• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.291-298
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• 2012

In automatic target recognition(ATR) systems, target extraction techniques are very important because ATR performance depends on segmentation result. So, this paper proposes a multi-sensor image fusion method based on adaptive weights. To incorporate the FLIR image and CCD image, we used information such as the bi-modality, distance and texture. A weight of the FLIR image is derived from the bi-modality and distance measure. For the weight of CCD image, the information that the target's texture is more uniform than the background region is used. The proposed algorithm is applied to many images and its performance is compared with the segmentation result using the single image. Experimental results show that the proposed method has the accurate extraction performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.410-416
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• 2010

This paper presents the auto-extraction method that searches for the Mapping points in the calibration algorithm of the image acquired by the wide angle CCD camera. In this algorithm, we remove the noise from the distorted image and then obtain the edge image. Proposed method extracts the distortion point, comparing the threshold value of the histogram of the horizontal and vertical pixel lines in edge image. This processing step can be directly applied to the original image of the wide angle CCD camera output. Proposed method results are compared with hand-worked result image using the two wide angle CCD cameras having different angles with the difference value of the result images respectively. Experimental results show that proposed method can allocate the distortion-calibration constant of the wide angle CCD camera regardless of lens type, distortion shape and image type.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.10-10
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• 2003

This paper extracts the edge of main components of face with Gabor wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.1-8
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• 2011

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.33-45
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• 2011

In this paper, we present a method for extracting video objects efficiently by using the modified graph cut algorithm based on contour information. First, we extract objects at the first frame by an automatic object extraction algorithm or the user interaction. To estimate the objects' contours at the current frame, motion information of objects' contour in the previous frame is analyzed. Block-based histogram back-projection is conducted along the estimated contour point. Each color model of objects and background can be generated from back-projection images. The probabilities of links between neighboring pixels are decided by the logarithmic based distance transform map obtained from the estimated contour image. Energy of the graph is defined by predefined color models and logarithmic distance transform map. Finally, the object is extracted by minimizing the energy. Experimental results of various test images show that our algorithm works more accurately than other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.349-356
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• 2004

The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. In this paper we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers which are the target for recognition. In this paper we also propose and apply a novel ART2-based hybrid network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm performs better for extraction and recognition of container identifiers compared to conventional algorithms.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.174-183
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• 2014

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.141-150
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• 2009

This paper proposes a system that can automatically write singing voices to music notes. First, the system uses Stabilized Diffusion Equation(SIDE) to divide the song to a series of syllabic parts based on pitch detection. By the song segmentation, our method can recognize the sound length of each fragment through clustering based on genetic algorithm. Moreover, this study introduces a concept called 'Relative Interval' so as to recognize interval based on pitch of singer. And it also adopted measure extraction algorithm using pause data to implement the higher precision of song transcription. By the experiments using 16 nursery songs, it is shown that the measure recognition rate is 91.5% and DMOS score reaches 3.82. These findings demonstrate effectiveness of system performance.