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http://hdl.handle.net/10609/103966
Title: | Human body parts segmentation via stacked and multi-task learning |
Author: | Sánchez Abril, Daniel |
Director: | Escalera, Sergio Baró, Xavier |
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. |
Keywords: | segmentation body parts stacked learning multi-task |
Document type: | info:eu-repo/semantics/doctoralThesis |
Issue Date: | 9-Oct-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
Appears in Collections: | Tesis doctorals |
Files in This Item:
File | Description | Size | Format | |
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phd_thesis_dani-1.pdf | Sánchez_Abril_dissertation | 13,22 MB | Adobe PDF | View/Open |
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