摘要 :
This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondenc...
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This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.
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摘要 :
This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is se...
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This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.
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摘要 :
The stereo matching algorithm is one of the important methods for 3D surface reconstruction. A stereo matching process produces a disparity map which provides the depth of information required in 3D reconstruction. This map consis...
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The stereo matching algorithm is one of the important methods for 3D surface reconstruction. A stereo matching process produces a disparity map which provides the depth of information required in 3D reconstruction. This map consists of disparity values of two corresponding points. Furthermore, the accuracy of 3D reconstruction depends on how precise the disparity being estimated on each pixel location. To get a good 3D reconstruction result, the propose stereo matching algorithm must be strong against the radiometric differences and edge distortions. Hence, this article proposes a new stereo matching algorithm with high accuracy for 3D surface reconstruction. First stage, Sum of Gradient Matching (SG) is proposed which uses magnitude differences with fixed window size. The gradient matching is strong against the radiometric distortions due to different characteristics of the input stereo cameras. Second stage, the Adaptive Support Weight (ASW) with iterative Guided Filter (ASW iGF) is proposed to improve the edges of object matching. The last stage, Joint Weighted Guided Filter (JWGF) is suggested to reduce the remaining noise on the disparity map. Based on the standard quantitative benchmarking stereo dataset, the proposed work in this article produces good results and performs much better compared with before the proposed framework. This new algorithm is also competitive with some established methods in the literature.
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