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Statistical Multi-Objects Segmentation for Indoor/Outdoor Scene Detection and Classification via Depth Images

Title
Statistical Multi-Objects Segmentation for Indoor/Outdoor Scene Detection and Classification via Depth Images
Author
김기범
Keywords
Depth images; multi-object detection; statistical segmentation; scene detection
Issue Date
2020-03
Publisher
IEEE
Citation
2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), page. 271-276
Abstract
With the advancement of technology, intelligence capabilities of machines are growing day by day. Researchers are committed to equip the machines with the capability of thinking humanly. Currently, the machines can sense and process information gathered from sensors. However, still there is a huge gape to improve the capability of thinking and understanding real scenes. Scene understanding is fiery area of research now a day. Therefore, we have proposed a model to understand and recognize a scene using depth data to make machines capable of interpreting the real time scenes like humans. The proposed recognition technique is a novel segmentation framework that uses statistical multi object segmentation to learn robust scene model and segregate the objects in the scene. Then, the unique features are extracted from these segregated objects to further process for recognition using linear SVM. Finally, multilayer perceptron is provided with the features and weights for the recognition of the scene. Our system demonstrated significant improvement over state-of-the-art systems. The proposed system is effective in autonomous vision systems like robotic vision, GPS based location finder, sports and security.
URI
https://ieeexplore.ieee.org/document/9044576?arnumber=9044576&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/163049
ISBN
978-1-7281-4675-1; 978-1-7281-4676-8
ISSN
2151-1411
DOI
10.1109/IBCAST47879.2020.9044576
Appears in Collections:
ETC[S] > 연구정보
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