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Stereo correspondence using an assisted discrete cosine transform method.

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thesis
posted on 24.05.2021, 14:48 by Edward Rosales
Many approaches have been taken towards the development of a compliant stereo correspondence algorithm that is capable of producing accurate disparity maps within a short period of time. There has been great progress over the past decade due to the vast increase in optimization techniques. Currently, the most successful algorithms contain explicit assumptions of the real world such as definitive differences in disparity among objects and constant textures within objects. This thesis starts by giving a brief description of disparity, along with descriptions of some common applications. Next, it explores various methods used in common stereo correspondence algorithms, as well as gives an in depth description and analysis of top performing algorithms. These algorithms are later used to compare with the proposed algorithm. In the proposed algorithm, frequency stereo correspondence in parallel with the traditional color intensity stereo correspondence is used to develop an initial disparity map. Frequency stereo correspondence is achieved using a winner-take-all block based Discrete Cosine Transform (DCT) to find the largest frequency components as well as their positions to use in disparity estimation. The proposed algorithm uses methods that are computationally inexpensive to reduce the computational time that plagues many of the common stereo correspondence algorithms. The proposed algorithm achieves an average correct disparity rate of 95.3%. This results in a disparity error rate of 4.07% compared to the top performing algorithms in the Middlebury website [1]; the DoubleBP, CoopRegion, AdaptingBP, and ADCensus algorithms that have error rates of 4.19%, 4.41%, 4.23%, and 3.97%, respectively. Additionally, experimental results demonstrate that the proposed algorithm is computationally efficient and significantly reduces the processing time that plagues many of the common stereo correspondence algorithms.

History

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis