Multi-clasificación discriminativa de partes corporales basada en códigos correctores de errores

Abstract

This Project aims at the application of different techniques from the field of Artificial Vision for the detection and segmentation of human limbs on a newly created database. The database contains a large number of images where multiple subjects appear performing various poses. The objective is to detect the limbs of such subjects, including the arms, legs, body or head, among others, to subsequently obtain a multi-limb segmentation map. In order to perform this detection we trained different classifiers cascades on Haar and HOG features on targeted regions where limbs appeared. Once trained, several experiments have been released over the database for detecting the limbs mentioned. Some methods have been used to verify the detections. Finally, segmentation techniques have been applied for two purposes: On one hand, segment the subject from the background of the image, and on the other hand, each limb of the subject. In this case we have chosen segmentation using Graph-cuts formulation.