Stereo Vision embedded system for Augmented Reality

Gudis, Eduardo; Wal, G. van der; Kuthirummal, Sujit; Chai, Sek; Samarasekera, Supun; Kumar, Rakesh; Branzoi, Vlad

doi:10.1109/cvprw.2012.6238889

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The suggested work in this article is using the bilateral filter (BF), which works for the second filtering process. The final disparity result is given in (5): (5) where the (p,q) represent the pixel of interest and neighbor pixel coordinates, w b denotes the BF window size, and are the intensity differences and spatial distance respectively, and are the spatial distance and color similarity parameters.…”

Section: Post-processingmentioning

confidence: 99%

“…The map contains the depth information and locations of the objects detection. The triangulation principle [4] is utilized to calculate the depth, which will be used in numerous applications in machine vision applications such as industrial automations augmented reality (AR) [5] and robotic arm [6]. Some of the applications are capable to be implemented in real time or near real time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Objects Detection Using Stereo Vision Sensor for Machine Vision Applications

Hamzah¹,

Wei²,

Anwar³

et al. 2018

IJET

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This paper presents a new algorithm for object detection using a stereo camera system, which is applicable for machine vision applications. The propose algorithm has four stages which the first stage is matching cost computation. This step acquires the preliminary result using a pixel based differences method. Then, the second stage known as aggregation step uses a guided filter with fixed window support size. This filter is efficiently reduce the noise and increase the edge properties. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter, which is effectively further reduce the remaining noise on the disparity map. This map is two-dimensional mapping of the final result which contains information of object detection and locations. Based on the standard benchmarking stereo dataset, the proposed work produces good results and performs much better compared with some recently published methods.

show abstract

Section: Post-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Objects Detection Using Stereo Vision Sensor for Machine Vision Applications

Hamzah¹,

Wei²,

Anwar³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…The map contains the depth information and locations of the objects detection. The triangulation principle [4] is utilized to calculate the depth which will be used in numerous applications in machine vision applications such as industrial automations augmented reality (AR) [5] and robotic arm [6].…”

Section: Introductionmentioning

confidence: 99%

Stereo matching based on absolute differences for multiple objects detection

Hamzah¹,

Wei²,

Anwar³

2019

TELKOMNIKA

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This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.

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Stereo Vision embedded system for Augmented Reality

Cited by 2 publications

References 9 publications

Accurate Objects Detection Using Stereo Vision Sensor for Machine Vision Applications

Accurate Objects Detection Using Stereo Vision Sensor for Machine Vision Applications

Stereo matching based on absolute differences for multiple objects detection

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