• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.565-574
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• 2015

Recently, a medium-level representation named "Stixel" has been extensively researched in stereo vision-based environmental perception. Obstacle detection using Stixel representation consists of three steps: static Stixel generation, dynamic Stixel generation, and Stixel segmentation. This paper focuses on the Stixel segmentation step and has two contributions. One is that it shows that Stixel segmentation performance can be enhanced by utilizing both image domain and real world domain features. The other is that it suggests that parameters used for Stixel segmentation can be effectively tuned based on genetic algorithm. The proposed method was quantitatively evaluated and the result showed that the proposed method increased Stixel segmentation accuracy compared with the previous method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.10-16
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• 2012

For high speed visual inspection in semiconductor industries, it is essential to acquire two-dimensional images on regions of interests with a large field of view (FOV) and a high resolution simultaneously. In this paper, an imaging system is newly proposed to achieve high quality image in terms of precision and FOV, which is composed of single lens, a beam splitter, two camera sensors, and stereo image grabbing board. For simultaneously acquired object images from two camera sensors, Zhang's camera calibration method is applied to calibrate each camera first of all. Secondly, to find a mathematical mapping function between two images acquired from different view cameras, the matching matrix from multiview camera geometry is calculated based on their image homography. Through the image homography, two images are finally registered to secure a large inspection FOV. Here the inspection system of using multiple images from multiple cameras need very fast processing unit for real-time image matching. For this purpose, parallel processing hardware and software are utilized, such as Compute Unified Device Architecture (CUDA). As a result, we can obtain a matched image from two separated images in real-time. Finally, the acquired homography is evaluated in term of accuracy through a series of experiments, and the obtained results shows the effectiveness of the proposed system and method.