Human Edge Segmentation From 2D Images By Histogram of Oriented Gradients and Edge Matching Algorithm
Phakjira Sombatpiboonporn, Theekapun Charoenpong, Ajaree Supasuteekul, Chamaporn Chianrabutra, Kanjana Pattanaworapan · 2019
Current research performance is limited to segment precise human edge. In this paper, we proposed an edge matching algorithm for human edge segmentation from 2D images by means of the histogram of oriented gradients technique and SVM classification. The algorithm having four steps, namely image sequence acquisition, human detection, edge segmentation and human edge segmentation, were carried out in this work. Data was collected from 710 full body human image. Based on the finding of this work, error results of the human edge in the parts of head, neck, body, and leg were 9.86, 13.60, 6.63, and 637 pixels, respectively. The advantages of the proposed method is this method can segment human from image by using only one image and a small group of databases.