• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.403-408
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• 2003

A general meaning of caricaturing is that a representation, especially pictorial or literary, in which the subject's distinctive features or peculiarities are deliberately exaggerated to produce a comic or grotesque effect. In other words, a caricature is defined as a rough sketch(dessin) which is made by detecting features from human face and exaggerating or warping those. There have been developed many methods which can make a caricature image from human face using computer. In this paper, we propose a new caricaturing system. The system uses a real-time image or supplied image as an input image and deals with it on four processing steps and then creates a caricatured image finally. The four Processing steps are like that. The first step is detecting a face from input image. The second step is extracting special coordinate values as facial geometric information. The third step is deforming the face image using local warping method and the coordinate values acquired in the second step. In fourth step, the system transforms the deformed image into the better improved edge image using a fuzzy Sobel method and then creates a caricatured image finally. In this paper , we can realize a caricaturing system which is simpler than any other exiting systems in ways that create a caricatured image and does not need complex algorithms using many image processing methods like image recognition, transformation and edge detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5169-5175
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• 2013

We present a framework for 3D hand gesture design and modeling. We adapted two different pattern matching techniques, Dynamic Time Warping (DTW) and Hidden Markov Models (HMMs), to support the registration and evaluation of 3D hand gestures as well as their recognition. One key ingredient of our framework is a concept for the convenient gesture design and registration using HMMs. DTW is used to recognize hand gestures with a limited training data, and evaluate how the performed gesture is similar to its template gesture. We facilitate the use of visual sensors and body sensors for capturing both locative and inertial gesture information. In our experimental evaluation, we designed 18 example hand gestures and analyzed the performance of recognition methods and gesture features under various conditions. We discuss the variability between users in gesture performance.

• The KIPS Transactions:PartA
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• v.17A no.4
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• pp.189-196
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• 2010

This paper presents an improved marker detection algorithm using hybrid features such as corner, line segment, region, and adaptive threshold values, etc. In usual augmented reality environments, there are often marker occlusion and poor illumination. However, existing ARToolkit fails to recognize the marker in these situations, especially, partial concealment of marker by user, large change of illumination and dim circumstances. In order to solve these problems, the adaptive threshold technique is adopted to extract a marker region and a corner extraction method based on line segments is presented against marker occlusions. In addition, a compensating method, corresponding the marker size and center between registered and extracted one, is proposed to increase the template matching efficiency, because the inside marker size of warped images is slightly distorted due to the movement of corner and warping. Therefore, experimental results showed that the proposed algorithm can robustly detect the marker in severe illumination change and occlusion environment and use similar markers because the matching efficiency was increased almost 30%.