Scene Understanding - A Survey

被引:0
作者
Aarthi, S. [1 ]
Chitrakala, S. [1 ]
机构
[1] Anna Univ, CEG, Dept Comp Sci & Engn, Madras, Tamil Nadu, India
来源
2017 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND SIGNAL PROCESSING (ICCCSP) | 2017年
关键词
Scene understanding; contextual scene; semantic scene; Image identification;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent times, scene understanding holds a great position in computer vision due to its real time perceiving, analyzing and elaborating an interpretation of dynamic scene which leads to new discoveries. A scene is a view of real world environment with multiple objects and surfaces in a meaningful way. Objects are compact and act upon whereas scene are extended in space and act within. The visual information can be given with many features such as Colors, Luminance and contours or in the form of Shapes, Parts and Textures or through semantic context. The goal of scene understanding is to make machines look like humans, to have a complete understanding of visual scenes. Scene understanding is influenced by cognitive vision with an involvement of major areas like computer vision, cognitive engineering and software engineering. Due to its enormous growth many outstanding universities like Boston University, Stafford Vision lab, Scene grammar lab, air lab, Laboratory Machine Vision and Pattern Recognition have been perseveringly working for added improvements in this area. This paper discusses an extensive survey of scene understanding with various strategies and methods.
引用
收藏
页码:191 / 194
页数:4
相关论文
共 50 条
  • [31] Towards Efficient Scene Understanding via Squeeze Reasoning
    Li, Xiangtai
    Li, Xia
    You, Ansheng
    Zhang, Li
    Cheng, Guangliang
    Yang, Kuiyuan
    Tong, Yunhai
    Lin, Zhouchen
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 7050 - 7063
  • [32] Moving vehicle tracking and scene understanding: A hybrid approach
    Xiaoxu Liu
    Wei Qi Yan
    Nikola Kasabov
    Multimedia Tools and Applications, 2024, 83 : 51541 - 51558
  • [33] Improving Semantic Scene Understanding Using Prior Information
    Laddha, Ankit
    Hebert, Martial
    UNMANNED SYSTEMS TECHNOLOGY XVIII, 2016, 9837
  • [34] Review on Panoramic Imaging and Its Applications in Scene Understanding
    Gao, Shaohua
    Yang, Kailun
    Shi, Hao
    Wang, Kaiwei
    Bai, Jian
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [35] Dynamic scene understanding by improved sparse topical coding
    Fu, Wei
    Wang, Jinqiao
    Lu, Hanqing
    Ma, Songde
    PATTERN RECOGNITION, 2013, 46 (07) : 1841 - 1850
  • [36] Towards Scene Understanding for Autonomous Operations on Airport Aprons
    Steininger, Daniel
    Kriegler, Andreas
    Pointner, Wolfgang
    Widhalm, Verena
    Simon, Julia
    Zendel, Oliver
    COMPUTER VISION - ACCV 2022 WORKSHOPS, 2023, 13848 : 153 - 169
  • [37] 3D Point Cloud Scene Data Acquisition and Its Key Technologies for Scene Understanding
    Li Yong
    Tong Guofeng
    Yang Jingchao
    Zhang Liqiang
    Peng Hao
    Gao Huashuai
    LASER & OPTOELECTRONICS PROGRESS, 2019, 56 (04)
  • [38] 3D Semantic Scene Completion: A Survey
    Luis Roldão
    Raoul de Charette
    Anne Verroust-Blondet
    International Journal of Computer Vision, 2022, 130 : 1978 - 2005
  • [39] Text extraction from natural scene image: A survey
    Zhang, Honggang
    Zhao, Kaili
    Song, Yi-Zhe
    Guo, Jun
    NEUROCOMPUTING, 2013, 122 : 310 - 323
  • [40] 3D Semantic Scene Completion: A Survey
    Roldao, Luis
    de Charette, Raoul
    Verroust-Blondet, Anne
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2022, 130 (08) : 1978 - 2005