• 대한임베디드공학회논문지
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• 제16권5호
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• pp.233-240
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• 2021

We propose a novel, highly accurate approximate multiplier using different types of inexact 4-2 compressors. The importance of low hardware costs leads us to develop approximate multiplication for error-resilient applications. Several rules are developed when selecting a topology for designing the proposed multiplier. Our highly accurate multiplier design considers the different error characteristics of adopted compressors, which achieves a good error distribution, including a low relative error of 0.02% in the 8-bit multiplication. Our analysis shows that the proposed multiplier significantly reduces power consumption and area by 45% and 26%, compared with the exact multiplier. Notably, a trade-off relationship between error characteristics and hardware costs can be achieved when considering those of existing highly accurate approximate multipliers. In the image blending, edge detection and image sharpening applications, the proposed 8-bit approximate multiplier shows better performance in terms of image quality metrics compared with other highly accurate approximate multipliers.

• ETRI Journal
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• 제43권4호
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• pp.684-693
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• 2021

This paper presents a low-cost two-stage approximate multiplier for bfloat16 (brain floating-point) data processing. For cost-efficient approximate multiplication, the first stage implements Mitchell's algorithm that performs the approximate multiplication using only two adders. The second stage adopts the exact multiplication to compensate for the error from the first stage by multiplying error terms and adding its truncated result to the final output. In our design, the low-cost multiplications in both stages can reduce hardware costs significantly and provide low relative errors by compensating for the error from the first stage. We apply our approximate multiplier to the convolutional neural network (CNN) inferences, which shows small accuracy drops with well-known pre-trained models for the ImageNet database. Therefore, our design allows low-cost CNN inference systems with high test accuracy.

• Transactions on Electrical and Electronic Materials
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• 제18권6호
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• pp.320-322
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• 2017

In this paper, a high-performance, low-area gradient-magnitude calculator architecture is proposed, based on approximate image processing. To reduce the computational complexity of the gradient-magnitude calculation, vector properties, the symmetry axis, and common terms were applied in a hardware-resource-shared architec-ture. The proposed gradient-magnitude calculator was implemented using an Altera Cyclone IV FPGA (EP4CE115F29) and the Quartus II v.16 device software. It satisfied the output-data quality while reducing the logic elements by 23% and the embedded multipliers by 76%, compared with previous work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권4호
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• pp.497-505
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• 2016

The two-dimensional (2D) Discrete Cosine Transform (DCT) is used widely in image and video processing systems. The perception of human visualization permits us to design approximate rather than exact DCT. In this paper, we propose a digital implementation of 16-point approximate 2D DCT architecture based on one-dimensional (1D) DCT and Modified Gate Diffusion Input (MGDI) technique. The 8-point 1D Approximate DCT architecture requires only 12 additions for realization in digital VLSI. Additions can be performed using the proposed 8 transistor (8T) MGDI Full Adder which reduces 2 transistors than the existing 10 transistor (10T) MGDI Full Adder. The Approximate MGDI 2D DCT using 8T MGDI Full adders is simulated in Tanner SPICE for $0.18{\mu}m$ CMOS process technology at 100MHZ.The simulation result shows that 13.9% of area and 15.08 % of power is reduced in the 8-point approximate 2D DCT, 10.63 % of area and 15.48% of power is reduced in case of 16-point approximate 2D DCT using 8 Transistor MGDI Full Adder than 10 Transistor MGDI Full Adder. The proposed architecture enhances results in terms of hardware complexity, regularity and modularity with a little compromise in accuracy.

• 정보처리학회논문지D
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• 제18D권5호
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• pp.323-328
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• 2011

정확한 내용 기반 이미지 검색을 위하여 Earth mover's distance와 Optimal color composition distance와 같은 거리함수들이 제안되었다. 이 거리함수들은 정확도가 높은 검색 결과를 가져오지만 검색 시간이 매우 크기 때문에 대용량 데이터베이스에서 사용하기 어렵다는 문제점을 가지고 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 선형 시간에 근사 Earth mover's distance를 구하기 위한 새로운 거리 함수를 제안한다. 제안하는 방법은 선형 시간에 두 이미지의 거리를 계산하기 위하여 공간 채움 곡선을 이용한다. 다양한 실험을 통하여 본 논문에서 제안하는 방법의 우수성을 검증한다. 실험 결과, 제안하는 기법이 Earth mover's distance에 비해 약 160배 정도의 검색 속도 향상 효과를 보이면서도 매우 유사한 결과를 검색하는 것으로 나타났다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.284-287
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• 1998

It is important to develop a method of assembling a set of sub pictures automatically into a mosaic picture , because a view through fiberscopes or microscopes with higher magnifying power is much larger than the field of view taken by a camera. This paper presents a method of assembling sub pictures, where roughly estimated junctions called approximate junctions are employed for matching triangles formed by selected junctions in sub pictures. To over come the difficulties in processing speed and noise corruption, fuzzy rules is applied to get fuzzy values for existence of approximate junctions and fuzzy similarity for congruent triangle matching. Some demonstration, exemplified by assembling microscopic metal matrix photographs, are given to show feasibility of this method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.969-970
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• 2006

This paper develops a recognition system of assembly parts using a hardware independent image module. Using a shared memory, the image module consists of the image acquiring process and the image processing process. We preprocess an acquisition image from the module, approximate the image edges to an ellipse, and then recognize an assembly part by matching the ellipse to a model base one.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.163-169
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• 2017

There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

• 전자공학회논문지CI
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• 제37권1호
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• pp.43-54
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• 2000

본 논문에서는 특허청에 등록된 상표들 중에서 90%를 차지하는 문자기반 상표를 대상으로 질의상표와 유사한 상표들을 보다 정확하게 검색하기 위한 방법을 제안한다. 제안한 방법은 상표 칭호에 대한 근사매칭으로 1차 유사도를 계산하며 동일 유사도 그룹에 대해 상표 외형에 대한 영상 처리로 2차 유사도를 계산하여 순위를 부여한다. 전체 시스템의 한 부분으로 기존 도형상표와 함께 구현하였으며, 성능평가시 기존 근사매칭보다 16.2%의 정확도 향상을 보였다.