• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.10
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• pp.2446-2456
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• 1993

By virtue of progress of computer science, CAD/CAM technology has been developed greatly in each area. But the problems in the integration of CAD/CAM are not yet solved completely. The reason is that the exchange of data between CAD and CAM is difficult because the domains of design and manufacturing are different in nature. To solve this problem, a feature based modeller is developed in this study, which makes it possible to communicate between design and manufacturing through features. The modeller has feature, the concept of semi-bounded plane is introduced, and implemented as a B-rep sheet model using half-edge data structure. The features are then created on a part by local modification of the boundary on a part based on feature template information. This approach generalizes the modelling of features in a geometry model.

• Korean Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.274-282
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• 1998

A feature is a local shape of a product directly related to the manufacturing process. The feature plays a role of the bridge connecting CAD and CAM. In the process planning for he CAM, information on manufacturing is required. To get the a manufacturing information from CAD dat, we need to recognize features. Once features are recognized, they are used as an input for the process planning. In this paper, we thoroughly investigate the composite features, which are generated by interacting simple features. The simple features in the composite feature usually have precedence relation in terms of process sequence. Based on the reason for the precedence relation, we classify the composite features for the process planning. In addition to the precedence relation, approach direction is used as an input for the process planning. In the process planning, the number of set-up orientations are minimized whole process sequence for the features are generated. We propose a process planning algorithm based on the topological sort and breadth-first search of graphs. The algorithn is verified using sample products.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1372-1381
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• 2017

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.178-186
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• 2011

Static CAD models are the CAD models that do not have feature information and modeling history. These static models are generated by translating CAD models in a specific CAD system into neutral formats such as STEP and IGES. When a CAD model is translated into a neutral format, its precious feature information such as feature parameters and modeling history is lost. Once the feature information is lost, the advantage of feature based modeling is not valid any longer, and modification for the model is purely dependent on geometric and topological manipulations. However, the capabilities of the existing methods to modify static CAD models are limited, Direct modification methods such as tweaking can only handle the modifications that do not involve topological changes. There was also an approach to modify static CAD model by using volume decomposition. However, this approach was also limited to modifications of protrusion features. To address this problem, we extend the volume decomposition approach to handle not only protrusion features but also depression features in a static CAD model. This method first generates the model that contains the volume of depression feature using the bounding box of a static CAD model. The difference between the model and the bounding box is selectively decomposed into so called the feature volume and the base volume. A modification of depression feature is achieved by manipulating the feature volume of the static CAD model.

• Korean Journal of Computational Design and Engineering
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• v.9 no.1
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• pp.51-61
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes.. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the second one of the two companion papers, describes the similarity assessment method using NFD.

• Korean Journal of Computational Design and Engineering
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• v.9 no.1
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• pp.44-50
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the first one of the two companion papers, describes the similarity assessment methods using convex decomposition and FFD.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.58-64
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• 2001

Recently many informations are transmitted ,md stored as video data, and they are on the rapid increase because of popularization of high performance computer and internet. In this paper, to retrieve video data, shots are found through analysis of video stream and the method of detection of key frame is studied. Finally users can retrieve the video efficiently. This Paper suggests a new feature that is robust to object movement in a shot and is not sensitive to change of color in boundary detection of shots, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc,). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not de image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that arc similar to user's query image arc retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.370-380
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• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.103-114
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• 2005

This paper presents a content-based image retrieval (CBIR) method using the combination of color and texture features. As a color feature, a color autocorrelogram is chosen which is extracted from the hue and saturation components of a color image. As a texture feature, BDIP(block difference of inverse probabilities) and BVLC(block variation of local correlation coefficients) are chosen which are extracted from the value component. When the features are extracted, the color autocorrelogram and the BVLC are simplified in consideration of their calculation complexity. After the feature extraction, vector components of these features are efficiently quantized in consideration of their storage space. Experiments for Corel and VisTex DBs show that the proposed retrieval method yields 9.5% maximum precision gain over the method using only the color autucorrelogram and 4.0% over the BDIP-BVLC. Also, the proposed method yields 12.6%, 14.6%, and 27.9% maximum precision gains over the methods using wavelet moments, CSD, and color histogram, respectively.