• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.93-98
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• 1999

본 논문에서는 영상 내 존재하는 의미 있는 객체단위로 초상권을 보호하는 기법을 제안한다. 제안된 방법은 초상권 보호 객체선택 단계와 객체에 마스크를 적용하는 단계 그리고 마스크가 적용된 객체를 추적하는 단계로 나누어진다. 초상권 보호 객체선택 단계에서는 블록분류(block classification) 및 워터쉐드(watershed) 알고리듬을 이용하여 분할된 결과영상을 얻고 이를 이용하여 사용자가 원하는 객체를 마우스로 클릭함으로써 손쉽게 초상권 보호법을 적용시킬 객체를 추출할 수 있다. 이렇게 정의된 객체는 다음 단계에서 마스크를 적용 받게 된다. 첫 번째 프레임에서 마스크가 적용되면 다음 프레임부터는 객체추적과정에서 연된 화면사이의 움직임 및 밝기정보에 의해 객체를 추적, 계속 마스크를 적용함으로써 초상권을 보호할 수 있다. 제안된 알고리듬은 초상권 보호를 위한 모자이크 처리 시 화질 저하에 따른 시청자의 화면 거부감을 최소화시키고, 반자동영상분할 알고리듬을 사용하여 객체 단위로 초상권 마스크를 적용하여 초상권 보호대상물을 놓치지 않고 추적할 수 있어 신뢰도를 높일 수 있는 장점을 가지고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.57-60
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• 2002

본 논문에서는 능동윤곽모델(active contour model)의 잘 알려져 있는 스네이크(snake) 알고리즘을 스테레오영상에 적용하여 좌 우 영상의 disparity 정보를 이용 객체의 경계선을 찾는 알고리즘을 제안한다. 스네이크는 객체의 경계를 얻기 위해 에지정보를 사용하는데 실제 이미지에서 객체의 경계가 아닌 인접한 주위의 강한 애지(edge)에 대해서도 영향을 받게 되는 문제가 있다. 이러한 문제를 해결하기 위해 스테레오영상의 disparity 정보를 이용하여 이를 개선하고 disparity 측정에 사용되는 블록매칭(block matching)방법을 스네이크 알고리즘에 적용시켰다.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1357-1367
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• 2013

Recently, as one of images based methods in facial expression recognition, the research which used ULBP block histogram feature and SVM classifier was performed. Due to the properties of LBP introduced by Ojala, such as highly distinction capability, durability to the illumination changes and simple operation, LBP is widely used in the field of image recognition. In this paper, we combined $LBP_{8,2}$ and $LBP_{8,1}$ to describe micro features in addition to shift, size change in calculating ULBP block histogram. From sub-windows of 660 of $LBP_{8,1}$ and 550 of $LBP_{8,2}$, ULBP histogram feature of 1210 were extracted and weak classifiers of 50 were generated using AdaBoost. By using the combined $LBP_{8,1}$ and $LBP_{8,2}$ hybrid type of ULBP histogram feature and SVM classifier, facial expression recognition rate could be improved and it was confirmed through various experiments. Facial expression recognition rate of 96.3% by hybrid boosted ULBP block histogram showed the superiority of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.81-88
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• 2004

In this paper, we propose a new objective quality assessment method considering the human visual perception characteristics. A subjective quality assessment is obtained by the response of the receptive field in the primary visual cortex and a human's eye can't focus on all of the visual range in a moment. Take advantage of two facts above, we apply Gabor wavelet transform which is well fit the receptive field in the cortex, to divided constant sized subblocks. Then a local distortion of the subblocks and a global distortion for the entire image are calculated in order. The proposed method has been evaluated using video test sequences provided by the Video Quality Experts Group (VQEG). The experimental results show that good correlation with human perception is obtained using the proposed metric, which is what we called GPSNR.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.868-876
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• 1999

In this paper, an efficient automatic video parsing technique on MPEG-compressed video that is fundamental for content-based indexing is described. The proposed method detects scene changes, regardless of IPB picture composition. To detect abrupt changes, the difference measure based on the dc coefficient in I picture and the macroblock reference feature in P and B pictures are utilized. For gradual scene changes, we use the macroblock reference information in P and B pictures. the process of scene change detection can be efficiently handled by extracting necessary data without full decoding of MPEG sequence. The performance of the proposed algorithm is analyzed based on precision and recall. the experimental results verified the effectiveness of the method for detecting scene changes of various MPEG sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1703-1710
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• 2017

This paper propose a 3D point clouds (depth) security technique for digital holographic display service. Image contents encryption is a method to provide only authorized right owners with the original image information by encrypting the entire image or a part of the image. The proposed method detected an edge from a depth and performed quad tree decomposition, and then performed encryption. And encrypts the most significant block among the divided blocks. The encryption effect was evaluated numerically and visually. The experimental results showed that encrypting only 0.43% of the entire data was enough to hide the constants of the original depth. By analyzing the encryption amount and the visual characteristics, we verified a relationship between the threshold for detecting an edge-map. As the threshold for detecting an edge increased, the encryption ratio decreased with respect to the encryption amount.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.57-62
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• 1999

Estimation of the point spread function (PSF) is one of the main research topic of image processing, because it determines the performance of the auto-focusing system. In this paper, a new algorithm for PSF estimation is proposed, and its application to image restoration is also presented. The procedure for complete realization of the auto-focusing system consists of two steps: PSF estimation based on edge classification, and image restoration using the estimated PSF. More specifically, we divide imput image into multiple small image or block, estimate unit step response and average them on the blocks which contain edge, and estimate 2-dimensional isotropic PSF from the 1 dimensional step response. Finally we obtain in-focused image by using image restoration based on the estimated PSF.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1323-1331
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• 2017

Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.

• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1085-1092
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• 2002

Studies on image information processing have been performed since long time ago because in daily life most of information are acquired by the since of sight. Since there should be a lot of data to describe image as a digital form, data compression is required in order to store or transmit digital image. Lately among most of image compression methods adopted on image compression standards, transform coding methods have been primarily used which transforms the correlations between pixels of image on frequency domain before image compression. It is blown that the standard methods using especially DCT features blocking effect which is the major cause of degrading the quality of image at high compression rate. Fractal encoding using quadtree partition is applied after reducing original image, and we are to find a optimal encoding for the number of scaling bit and offset bit.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.113-121
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• 2012

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.