A New Scene Segmentation Method Based on Color Information for Mobile Robot in Indoor Environment

2016 ◽  
pp. 353-363
Author(s):  
Xu-dong Zhang ◽  
Qi-Jie Zhao ◽  
Qing-XU Meng ◽  
Da-Wei Tu ◽  
Jin-Gang Yi
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Scene Segmentation ◽  
Segmentation Method ◽  
Color Information

A Novel Role-Based Movie Scene Segmentation Method

2009 ◽  
pp. 917-922 ◽  
Author(s):  
Chao Liang ◽  
Yifan Zhang ◽  
Jian Cheng ◽  
Changsheng Xu ◽  
Hanqing Lu
Keyword(s):  
Scene Segmentation ◽  
Segmentation Method ◽  
Role Based

scholarly journals Benchmark Dataset Based on Category Maps with Indoor–Outdoor Mixed Features for Positional Scene Recognition by a Mobile Robot

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Hirokazu Madokoro ◽  
Hanwool Woo ◽  
Stephanie Nix ◽  
Kazuhito Sato
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Indoor Environment ◽  
Scene Recognition ◽  
Visual Features ◽  
Wide Field ◽  
Mixed Features ◽  
Wide Field Of View ◽  
Benchmark Datasets ◽  
Unified Method

This study was conducted to develop original benchmark datasets that simultaneously include indoor–outdoor visual features. Indoor visual information related to images includes outdoor features to a degree that varies extremely by time, weather, and season. We obtained time-series scene images using a wide field of view (FOV) camera mounted on a mobile robot moving along a 392-m route in an indoor environment surrounded by transparent glass walls and windows for two directions in three seasons. For this study, we propose a unified method for extracting, characterizing, and recognizing visual landmarks that are robust to human occlusion in a real environment in which robots coexist with people. Using our method, we conducted an evaluation experiment to recognize scenes divided up to 64 zones with fixed intervals. The experimentally obtained results using the datasets revealed the performance and characteristics of meta-parameter optimization, mapping characteristics to category maps, and recognition accuracy. Moreover, we visualized similarities between scene images using category maps. We also identified cluster boundaries obtained from mapping weights.

Robust Vision-Based Monitoring of Indoor Environments by an Autonomous Mobile Robot

2007 ◽  
Author(s):  
Donato Di Paola ◽  
Annalisa Milella ◽  
Grazia Cicirelli ◽  
Arcangelo Distante
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Experimental Tests ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Environment Monitoring ◽  
Inference System ◽  
Sift Algorithm ◽  
Matching Process ◽  
Logic Inference

This paper presents a novel vision-based approach for indoor environment monitoring by a mobile robot. The proposed system is based on computer vision methods to match the current scene with a stored one, looking for new or removed objects. The matching process uses both keypoint features and colour information. A PCA-SIFT algorithm is employed for feature extraction and matching. Colour-based segmentation is performed separately, using HSV coding. A fuzzy logic inference system is applied to fuse information from both steps and decide whether a significant variation of the scene has occurred. Results from experimental tests demonstrate the feasibility of the proposed method in robot surveillance applications.

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