scholarly journals Measurement of Human Flow Information Using Range Image Sensor

Author(s):  
Satoshi ABIKO ◽  
Shigenori TANAKA ◽  
Shinichi MURAMOTO ◽  
Kouhei UETANI ◽  
Katsuma WAKABAYASHI
2008 ◽  
Vol 74 (739) ◽  
pp. 499-505 ◽  
Author(s):  
Masateru TATEISHI ◽  
Hidetoshi ISHIYAMA ◽  
Kazunori UMEDA

Author(s):  
Shuya Kawaguchi ◽  
Hiroshi Takemura ◽  
Hiroshi Mizoguchi ◽  
Fusako Kusunoki ◽  
Ryohei Egusa ◽  
...  

2016 ◽  
Vol 2016 (12) ◽  
pp. 1-6 ◽  
Author(s):  
Taichi KASUGAI ◽  
Sang-Man HAN ◽  
Hanh TRANG ◽  
Taishi TAKASAWA ◽  
Satoshi AOYAMA ◽  
...  

2007 ◽  
Vol 19 (3) ◽  
pp. 290-297
Author(s):  
Naotaka Hikosaka ◽  
◽  
Kei Watanabe ◽  
Kazunori Umeda

We discuss the recognition of obstacles by detecting a plane using relative disparity maps obtained from a small range image sensor incorporated in a humanoid. Our proposal enables easy plane detection and obstacle recognition using relative disparity from a reference plane alone. We built an integrated controller that feeds back obstacle information to the humanoid. We confirmed through experiments that the humanoid recognized obstacles and autonomously stopped walking.


Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 213 ◽  
Author(s):  
Yan Liu ◽  
Bingxue Lv ◽  
Wei Huang ◽  
Baohua Jin ◽  
Canlin Li

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.


2016 ◽  
Vol 2016 (12) ◽  
pp. 1-6 ◽  
Author(s):  
Hanh Trang ◽  
Taichi Kasugai ◽  
Keigo Isobe ◽  
Sang Man Han ◽  
Taichi Takasawa ◽  
...  
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