• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.87-95
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• 1996

A new design scheme for the fuzzy pattern matching classifier (FPMC) is proposed. in conventional design of FPMC, there are no exact information about the membership function of which shape and number critically affect the performance of classifier. So far, a trial and error or heuristic method is used to find membership functions for the input patterns. But each of them have limits in its application to the various types of pattern recognition problem. In this paper, a new method to find the appropriate shape and number of membership functions for the input patterns which minimize classification error is proposed using genetic algorithms(GAs). Genetic algorithms belong to a class of stochastic algorithms based on biological models of evolution. They have been applied to many function optimization problems and shown to find optimal or near optimal solutions. In this paper, GAs are used to find the appropriate shape and number of membership functions based on fitness function which is inversely proportional to classification error. The strings in GAs determine the membership functions and recognition results using these membership functions affect reproduction of next generation in GAs. The proposed design scheme is applied to the several patterns such as tire tread patterns and handwritten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.3
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• pp.299-304
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• 2021

Purpose. The purpose of the present study was to investigate the effects of image matching experience of operators on the accuracy and working time of image registration between radiographic and optical scan images. Materials and methods. Computed tomography and optical scan of a dentate dental arch were obtained. Image matching between the computed tomography and the optical scan (IDC S1, Amann Girrbach, Koblah, Austria) was performed using the point-based automatic registration method in planning software programs (Implant Studio, 3Shape, Copenhagen, Denmark) using two different experience conditions on image registration: experienced group and inexperienced group (n = 15 per group, N = 30). The accuracy of image registration in each group was evaluated by measuring linear discrepancies between matched images, and working time was recorded. Independent t test was used to statistically analyze the result data (α = .05). Results. In the linear deviation, no statistically significant difference was found between the experienced and inexperienced groups. Meanwhile, the working time for image registration was significantly shorter in the experienced group than in the inexperienced group (P = .007). Conclusion. Difference in the image matching experience may not influence the accuracy of image registration of optical scan to computed tomography when the point-based automatic registration was used, but affect the working time for the image registration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.278-286
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for large screen and full HD(high definition) display. Conventionally, block matching algorithms (BMA) are widely used to do motion estimation for simplicity of implementation. However, there are still several drawbacks. So in this paper, we propose a novel shape-based ME algorithm to increase accuracy and reduce ME computational cost. To increase ME accuracy, we do motion estimation based on shape of moving objects. And only moving areas are included for motion estimation to reduce computational cost. The results show that the computational cost is 25 % lower than full search BMA, while the performance is similar or is better, especially in the fast moving region.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.55-62
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• 2004

A semi-automatic registration process of determining specified points is presented, which is required to register brain MR images based on Talairach atlas. Generally, ten specified points that define Talairach coordinates are anterior commissure(AC), posterior commissure (PC), anterior feint (AP), posterior point (PP), superior point (SP), inferior point (IP), left point (LP), right point (RP) and two points for the midline of the brain. The suggested method reduces user interaction for S points, and finds the necessary points for registration in a more stable manner by finding AC and PC using two-level shape matching of the corpus callosum (CC) in an edge-enhanced brain M image. Remaining points are found using the intensity information of cutview.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.471-480
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• 2002

In this paper, we implemented a real-time system improving the performance of the paper currency discrimination by integrating a weighted region of interest matching algorithm with a weighted shape feature matching algorithm of the blocked image. The system classifies the paper currency by comparing a query image with compared images based on the database that contain images of paper currency. Especially, the system has good efficiency at the contaminated, rotated, and translated paper currency. The system hardware consists of three parts as follows : the paper currency image acquired by CIS(contact image sensor) is applied to the pre-processing part with A/D converter and PLD. Finally the pre-processed image data are classified by the main image processing part with a high-speed DSP based on the proposed algorithm.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.22-33
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• 2005

In this paper. we propose an image registration algorithm based on variable-sized blocks of ellipsoidal prototype model which is similar in shape to human face. While matching blocks, the existing cylindrical prototype model which only takes into account left and right curvature can accomplish a correct alignment on left and right images. But, registration errors are produced from up and down images because the cylindrical prototype model not reflects characteristics of head shape and jaw structure of human. The proposed method is a block matching algorithm which uses variable-sized blocks with considering left-right and up-down curvature of ellipsoidal face model and can correctly align images by using the correlation between them. We then adapt image mosaic technique to generate a face texture from aligned images. For this purpose, we stitch them with assigning linear weights according to the overlapped region and remove ghost effects to make more realistic facial texture.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.430-433
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• 1996

The shape of an object plays a very important role in pattern analysis and classification. Roughly, the researches on this topic can be classified into three fields, i.e. (i) edge detection, (ii) dominant points extraction, and (iii) shape recognition and classification. Many works have been done in these three fields. However, it is very seldom to see the research that discusses the connection relationship of objects. This problem is very important in robot assembly systems. Therefore, here we focus on this problem and discuss how to recover the connection relationship of planar objects. Our method is based on the partial curve identification algorithm. The experiment results show the efficiency and validity of this method.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.172-175
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• 2002

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.73-82
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• 1996

In this paper, an efficient algorithm for recognizing and positioning occuluded objects in a two-dimensional plane is presented. Model objects and unknown input image are approximated by polygonal boundaries, which are compactly represented by shape functions of the polygons. The input image is partitioned into measningful segments whose end points are at the locations of possible occlusion - i.e. at concave vertices. Each segment is matched against known model objects by calculating a matching measure, which is defined as the minimum euclidean distance between the shape functions. An O(mm(n+m) algorithm for computing the measure is presentd, where n and m are the number of veritces for a model and an unknown object, respectively. Match results from aprtial segments are combined based on mutual compatibility, then are verified using distance transformation and translation vector to produce the final recognition. The proposed algorithm is invariant under translation and rotation of objects, which has been shown by experimental results.