Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/103966
Title: Human body parts segmentation via stacked and multi-task learning
Author: Sánchez Abril, Daniel
Director: Escalera Guerrero, Sergio
Baró Solé, Xavier  
Keywords: segmentation
body parts
stacked learning
multi-task
Issue Date: 9-Oct-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The segmentation of people in RGB images poses a key obstacle in the field of computer vision. In our thesis, we tackle this issue through hand-crafted features in a two-stage pipeline, targeting both binary and multiple body part segmentation. For our purposes, we employ tools such as AdaBoost, support vector machines, Haar-like features, histograms of oriented gradients and graphics. We also address the differences between cascade learning and stacked learning. Finally, we analyse a multimodal approach to combining different tasks, which allows us to improve and refine our predictions concerning the segmentation of body parts using 2D and 3D estimations of posture and depth, a feat made possible thanks to deep learning.
Language: English
URI: http://hdl.handle.net/10609/103966
Appears in Collections:Doctoral Thesis

Share:
Export:
Files in This Item:
File Description SizeFormat 
phd_thesis_dani-1.pdfSánchez_Abril_dissertation13.22 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons