A census-based stereo matching algorithm with multiple sparse windows

Bae, Kyeong-ryeol; Son, Hyeon-Sik; Hyun, Jongkil; Moon, Byungin

doi:10.1109/icufn.2015.7182542

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…We used an eight-bit, gray-level 752 × 480 image and a 15 × 15 window for the stereo matching and an 11 × 11 window for the post-processing. We chose these window sizes because of their high degree of matching accuracy [ 22 ]. Figure 4 shows the fully pipelined hardware architecture of the stereo matching processor, and Table 2 summarizes the features of the stereo matching processors.…”

Section: Stereo Matching Processormentioning

confidence: 99%

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

Lee

Shim

et al. 2017

Sensors

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Abstract:Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical solution to achieving the desired requirements of a high-performance circuit. In this paper, we present the benefits of 3D stacking and process technology scaling on stereo matching processors. We implemented 2-tier 3D-stacked stereo matching processors with GlobalFoundries 130-nm and Nangate 45-nm process design kits and compare them with their two-dimensional (2D) counterparts to identify comprehensive design benefits. In addition, we examine the findings from various analyses to identify the power benefits of 3D-stacked integrated circuit (IC) and device technology advancements. From experiments, we observe that the proposed 3D-stacked ICs, compared to their 2D IC counterparts, obtain 43% area, 13% power, and 14% wire length reductions. In addition, we present a logic partitioning method suitable for a pipeline-based hardware architecture that minimizes the use of TSVs.

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Section: Stereo Matching Processormentioning

confidence: 99%

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

Lee

Shim

et al. 2017

Sensors

Self Cite

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“…The MSW method aggregates the matching costs of the adjacent sparse windows (SWs) to calculate one matching cost. By applying it to ACT in the calculation of the RMCs of the pixels in the support window, the proposed algorithm becomes robust to noise and generates a slight blurring effect because it aggregates the calculated matching costs of the SWs, whose center pixels are different from each other [13]. In addition, it improves the reliability of the matching cost by using the TAD between the center pixels of a pair of left and right SWs.…”

Section: Proposed Stereo Matching Algorithmmentioning

confidence: 99%

“…This paper, therefore, proposes an improved stereo matching algorithm [12] based on the existing ACT and analyzes its results. To achieve better matching accuracy and robustness to noise, the proposed algorithm adopts the truncated absolute difference (TAD) [8,9] and the multiple sparse windows (MSWs) method [13]. Section 2 of the paper introduces the existing ACT, Section 3 explains the proposed stereo matching algorithm, Section 4 evaluates the experimental results, and Section 5 concludes it.…”

Section: Introductionmentioning

confidence: 99%

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

2017

J Inf Process Syst

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An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

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Modified adaptive support weight and disparity search range estimation schemes for stereo matching processors

Shim

Moon

2017

J Supercomput

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A census-based stereo matching algorithm with multiple sparse windows

Cited by 7 publications

References 10 publications

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

Modified adaptive support weight and disparity search range estimation schemes for stereo matching processors

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