• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1790-1806
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• 2016

Digital image encryption is widely used for image data security. JPEG standard compresses image with great performance on reducing file size. Thus, to encrypt an image in JPEG format we should keep the quality of original image and reduced size. This paper proposes a JPEG image encryption scheme based on quantized DC and non-zero AC coefficients inner category scrambling. Instead of coefficient value encryption, the address of coefficient is encrypted to get the address of cipher text. Then 8*8 blocks are shuffled. Chaotic iteration is employed to generate chaotic sequences for address scrambling and block shuffling. Analysis of simulation shows the proposed scheme is resistant to common attacks. Moreover, the proposed method keeps the file size of the encrypted image in an acceptable range compared with the plain text. To enlarge the cipher text possible space and improve the resistance to sophisticated attacks, several additional procedures are further developed. Contrast experiments verify these procedures can refine the proposed scheme and achieve significant improvements.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• 전자공학회논문지B
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• 제33B권10호
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• pp.120-129
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• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

• 전자공학회논문지B
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• 제28B권5호
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• pp.327-334
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• 1991

This paper proposes an efficient method of implementing the generalized Hough transform (GHT), which has been hindered by an excessive computing load and a large memory requirement. The conventional algorithm requires a parameter space of 4 dimensions in detection a rotated, scaled, and translated object in an input image. Prior to the application of GHT to the input image, the proposed method determines the angle of rotation and the scaling factor of the test image using the proportion of the edge components between the reference image and test image. With the rotation angle and the scaling factor already determined, the parameter spaceis to be reduced to a simple array of 2 dimensions by applying the unit GHT only one time. The experiments with the image of airplanes reveal that both of the computing time and the requires memory size are reduced by 95 percent, without any degradatationof accuracy, compared with the conventional GHT algorithm.

• JSTS:Journal of Semiconductor Technology and Science
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• 제13권2호
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• pp.157-169
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• 2013

Scale Invariant Feature Transform (SIFT) generates image features widely used to match objects in different images. Previous work on hardware-based SIFT implementation requires excessive internal memory and hardware logic [1]. In this paper, a new hardware organization is proposed to implement SIFT with less memory and hardware cost than the previous work. To this end, a parallel Gaussian filter bank is adopted to eliminate the buffers that store intermediate results because parallel operations allow all intermediate results available at the same time. Furthermore, the processing order is changed from the raster-scan order to the block-by-block order so that the line buffer size storing the source image is also reduced. These techniques trade the reduction of memory size with a slight increase of the execution time and external memory bandwidth. As a result, the memory size is reduced by 94.4%. The proposed hardware for SIFT implementation includes the Descriptor generation block, which is omitted in the previous work [1]. The addition of the hardwired descriptor generation improves the computation speed by about 30 times when compared with the previous work.

• 제어로봇시스템학회논문지
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• 제15권2호
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• pp.225-230
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• 2009

We propose a template matching method for component inspection of SMD assembly system. To discriminate wrong assembled components, the input image of component is matched with its standard image by template matching algorithm. For a fast inspection system, the calculation time of matching algorithm should be reduced. Since the standard images of all components located in a PCB are stored in computer, it is desirable to reduce the memory size of standard image. We apply the discrete wavelet transformation to reduce the image size as well as the calculation time. Only 7% memory of the BMP image is used to discriminate goodness or badness of components assembly. Comparative results are presented to verify the usefulness of the proposed method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.731-734
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• 1998

A scalable parallel algorithm is proposed for efficient image component labeling with local operatos on a mesh connected SIMD computer. In contrast to the conventional parallel labeling algorithms, where a single pixel is assigned to each PE, the algorithm presented here is scalable and can assign m$\times$m pixel set to each PE according to the input image size. The assigned pixel set is converted to a single pixel that has representative value, and the amount of the required memory and processing time can be highly reduced. For N$\times$N image, if m$\times$m pixel set is assigned to each PE of P$\times$P mesh, where P=N/m, the time complexity due to the communication of each PE and the computation complexity are reduced to O(PlogP) bit operations and O(P) bit operations, respectively, which is 1/m of each of the conventional method. This method also diminishes the amount of memory in each PE to O(P), and can decrease the number of PE to O(P2) =Θ(N2/m2) as compared to O(N2) of conventional method. Because the proposed parallel labeling algorithm is scalable, we can adapt to the increase of image size without the hardware change of the given mesh connected SIMD computer.

• 방송공학회논문지
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• 제21권2호
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• pp.252-260
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• 2016

사람 검출의 성능은 카메라의 위치 및 각도 등에 큰 영향을 받는다. 이로 인해 획득한 2D 영상에서 사람은 위치에 따라 각기 다른 크기를 갖는 형태로 나타난다. 이렇게 다양한 크기를 갖는 사람들을 모두 검출하는 것은 실시간 시스템의 구현을 어렵게 만드는 요인이 된다. 그러나 만일 영상의 특정 위치의 사람의 크기를 예측할 수 있다면, 해당 위치의 사람 검출을 위한 연산량이 크게 감소될 수 있을 것이다. 본 논문에서는 실내 공간의 구조를 깊이맵으로 구성하고, 실내 공간에 존재하는 사람의 영상을 3D 공간에 재구성함으로써 크기를 예측하는 기법을 제안한다. 3D 공간에서는 어느 위치에서든지 사람의 크기가 일관되므로 이를 2D 영상으로 투영하게 되면 2D 영상의 좌표에 따른 정확한 사람의 크기를 추정할 수 있다. 실험 결과로부터 제안 방법이 효과적으로 사람의 크기를 예측할 수 있고, 기존이 기계학습 기반 사람 검출 방법들의 처리속도가 감소됨을 증명하였다.

• 한국방사선학회논문지
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• 제12권1호
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• pp.85-91
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• 2018

전산화단층촬영기법은 투영 영상을 재구성하여 단면 영상을 획득하는 기법으로 다양한 분야에 적용되고 있다. 재구성된 영상의 공간분해능은 장치, 대상, 재구성 과정에 의존한다. 본 논문은 평행빔 구조에서 투영 영상의 개수 및 검출기의 픽셀 크기가 재구성된 영상의 공간분해능에 미치는 영향을 조사하였다. 재구성 프로그램은 Visual C++로 작성하였으며 단면 영상은 $512{\times}512$ 크기로 하였다. 공간분해능의 특성을 평가하기 위해 수학적 막대 팬텀을 구성하였고, Min-Max 방법을 도입하였다. 재구성에 사용되는 투영의 개수가 작은 경우 허상이 나타났으며 Min-Max도 낮았다. 투영의 개수를 지속적으로 증가시키면 재구성된 영상의 공간분해능을 나타내는 Min-Max는 상향 포화되었다. 검출기의 픽셀 크기를 재구성되는 단면 영상의 픽셀 크기의 50%로 줄이면 영상은 거의 완벽하게 복원되고, 검출기픽셀 크기가 증가할수록 Min-Max는 감소하였다. 본 연구는 CT장치 설계 시 요구되는 공간분해능을 달성하기 위해 필요한 검출기 및 회전 스테이지의 정밀도를 결정하는데 도움이 될 것이다.