• Journal of KIISE:Computer Systems and Theory
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• v.34 no.12
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• pp.665-682
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• 2007

Line drawing is a widely used style in non-photorealistic rendering because it generates expressive descriptions of object shapes with a set of strokes. Although various techniques for line drawing of 3D objects have been developed, line drawing of 2D images has attracted little attention despite interesting applications, such as image stylization. This paper presents a robust and effective technique for generating line drawings from 2D images. The algorithm consists of three parts; filtering, linking, and stylization. In the filtering process, it constructs a likelihood function that estimates possible positions of lines in an image. In the linking process, line strokes are extracted from the likelihood function using clustering and graph search algorithms. In the stylization process, it generates various kinds of line drawings by applying curve fitting and texture mapping to the extracted line strokes. Experimental results demonstrate that the proposed technique can be applied to the various kinds of line drawings from 2D images with detail control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1120-1131
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• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.359-368
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• 2002

In this paper, we propose effective boundary line extraction algorithm for moving objects by matching error image and moving vectors, and fast tracking algorithm for moving object by partial boundary lines. We extracted boundary line for moving object by generating seeds with probability distribution function based on Watershed algorithm, and by extracting boundary line for moving objects through extending seeds, and then by using moving vectors. We processed tracking algorithm for moving object by using a part of boundary lines as features. We set up a part of every-direction boundary line for moving object as the initial feature vectors for moving objects. Then, we tracked moving object within current frames by using feature vector for the previous frames. As the result of the simulation for tracking moving object on the real images, we found that tracking processing of the proposed algorithm was simple due to tracking boundary line only for moving object as a feature, in contrast to the traditional tracking algorithm for active contour line that have varying processing cost with the length of boundary line. The operations was reduced about 39% as contrasted with the full search BMA. Tracking error was less than 4 pixel when the feature vector was $(15\times{5)}$ through the information of every-direction boundary line. The proposed algorithm just needed 200 times of search operation.

• Reproductive and Developmental Biology
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• v.30 no.1
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• pp.13-19
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• 2006

To search of new porcine pheromonal odorants for biostimulation control system technologies to improve reproductive efficiency in livestock species, the comparative molecular field analysis (CoMFA) for binding affinity constant $(p(Od)_{50})$ between porcine odorant binding protein (pOBP) and ligands of odorant 2-(cyclohexyloxy) tetrahydrofurane derivatives as substrate molecule was conducted and discussed. In the optimized CoMFA model AIV with chirality $(C_1'(R),\;C_2(S))$ in substrate molecule and atom based fit alignment (A) of odorants, the statistical results showed the best predictability of the binding affinities $(p(Od)_{50})$ based on the LOO cross-validated value $r^2_{cv}.\;(q^2=0.886)$ and non-cross-validated conventional coefficient $(r^2_{ncv}.=0.984)$. the binding affinity constants exhibited a good correlation with steric (40.8%), electrostatic (14.6%) and hydrophobic (44.6%) factors of the substrate molecules. from the analytical results of the contour maps, which may give us some valuable informations to the modification of odorants for effective binding affinity.

• Reproductive and Developmental Biology
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• v.30 no.3
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• pp.169-174
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• 2006

To search of a new porcine pheromonal odorants, the comparative molecular similarity indices analysis(CoMSIA) between porcine odorant binding protein(pOBP) as receptor and ligands of green odorants 2-(Cyclohexyloxy)tetrahydrofurane derivatives as substrate molecule were conducted and disscused quantitatively. In the optimized CoMSIA model(I-AI) with chirality($I:\;C_{1'}(R),\;C_2(S)$) in substrate molecules and atom based fit alignment(AE) of the odorants the statistical PLS results showed the best predictability of the binding affinities based on the LOO cross-validated value ${r^2}_{cv.}\;(q^2=0.856)$ and non cross-validated conventional coefficient(${r^2}_{ncv.}=0.964)$). The structural distinctions of the highest active molecules were able to understand from the interaction between pOBP and green odorants in the contour maps with CoMSIA model.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.49-55
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• 2017

Computed tomographic angiography (CTA) is widely used in the diagnosis and treatment of coronary artery disease because it shows not only the whole anatomical structure of the cardiovascular three-dimensionally but also provides information on the lesion and type of plaque. However, due to the large size of the image, there is a limitation in manually extracting coronary arteries, and related researches are performed to automatically extract coronary arteries accurately. As the coronary artery originate from the ascending aorta, the ascending aorta and ostium should be detected to extract the coronary tree accurately. In this paper, we propose an automatic segmentation for the ostium as a starting structure of coronary artery in CTA. First, the region of the ascending aorta is initially detected by using Hough circle transform based on the relative position and size of the ascending aorta. Second, the volume of interest is defined to reduce the search range based on the initial area. Third, the refined ascending aorta is segmented by using a two-dimensional geodesic active contour. Finally, the two ostia are detected within the region of the refined ascending aorta. For the evaluation of our method, we measured the Euclidean distance between the result and the ground truths annotated manually by medical experts in 20 CTA images. The experimental results showed that the ostia were accurately detected.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.539-548
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• 2001

In this paper, we propose that fast tracking algorithm for moving object is separated from background, using partial boundary line information. After detecting boundary line from input image, we track moving object by using the algorithm which takes boundary line information as feature of moving object. we extract moving vector on the imput image which has environmental variation, using high-performance BMA, and we extract moving object on the basis of moving vector. Next, we extract boundary line on the moving object as an initial feature-vector generating step for the moving object. Among those boundary lines, we consider a part of the boundary line in every direction as feature vector. And then, as a step for the moving object, we extract moving vector from feature vector generated under the information of the boundary line of the moving object on the previous frame, and we perform tracking moving object from the current frame. As a result, we show that the proposed algorithm using feature vector generated by each directional boundary line is simple tracking operation cost compared with the previous active contour tracking algorithm that changes processing time by boundary line size of moving object. The simulation for proposed algorithm shows that BMA operation is reduced about 39% in real image and tracking error is less than 2 pixel when the size of feature vector is [$10{\times}5$] using the information of each direction boundary line. Also the proposed algorithm just needs 200 times of search operation bout processing cost is varies by the size of boundary line on the previous algorithm.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.1-12
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• 2012

There are many trees in a roadside, parks or facilities for landscape. Although we are easily seeing a tree in around, it would be difficult to classify it and to get some information about it, such as its name, species and surroundings of the tree. To find them, you have to find the illustrated books for plants or search for them on internet. The important components of a tree are leaf, flower, bark, and so on. Generally we can classify the tree by its leaves. A leaf has the inherited features of the shape, vein, and so on. The shape is important role to decide what the tree is. And texture included in vein is also efficient feature to classify them. This paper evaluates the performance of a leaf classification system using both shape and texture features. We use Fourier descriptors for shape features, and both gray-level co-occurrence matrices and wavelets for texture features, and used combinations of such features for evaluation of images from the Flavia dataset. We compared the recognition rates and the precision-recall performances of these features. Various experiments showed that a combination of shape and texture gave better results for performance. The best came from the case of a combination of features of shape and texture with a flipped contour for a Fourier descriptor.