection

Clasifying images using Histogram Oriented Gradients algorithm HOG

shapes like circle, star, square and rectangle are classified using support vector machine