A Single-Camera Stereo Apparatus for Road Detection

2018 ◽  
Author(s):  
Zhiyuan Chen ◽  
Yingna Su ◽  
Hui Kong
Keyword(s):  
Single Camera ◽  
Road Detection

Wheelchair robot navigation in different weather conditions using deep learning and evolved neural controller

2019 ◽  
Vol 46 (1) ◽  
pp. 146-158 ◽  
Author(s):  
K.M. Ibrahim Khalilullah ◽  
Shunsuke Ota ◽  
Toshiyuki Yasuda ◽  
Mitsuru Jindai
Keyword(s):  
Neural Network ◽  
Dimensionality Reduction ◽  
Robot Navigation ◽  
Weather Conditions ◽  
Single Camera ◽  
Road Detection ◽  
Google Maps ◽  
Neural Controller ◽  
Content Type ◽  
Road Junction

Purpose Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people. Design/methodology/approach A road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination. Findings The developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments. Originality/value The wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.

Muybridge / Technology

2010 ◽  
Vol 1 (1) ◽  
pp. 51-62
Author(s):  
Marta Braun
Keyword(s):  
Single Camera ◽  
Art And Science ◽  
Animal Locomotion ◽  
Human Figure ◽  
Final Work

Eadweard Muybridge's 1887 photographic atlas Animal Locomotion is a curious mixture of art and science, a polysemic text that has been subject to a number of readings. This paper focuses on Muybridge's technology. It seeks to understand his commitment to making photographs with a battery of cameras rather than a single camera. It suggests reasons for his choice of apparatus and shows how his final work, The Human Figure in Motion (1901), justifies the choices he made.

scholarly journals Fast 3D Rotation Estimation of Fruits Using Spheroid Models

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2232
Author(s):  
Antonio Albiol ◽  
Alberto Albiol ◽  
Carlos Sánchez de Merás
Keyword(s):  
Fruit Quality ◽  
3D Model ◽  
Geometric Model ◽  
Fruit Shape ◽  
Single Camera ◽  
Global Score ◽  
Topographic Map ◽  
Single Image ◽  
Large Effort ◽  
Fruit Surface

Automated fruit inspection using cameras involves the analysis of a collection of views of the same fruit obtained by rotating a fruit while it is transported. Conventionally, each view is analyzed independently. However, in order to get a global score of the fruit quality, it is necessary to match the defects between adjacent views to prevent counting them more than once and assert that the whole surface has been examined. To accomplish this goal, this paper estimates the 3D rotation undergone by the fruit using a single camera. A 3D model of the fruit geometry is needed to estimate the rotation. This paper proposes to model the fruit shape as a 3D spheroid. The spheroid size and pose in each view is estimated from the silhouettes of all views. Once the geometric model has been fitted, a single 3D rotation for each view transition is estimated. Once all rotations have been estimated, it is possible to use them to propagate defects to neighbor views or to even build a topographic map of the whole fruit surface, thus opening the possibility to analyze a single image (the map) instead of a collection of individual views. A large effort was made to make this method as fast as possible. Execution times are under 0.5 ms to estimate each 3D rotation on a standard I7 CPU using a single core.

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