• Journal of Korea Game Society
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• v.10 no.4
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• pp.63-71
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• 2010

In this paper, we propose a boundary based marker binary coding method for augmented reality games, which enables the marker-area to be binary coded well in any lighting environments. First, it detects the boundary after transforming an original marker image to a gray scale image, and it executes 4 way pixel extensions for all boundary pixels in order to make the boundary to closed area. Next, for all boundary pixels it compares the brightness of right and left ones of each pixel and allocates black for the lower side and white for the higher side by filling inside area thru the seeded region growing. Experimental results showed that our proposed method produces a good binary marker image recognizable in various light environments. In addition, it showed the possibility of real-time calculation by considering the result of operation speed which is 51 fps for VGA image.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.63-73
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• 2009

This paper presents a vehicle detection method that uses convolution matching method based on a simple color information. An input image is expressed as 8 oriented color expression(Red, Green, Blue, White, Black, Cyan, Yellow, Magenta) considering an orientation of a pixel color vector. It makes the image very reliable and strong against changes of illumination condition or environment. This paper divides the vehicle detection into a hypothesis generation step and a hypothesis verification step. In the hypothesis generation step, the vehicle candidate region is found by vertical edge and shadow. In the hypothesis verification step, the convolution matching and the complexity of image edge are used to detect real vehicles. It is proved that the proposed method has the fast and high detection rate on various experiments where the illumination source and environment are changed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.283-290
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• 2006

The cellular neural networks have the structure that consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.