scholarly journals Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries

2014 ◽  
Author(s):  
Syed Raza ◽  
Omar Javed ◽  
Aveek Das ◽  
Harpreet Sawhney ◽  
Hui Cheng ◽  
...  
Keyword(s):  
Depth Extraction ◽  
Geometric Context ◽  
Occlusion Boundaries

scholarly journals Finding Temporally Consistent Occlusion Boundaries in Videos Using Geometric Context

2015 ◽  
Author(s):  
S. Hussain Raza ◽  
Ahmad Humayun ◽  
Irfan Essa ◽  
Matthias Grundmann ◽  
David Anderson
Keyword(s):  
Geometric Context ◽  
Occlusion Boundaries

scholarly journals One Shot 360-Degree Light Field Capture and Reconstruction with Depth Extraction Based on Optical Flow for Light Field Camera

2018 ◽  
Vol 8 (6) ◽  
pp. 890 ◽  
Author(s):  
Youngmo Jeong ◽  
Seokil Moon ◽  
Jinsoo Jeong ◽  
Gang Li ◽  
Jaebum Cho ◽  
...  
Keyword(s):  
Optical Flow ◽  
Light Field ◽  
Depth Extraction

Iteratively weighted center of gravity algorithm used for depth extraction of streak image

2018 ◽  
Vol 47 (2) ◽  
pp. 230004
Author(s):  
叶光超 Ye Guangchao ◽  
李旭东 Li Xudong ◽  
董志伟 Dong Zhiwei ◽  
樊荣伟 Fan Rongwei ◽  
陈德应 Chen Deying
Keyword(s):  
Center Of Gravity ◽  
Streak Image ◽  
Depth Extraction

scholarly journals A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

2019 ◽  
Vol 14 (06) ◽  
pp. 27 ◽  
Author(s):  
Kamal El Guemmat ◽  
Sara Ouahabi
Keyword(s):  
Search Engines ◽  
Learning Objects ◽  
Learning Object ◽  
Automatic Indexing ◽  
New Approach ◽  
Learning Object Metadata ◽  
Depth Extraction ◽  
Semantic Distances ◽  
Linguistic Structures

Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.

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