Census Filtering Based Stereomatching under Varying Radiometric Conditions

Sharma, Sharda; Deepambika, V.A.; Rahman, M.

doi:10.1016/j.procs.2015.08.026

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Combined census and adaptive window for handling radiometric changes is presented in (San and War, 2017). Census filtering and hamming distance based cost aggregation is proposed in (Sarika et al, 2015). Evaluation of stereo matching cost for varying radiometric images is introduced in (Hirschmuller and Scharstein, 2009;Stolc et al, 2016) proposes stochastic binary local descriptor for the stereo correspondence.…”

Section: Related Workmentioning

confidence: 99%

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Deepambika¹,

Rahiman²

2019

Journal of Computer Science

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Computer stereo vision tries to mimic human vision by grabbing multiple views of the same scene and cognizing it. The stereo correspondence will find out the matching pixels between the two views based on the Lambertian criteria, which results in disparity. The distance of the objects from the camera can be calculated using this disparity. But in the real world scenario, this Lambertian assumption may not work always due to the radiometric variation between the image pairs and the conventional approaches results in erroneous disparity. In this work, for doing the radiometric invariant stereo matching, the simple local binary pattern is used. The correspondence is done by using semi global block matching method, which can handle the depth changes of curved surfaces and slanting surfaces by adding suitable penalty terms. The performance evaluation of the proposed shows lesser error rate in the range of 0.14%-0.3883% and run time requirement of 0.20 milliseconds only. This radiometric invariant stereo correspondence attains accuracy as that of global method with run time speed as that of local method and is suitable for most of the real time stereo vision applications.

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Section: Related Workmentioning

confidence: 99%

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Deepambika¹,

Rahiman²

2019

Journal of Computer Science

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“…But in real world environments, there will be radiometric variations in the input images, due to illumination variations and the camera exposure time variations. Various non-parametric approaches can be used to handle these changes Sarika et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Optimised Implementation of Dense Stereo Correspondence for Resource Limited Hardware

Anirudhan¹,

Rahiman²

2018

Journal of Computer Science

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Computer stereo vision is a passive sensing technique that helps to recover 3D information of an environment from 2D images. The stereo correspondence is a challenging task that finds out matching pixels between the stereo image pair based on Lambertian criteria and its result is a disparity space image. The depth of the objects from the camera can be calculated from this disparity value by using the principle of triangulation. For the vision guided robot navigation, the requirement of stereo matching algorithms on low power dedicated hardware that can achieve a high frame rate is unambiguous. A new, highly optimized implementation of correlation based, Sum of Absolute Differences correspondences algorithm on a low cost resource limited FPGA is presented here. This System-on-Programmable-Chip architecture based system achieved a higher frame rate of 50 fps with 64 disparity levels without using a microprocessor. On performance evaluation, the disparity map shows a maximum error of 0.308% only. This reconfigurable parallel processing, high speed architecture of the algorithm implementation takes only 43% of available resources of low density Altera Cyclone II. This hardware implementation of stereo vision system outperforms in terms of accuracy, speed and resource utilization of all the other existing stereo systems of its similar kind. Also, it offers a better trade-off between run-time speed and accuracy and is found suitable for most of the range finding real-time applications.

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Real-Time Stereo Vision Hardware Accelerator: Fusion of SAD and Adaptive Census Algorithm

Yang,

Liang,

Lin

et al. 2024

IEEE Access

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Census Filtering Based Stereomatching under Varying Radiometric Conditions

Cited by 5 publications

References 7 publications

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Optimised Implementation of Dense Stereo Correspondence for Resource Limited Hardware

Real-Time Stereo Vision Hardware Accelerator: Fusion of SAD and Adaptive Census Algorithm

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