Quaternary Census Transform Based on the Human Visual System for Stereo Matching

Ji, Seo-Won; Kim, Seung-Wook; Lim, Dongpan; Jung, Seung Won; Ko, Sung Jea

doi:10.1109/access.2020.3003919

Cited by 10 publications

(1 citation statement)

References 42 publications

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“…To get a more efficient bit sting, the sparse CT [24] studied the influence of different block sizes and different sparse factors for CT on the accuracy, and the starcensus transform [25] proposed the concept of scan patterns which set different orders to compare the intensity between the centre pixel and surrounding pixels. Besides, recent researches like [26] proposed a new type of adaptive window method to overcome the defects of the fixed window used in traditional census algorithm, and a quaternary code stream was proposed to get a more efficient threshold [27].…”

Section: Introductionmentioning

confidence: 99%

An improved point feature‐based sparse stereo vision

Chen

Song

et al. 2022

IET Image Processing

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Since the limitation on the onboard equipment, the sparse stereo vision is becoming a suitable choice for the deployment of micro air vehicles (MAV) and small robots. However, for the point feature-based sparse stereo, most of the current stereo algorithms ignore the similarity between feature points, so it is hard to achieve high accuracy. In addition, the problem of clustered feature distribution will still affect the performance of point featurebased algorithms in the application. To make up for these deficiencies, the authors propose an improved features from accelerated segment test (FAST) feature detector to suppress the point detection in complex texture regions. Most importantly, the authors present a novel census transform (CT)-based algorithm that contains two encoders 'texture orientation' and 'texture gradient' to get a more efficient census bit string for the feature point. Instead of randomly selecting pixels to calculate the bit string, we combine the texture characteristics of the census windows where feature points are located. Compared with the original CT, the processing speed of our method is improved, and the average error of our method is reduced by 18.05%. The evaluation results show the presented improved point feature-based sparse stereo algorithm has a great value in engineering applications.

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