• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.809-814
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• 2013

Summed area table (SAT) is a data structure in which the sum of pixel values in an arbitrary rectangular area can be represented by the linear combination of four pixel values. Since SAT serially accumulates the pixel values from an image corner to the other corner, a high-resolution image can yield overflow in a floating-point representation. In this paper, we present a new SAT construction technique, which accumulates only the residuals from the linearly-regressed representation of an image and thereby significantly reduces the accumulation errors. Also, we propose a method to find the integral of the linear regression in constant time using double integral. We performed experiments on the image reconstruction, and the results showed that our approach more reduces the accumulation errors than the conventional fixed-offset SAT.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.386-388
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• 2013

합산 영역 테이블(Summed Area Table)을 사용하면 현재 픽셀 주변으로 임의의 사각 영역의 평균을 모든 픽셀을 읽을 필요 없이, 단 4번의 픽셀의 합과 차로 표시할 수 있다. 그러나 많은 픽셀의 값이 누적되는 경우 부동소수점 표현의 정밀도가 떨어지는 문제가 발생한다. 따라서 본 논문에서는 합산 영역 테이블의 정밀도를 향상시키기 위한 방법으로 선형 회귀분석(linear regression)을 이용한 오프셋을 사용할 것을 제안한다. 회귀분석을 통해 구축한 다항식을 통해 픽셀 그리고 채널 별로 다른 오프셋을 적용하여 정밀도를 효과적으로 향상하였다.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.67-78
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• 2012

Summed area table allows filtering of arbitrary-width box regions for every pixel in constant time per pixel. This characteristic makes it beneficial in image processing applications where the sum or average of the surrounding pixel intensity is required. Although calculating the summed area table of an image data is primarily a memory bound job consisting of row or column-wise summation, previous works had to endure excessive access to the high latency global memory in order to exploit data parallelism. In this paper, we propose an efficient algorithm for generating the summed area table in the GPGPU environment where the input is decomposed into square sub-images with intermediate data that are propagated between them. By doing so, the global memory access is almost halved compared to the previous methods making an efficient use of the available memory bandwidth. The results show a substantial increase in performance.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.159-168
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• 2015

This paper introduces effective noise removal method for medical ultrasound volume data. Ultrasound volume data need to be filtered because it has a lot of noise. Conventional 2d filtering methods ignore information of adjacent layers and conventional 3d filtering methods are slow or have simple filter that are not efficient for removing noise and also don't equally operate filtering because that don't take into account ultrasound' sampling character. To solve this problem, we introduce method that fast perform in parallel bilateral filtering that is known as good for noise removal and adjust proportionally window size depending on that's position. Experiments compare noise removal and loss of original data among average filtered or biliteral filtered or adaptive biliteral filtered ultrasound volume rendering images. In this way, we can more efficiently and correctly remove noise of ultrasound volume data.