{"title":"Scene understanding — A survey","authors":"S. Aarthi, S. Chitrakala","doi":"10.1109/ICCCSP.2017.7944094","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":269595,"journal":{"name":"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCSP.2017.7944094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
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.