Cooperative Stereo Matching with Color-Based Adaptive Local Support

Brockers, Roland

doi:10.1007/978-3-642-03767-2_124

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…The mutual information cost is also rescaled to a range from 0 to 1023. Due to the fixed local support, the disparity images generated using Census show slightly blurred object boundaries (Brockers, 2009). Hence, a new matching cost in this paper is a merging of MI and Census.…”

Section: Matching Costsmentioning

confidence: 99%

Evaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images

Zhu

d’Angelo

Butenuth

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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In the last years, most dense stereo matching methods use evaluation on the Middlebury stereo vision benchmark datasets. Most recent stereo algorithms were designed to perform well on these close range stereo datasets with relatively small baselines and good radiometric behaviour. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using the common Middlebury datasets, aerial and satellite datasets with ground truth. The experimental results show that the performance of dense stereo methods for datasets with larger baselines and stronger radiometric changes relies on even more robust matching costs. In addition, a novel matching cost based on mutual information and Census is introduced showing the most robust performance on close range, aerial and satellite data.

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Section: Matching Costsmentioning

confidence: 99%

Evaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images

Zhu

d’Angelo

Butenuth

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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“…These registers are kept in buffers during the next processing round so that they can be reused during several clock periods (7,8). Second the column SAD's from several processing rounds are summed to calculated the SAD values (4,5,6). For a 3x3 subwindow with a disparity range of 2; 9 AD calculations, 12 adders and 3 registers files with a size equal to the window width are needed.…”

Section: F Sum Of Absolute Differencesmentioning

confidence: 99%

“…An example of such a cooperative algorithm that makes use of color information can be found in the paper of Brockers [6]. He proposes to use color information in a local window to align support areas with local borders to keep object boundaries while using a cooperative stereo method.…”

Section: Introductionmentioning

confidence: 99%

A binary adaptable window SoC architecture for a stereo vision based depth field processor

Motten

Claesen

2010

2010 18th IEEE/IFIP International Conference on VLSI and System-on-Chip

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Abstract-This paper presents a novel binary fully adaptable window for incorporating in a stereo matching System-on-Chip (SoC) architecture. This architecture is fully scalable and parameterizable to allow for custom SoC implementations, as well as rapid prototyping on FPGAs. For each window a binary mask window is generated which selects the supporting pixels in the cost aggregation phase of the SAD algorithm. This selection is performed using color similarity and spatial distance metrics. Hardware resource utilization for a fixed window and an adaptable window cost aggregation is compared based on FPGA logic element use.

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“…Instead of segmenting images using computationally intensive algorithms, this grouping can be coded using pre-calculated bilateral weights of local support areas [11]. The final smoothness term is expressed as follows:…”

Section: Disparity Smoothnessmentioning

confidence: 99%

HDR image construction from multi-exposed stereo LDR images

Sun

Mansour

Ward

2010

2010 IEEE International Conference on Image Processing

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In this paper, we present an algorithm that generates high dynamic range (HDR) images from multi-exposed low dynamic range (LDR) stereo images. The vast majority of cameras in the market only capture a limited dynamic range of a scene. Our algorithm first computes the disparity map between the stereo images. The disparity map is used to compute the camera response function which in turn results in the scene radiance maps. A refinement step for the disparity map is then applied to eliminate edge artifacts in the final HDR image. Existing methods generate HDR images of good quality for still or slow motion scenes, but give defects when the motion is fast. Our algorithm can deal with images taken during fast motion scenes and tolerate saturation and radiometric changes better than other stereo matching algorithms.Index Terms-High dynamic range imaging, stereo matching.

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Cooperative Stereo Matching with Color-Based Adaptive Local Support

Cited by 15 publications

References 13 publications

Evaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images

Evaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images

A binary adaptable window SoC architecture for a stereo vision based depth field processor

HDR image construction from multi-exposed stereo LDR images

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