Stereo Vision for Obstacle Detection: A Graph-Based Approach

Foggia, Pasquale; Jolion, Jean-Michel; Limongiello, Alessandro; Vento, Mario

doi:10.1007/978-3-540-72903-7_4

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…view [14] For computation of disparity map of given stereoscopic images (the left and right image) the stereo matching algorithm is used so as to obtain a rectified stereo image pair [1]. Disparity computation can be done by using feature based algorithm and area based algorithm.…”

Section: Disparity Computation Fig 2 Disparity and Depth Computationmentioning

confidence: 99%

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Detection of Obstacles using Stereo Imaging

Deshmukh¹,

Deshpande²

2015

IJCA

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This paper presents an implementation of algorithm for detecting obstacles based on stereo vision technique. This algorithm mainly focuses on detection of obstacle and computation of obstacle depth based on stereo matching and disparity map. This technique incorporates several steps such as pre-processing and depth analysis for obstacle detection. In the preprocessing step, Otsu's thresholding and adaptive thresholding methods are used for efficient detection of obstacles. After computation of obstacles, depth analysis is performed with the help of disparity map. Experimental results are demonstrated giving robust analysis of obstacle detection based on standard stereo dataset.

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Section: Disparity Computation Fig 2 Disparity and Depth Computationmentioning

confidence: 99%

“…illustrates the overall idea of stereo vision camera arrangement for disparity and depth computation. Here, xl and yl are the coordinates of left image, xr and yr are the coordinates of the right image, f is the focal length, d is the distance between two images[14].…”

mentioning

confidence: 99%

Detection of Obstacles using Stereo Imaging

Deshmukh¹,

Deshpande²

2015

IJCA

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“…These methods use global primitives. Some research has used a graph-based method Foggia et al (2007) and color segmentation based stereo methods Taguchi et al (2008) which belong to what is called global approaches. Other approaches have been proposed: they are based on a probabilistic framework optimization, such as belief propagation Lee & Ho (2008).…”

Section: D Localization Of Obstacles By Stereo Matchingmentioning

confidence: 99%

Intelligent Surveillance System Based on Stereo Vision for Level Crossings Safety Applications

Fakhfakh¹,

Khoudour²,

Bruyelle³

et al. 2012

Recent Developments in Video Surveillance

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“…Cost-relaxation approaches, which were invented by Marr and Poggio [18] and which are picked up again by Brockers [19], belong to this family. Some research used a graph-based method [21] and color segmentation based stereo methods [20] which belong to what is called -global approaches‖. Other approaches were proposed: they are based on a probabilistic framework optimization, such as expectationmaximization [23] and belief propagation [22,27].…”

Section: Related Workmentioning

confidence: 99%