Cost and power efficient FPGA based stereo vision system using directional graph transform

Dehnavi, Mohammad; Eshghi, Mohammad

doi:10.1016/j.jvcir.2018.09.002

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…The Version 2 dataset is adopted in [7]- [8], [13]- [15], and [19]- [20], which contains four sets of ground truths of disparity maps without noise. The Version 3 dataset is adopted in [22]- [23], it provides more complicated scenes with a light-noise environment. The following section provides detailed comparisons made by using these two datasets, and the comparing results between different system configurations are also provided.…”

Section: Resultsmentioning

confidence: 99%

“…When the oe " was equal to 4.0, the error rate of the proposed design is 19.64%. Compared to the design from [23], the proposed algorithm can lead to a 27.98% reduction in the error rate on average. To summarize, the used of the proposed design leads to the lowest error rate when compared with the designs also adopting the Version 3 dataset.…”

Section: B Comparison With Middlebury Version 3 Datasetmentioning

confidence: 99%

“…To improve the accuracy of estimation in complex and light-noise environments, some algorithms [23] involved the use of encoding methods to calculate the energy or the cost and then decided the disparity results. However, to the best of our knowledge, there are still many problems that need to be overcome when processing these environments.…”

Section: B Regional Matching Algorithmmentioning

confidence: 99%

“…Table III lists the evaluation results with the Version 3 dataset. Here, for the purpose of validation, we adopted the designs from [22]- [23]. The average error rate of the proposed algorithm was 30.30% when the oe " was equal to 1.0.…”

Section: B Comparison With Middlebury Version 3 Datasetmentioning

confidence: 99%

See 3 more Smart Citations

A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

Chen

Liao

et al. 2022

IEEE Access

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Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost.INDEX TERMS Depth estimation, hardware implementation, high-speed, low-cost, stereo matching.

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Section: Resultsmentioning

confidence: 99%

Section: B Comparison With Middlebury Version 3 Datasetmentioning

confidence: 99%

Section: B Regional Matching Algorithmmentioning

confidence: 99%

Section: B Comparison With Middlebury Version 3 Datasetmentioning

confidence: 99%

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A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

Chen

Liao

et al. 2022

IEEE Access

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“…The process can be classified into two major categories which is global method and local method [3]. By using global method, the disparity values are computed by optimizing a general energy function for all pixels of an image [4]. This method gives a higher accuracy, but it also required high computational complexity [5].…”

Section: Introductionmentioning

confidence: 99%

Stereo matching algorithm based on combined matching cost computation and edge preserving filters

Zahari

Hamzah

Manap

et al. 2022

IJEECS

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The stereo matching <span>process is one of the key areas that impact the stereo vision technologies which are commonly used in the application of three-dimensional reconstructions. The accuracy of the depth information used in three-dimensional reconstruction is directly proportional to the accuracy of the disparity obtained from stereo matching. The challenging issue in the stereo matching process is to determine the accurate corresponding point between the left image and right image, especially for image pairs that have different exposure such as different illumination and image pair with less texture region. In order to increase the accuracy of disparity value, a new stereo matching algorithm is proposed based on the combination of Sum of absolute different and census transform at matching cost computation. guided filter was used in the matching cost aggregation in order to remove noise and preserve the edge of the image. In the optimization step, the winner take all strategy is used to select the minimum matching cost. Finally, a median filter is applied to the initial disparity map for refinement purposes. The experimental results show that the algorithm is effective in reducing the error and improving the accuracy of the disparity map in different illumination regions, less textured regions and different environmental exposure.</span>

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Implementation of a real‐time stereo vision algorithm on a cost‐effective heterogeneous multicore platform

Saidi

Khouas

Amira

2022

Concurrency and Computation

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Summary Stereo vision is a major computer vision technique commonly used for robotics applications. Existing software implementations of this technique on general‐purpose processors offer low time‐to‐market compared to other platforms. However, such implementations can hardly achieve real‐time and their cost is usually relatively high. These issues can be solved by embedded multicore platforms. In this article, we present a low‐cost, improved software implementation of a stereo matching algorithm in the correlation stage that combines a sparse rank transform with a combination of sum of absolute differences 1‐D and 2‐D box filtering algorithms. A circular buffer scheme is used to optimize memory usage during the rank computation stage. The system runs on a heterogeneous multicore platform (ODROID XU4). Through the extensive use of single instruction multiple data Neon intrinsics, the system can process images with a size of 320prefix×240$$ 320\times 240 $$ pixels and a disparity range of 20 pixels at a rate of 111 frames per second. The proposed system can be used in mobile robot platforms that require low power consumption while delivering real‐time performance.

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Cost and power efficient FPGA based stereo vision system using directional graph transform

Cited by 7 publications

References 35 publications

A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

Stereo matching algorithm based on combined matching cost computation and edge preserving filters

Implementation of a real‐time stereo vision algorithm on a cost‐effective heterogeneous multicore platform

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