• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.299-308
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• 2009

AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3454-3459
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• 2013

Sumi-e is one of the art work that uses not only ink line but also color painting. This technique is well known as a representative Asian painting style and widely used in movie, advertisement poster and various effect in camera device. In this paper, we propose an algorithm that can generate result image with Sumi-e effects of example image based on computer graphics and image processing techniques. For this, we pass two steps. The first is painting expression step. We used texture transfer technique to generate result with texture effect of reference image by analyzing numerically. The second step is ink-painting effect generation step. We express ink-painting effect in outline by considering intensity variation in edge of example image. Our algorithm can express various Sumi-e style based on selected reference image. So it can be utilized to various contents generating research.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.103-106
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• 2001

본 논문에서는 MPEG2비디오 스트림에서 직접 얻을 수 있는 정보들을 활용하여 카메라의 움직임을 추정하여 이를 기반으로 하여 움직이는 객체를 추정하고자 한다. 이를 위해, 먼저 MPEG2의 움직임 벡터는 압축의 효율성 때문에 움직임의 예측이 순서적이지 못한데, 예측 프레임들의 속성을 이용하여 이를 광 플로우(Optical Flow)를 갖는 움직임 벡터(Motion Vector)로 변환하였다. 그리고 이러한 벡터들을 이용하여 카메라의 기본적인 움직임인 팬(Fan), 틸트(Tilt). 줌(Zoom) 등을 정의하였다. 이를 위하여 팬, 틸트-줌 카메라 모델의 매개변수와 같은 의미의 $\Delta$x, $\Delta$y, $\alpha$값을 정의하고자 움직임 벡터 성분의 Hough변환을 이용하여 $\Delta$x, $\Delta$y, $\alpha$값들을 구하였다. 또한 이러한 카메라 움직임(Camera Operation)은 시간적으로 연속적으로 발생하는 특징을 이용하여 각 프레임마다 구한 카메라의 움직임을 보정하였다. 마지막으로 움직이는 객체의 추정은 우선 사용자가 원하는 객체를 바운딩박스 형태로 정의한 후 카메라 움직임이 보정된 객체의 움직임 벡터를 한 GOF(Group of Pictures) 단위로 면적 기여도에 따라 누적하여 객체를 추적하고 해석하였으며 DCT 질감 정보를 이용하여 객체의 영역을 재설정 하였다. 물론 압축된 MFEG2비디오에서 얻을 수 있는 정보들은 최대 블록 단위이므로 객체의 정의도 블록단위 이상의 객체로 제한하였다. 제안된 방법은 비디오 스트림에서 직접 정보를 얻음으로써 계산속도의 향상은 물론 카메라의 움직임특성과 움직이는 객체의 추적들을 활용하여 기존의 내용기반의 검색 및 분석에도 많이 응용될 수 있다. 이러한 개발 기술들은 압축된 데이터의 검색 및 분석에 유용하게 사용되리라고 기대되며 , 특히 검색 툴이나 비디오 편집 툴 또는 교통량 감시 시스템, 혹은 무인 감시시스템 등에서 압축된 영상의 저장과 빠른 분석을 요구시 필요하리라고 기대된다.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.937-946
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• 1995

We proposed and efficient block segmentation and classification method for the document analysis using SGLDM(spatial gray level dependence matrix) and BP (back Propagation) neural network. Seven texture features are extracted directly from the SGLDM of each gray-level block image, and by using the nonlinear classifier of neural network BP, we can classify document blocks into 9 categories. The proposed method classifies the equation block, the table block and the flow chart block, which are mostly composed of the characters, out of the blocks that are conventionally classified as non-character blocks. By applying Sobel operator on the gray-level document image beforebinarization, we can reduce the effect of the background noises, and by using the additional horizontal-vertical smoothing as well as the vertical-horizontal smoothing of images, we can obtain an effective block segmentation that does not lead to the segmentation into small pieces. The result of experiment shows that a document can be segmented and classified into the character blocks of large fonts, small fonts, the character recognigible candidates of tables, flow charts, equations, and the non-character blocks of photos, figures, and graphs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• Korean Journal of Food Science and Technology
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• v.42 no.2
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• pp.165-174
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• 2010

The effects of substituting whey protein isolate (WPI) for butter in the preparation of butter sponge cake were determined by objective and subjective tests. The specific gravity of cake batter, the cooking loss and moisture content of cake were all decreased with increasing amounts of WPI, whereas specific loaf volume was increased. With increasing WPI content, redness of crust and crumb, as well as lightness of crumb were increased, whereas lightness of crust, as well as yellowness of crust and crumb were all decreased. Hardness, chewiness, gumminess, adhesiveness and fracturability were increased significantly with increasing amounts of WPI, however, cohesiveness, springiness, and resilience were decreased. In the sensory evaluation, 20% WPI-substituted cake displayed scores similar to those of control. These results suggest that substitution of 20% WPI could be the best ratio for the preparation of butter sponge cake.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.6
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• pp.1022-1031
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• 2008

Pound cakes were prepared with Citrus mandarin powder(CMP) cultivated in JeJu Island, Korea. The impact of CMP amount level, which was incorporated into wheat flour by the ration of 0, 5, 10, 15, and 20% based on a flour weight, on the rheology and sensory profile of the pound cakes was measured. Moisture content of 13.70%, crude protein 5.12%, crude lipid 1.30%, crud ash 1.92%, respectively. Also evaluation was performed on the changes in physicochemical properties of the pound cakes during storage at 4 and $30^{\circ}C$. According to the amylogram, gelatinization temperature of the control dough was $63.35^{\circ}C$ and those of the dough with CMP were $63.85{\sim}66.55^{\circ}C$. Maximum viscosity of the dough was 686 B.U in the control, those were 575 B.U, 553 B.U, 504 B.U and 401 B.U in the dough with 5, 10, 15, and 20% CMP, respectively. The retrogradation degree(setback value) of CMP dough was $31{\sim}57%$ lower than that of the control dough under the same conditions. Water holding capacity of pound cake was increased gradually in proportion to the amount of CMP. The CMP addition decreased the brightuess(L) of pound cakes but increased redness(a) and yellowness(b). Hardness of pound cakes was significantly increased by CMP addition, while springiness, adhesiveness and cohesiveness were decreased. Based on sensory evaluation, pound cakes added with CMP were not significantly different in color and texture, while that of 10% CMP was significantly high in taste, flavor, and overall preferences, compared to the control. pH of pound cake with CMP was decreased during storage, showing that pH of CMP samples was lower than the control. Titrated acidity of pound cake with CMP was increased rapidly from storage for 10 days, which the changes in degree was fast in accordance with CMP amount. The Hunter's color value of pound cake with CMP was decreased, as the storage time proceeded. In the samples prepared with CMP, the firmness, adhesiveness, gumminess and chewiness was increased as the storage time proceeded, while springiness and cohesiveness was decreased.