Academic literature on the topic 'Active stereo vision'

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Journal articles on the topic "Active stereo vision"

1

Grosso, E., and M. Tistarelli. "Active/dynamic stereo vision." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 9 (1995): 868–79. http://dx.doi.org/10.1109/34.406652.

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2

Jang, Mingyu, Hyunse Yoon, Seongmin Lee, Jiwoo Kang, and Sanghoon Lee. "A Comparison and Evaluation of Stereo Matching on Active Stereo Images." Sensors 22, no. 9 (2022): 3332. http://dx.doi.org/10.3390/s22093332.

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The relationship between the disparity and depth information of corresponding pixels is inversely proportional. Thus, in order to accurately estimate depth from stereo vision, it is important to obtain accurate disparity maps, which encode the difference between horizontal coordinates of corresponding image points. Stereo vision can be classified as either passive or active. Active stereo vision generates pattern texture, which passive stereo vision does not have, on the image to fill the textureless regions. In passive stereo vision, many surveys have discovered that disparity accuracy is heavily reliant on attributes, such as radiometric variation and color variation, and have found the best-performing conditions. However, in active stereo matching, the accuracy of the disparity map is influenced not only by those affecting the passive stereo technique, but also by the attributes of the generated pattern textures. Therefore, in this paper, we analyze and evaluate the relationship between the performance of the active stereo technique and the attributes of pattern texture. When evaluating, experiments are conducted under various settings, such as changing the pattern intensity, pattern contrast, number of pattern dots, and global gain, that may affect the overall performance of the active stereo matching technique. Through this evaluation, our discovery can act as a noteworthy reference for constructing an active stereo system.
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3

Gasteratos, Antonios. "Tele-Autonomous Active Stereo-Vision Head." International Journal of Optomechatronics 2, no. 2 (2008): 144–61. http://dx.doi.org/10.1080/15599610802081753.

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4

Yexin Wang, Yexin Wang, Fuqiang Zhou Fuqiang Zhou, and Yi Cui Yi Cui. "Single-camera active stereo vision system using fiber bundles." Chinese Optics Letters 12, no. 10 (2014): 101301–4. http://dx.doi.org/10.3788/col201412.101301.

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5

Samson, Eric, Denis Laurendeau, Marc Parizeau, Sylvain Comtois, Jean-François Allan, and Clément Gosselin. "The Agile Stereo Pair for active vision." Machine Vision and Applications 17, no. 1 (2006): 32–50. http://dx.doi.org/10.1007/s00138-006-0013-7.

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6

Feller, Michael, Jae-Sang Hyun, and Song Zhang. "Active Stereo Vision for Precise Autonomous Vehicle Control." Electronic Imaging 2020, no. 16 (2020): 258–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-257.

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This paper describes the development of a low-cost, lowpower, accurate sensor designed for precise, feedback control of an autonomous vehicle to a hitch. The solution that has been developed uses an active stereo vision system, combining classical stereo vision with a low cost, low power laser speckle projection system, which solves the correspondence problem experienced by classic stereo vision sensors. A third camera is added to the sensor for texture mapping. A model test of the hitching problem was developed using an RC car and a target to represent a hitch. A control system is implemented to precisely control the vehicle to the hitch. The system can successfully control the vehicle from within 35° of perpendicular to the hitch, to a final position with an overall standard deviation of 3.0 m m of lateral error and 1.5° of angular error.
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Ko, Jung-Hwan. "Active Object Tracking System based on Stereo Vision." Journal of the Institute of Electronics and Information Engineers 53, no. 4 (2016): 159–66. http://dx.doi.org/10.5573/ieie.2016.53.4.159.

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8

Porta, J. M., J. J. Verbeek, and B. J. A. Kröse. "Active Appearance-Based Robot Localization Using Stereo Vision." Autonomous Robots 18, no. 1 (2005): 59–80. http://dx.doi.org/10.1023/b:auro.0000047287.00119.b6.

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9

Yongchang Wang, Kai Liu, Qi Hao, Xianwang Wang, D. L. Lau, and L. G. Hassebrook. "Robust Active Stereo Vision Using Kullback-Leibler Divergence." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 3 (2012): 548–63. http://dx.doi.org/10.1109/tpami.2011.162.

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10

Mohamed, Abdulla, Phil F. Culverhouse, Ricardo De Azambuja, Angelo Cangelosi, and Chenguang Yang. "Automating Active Stereo Vision Calibration Process with Cobots." IFAC-PapersOnLine 50, no. 2 (2017): 163–68. http://dx.doi.org/10.1016/j.ifacol.2017.12.030.

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