• Journal of Digital Convergence
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• v.12 no.8
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• pp.337-343
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• 2014

This paper proposes a minimal contour tracking algorithm that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. This algorithm perform detection for object tracking and when it transmit image data to server from camera, it minimized communication load by reducing quantity of transmission data. This algorithm use minimal tracking area based on the kinematics of the object. The modeling of object's kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile object within scheduled time. In applications to detect an object in real time,when transmitting a large amount of image data it is possible to reduce the transmission load.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.295-302
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• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.237-244
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• 2002

In this paper, we propose a stereo image recognition-based method for measuring the volume of the rectangular parallelepiped. The method measures the volume from two images captured with two CCD (charge coupled device) cameras by sequential processes such as ROI (region of interest) extraction, feature extraction, and stereo matching-based vortex recognition. The proposed method makes it possible to measure the volume of the 3D object at high speed because only a few features are used in the process of stereo matching. From experimental results, it is demonstrated that this method is very effective for measuring the volume of the rectangular parallelepiped at high speed.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2960-2970
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• 1997

In a variety of fields, recently, there is a growing interest in Geographic Information System which facilitates efficient storage and retrieval of geographic information. It is of extreme importance to make a good choice of efficient input method, because it takes the most of the lime and cost in constructing a GIS. Among several steps, thinning input image to produce skeleton of unit width is prerequisite to the automatic input or geographic maps. In this paper, we systematically evaluate the performance of representative thinning algorithms in geographic maps such as contour, cadastral, and water and sewer maps, and suggest appropriate algorithms for the maps, respectively. A thorough experiment indicates that Arcelli's method is best for contour maps, Holt's method for cadastral maps, and Chen's method for water and sewer maps.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.577-586
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• 2005

Many algorithms have been proposed for (ace recognition that is one of the most successful applications in image processing, pattern recognition and computer vision fields. Research for what kind of attribute of face that make harder or easier recognizing the target is going on recently. In flus paper, we propose method to improve recognition performance using relevance of face data and applied algorithms, because recognition performance of each algorithm according to facial attribute(illumination and expression) is change. In the experiment, we use n-tuple classifier, PCA and Gabor wavelet as recognition algorithm. And we propose three vectorization methods. First of all, we estimate the fitnesses of three recognition algorithms about each cluster after clustering the test data using k-means algorithm then we compose new clusters by integrating clusters that select same algorithm. We estimate similarity about a new cluster of test data and then we recognize the target using the nearest cluster. As a result, we can observe that the recognition performance has improved than the performance by a single algorithm without clustering.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.562-573
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• 2002

In this paper, we propose a method for measuring the volume of packages for the efficient handling of the packages. Using the geometrical characteristics of the rectangular parallelepiped type objects, the method measures the volume of packages with one camera only in real time. In preprocessing of volume measurement, the method extracts outer lines of the object and then crossing points of the lines as feature points or vertexes. From these cross points(-feature points-), the volume of the package is calculated. Compared to the direct feature extraction, the proposed method shows especially the blurring robust result by using the line for feature extraction. Additionally, the method can get the stable result by considering object's direction. From experimental results, it is demonstrated that this method is very effective for the real time volume measurement of the rectangular parallelepiped.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.375-382
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• 2002

Handwritten character recognition is classified into on-line handwritten character recognition and off-line handwritten character recognition. On-line handwritten character recognition has made a remarkable outcome compared to off-line hacdwritten character recognition. This method can acquire the dynamic written information such as the writing order and the position of a stroke by means of pen-based electronic input device such as a tablet board. On the contrary, Any dynamic information can not be acquired in off-line handwritten character recognition since there are extreme overlapping between consonants and vowels, and heavily noisy images between strokes, which change the recognition performance with the result of the preprocessing. This paper proposes a method that effectively extracts the stroke including dynamic information of characters for off-line Korean handwritten character recognition. First of all, this method makes improvement and binarization of input handwritten character image as preprocessing procedure using watershed algorithm. The next procedure is extraction of skeleton by using the transformed Lu and Wang's thinning: algorithm, and segment pixel array is extracted by abstracting the feature point of the characters. Then, the vectorization is executed with a maximum permission error method. In the case that a few strokes are bound in a segment, a segment pixel array is divided with two or more segment vectors. In order to reconstruct the extracted segment vector with a complete stroke, the directional component of the vector is mortified by using right-hand writing coordinate system. With combination of segment vectors which are adjacent and can be combined, the reconstruction of complete stroke is made out which is suitable for character recognition. As experimentation, it is verified that the proposed method is suitable for handwritten Korean character recognition.