• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1136-1142
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• 2010

In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.57.4-57
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• 2002

$\textbullet$ Image segmentation is an essential technique of image analysis. In spite of the traditional issues in contour initialization and boundary concavities, active contour models(snakes) are popular and known as successful methods for segmentation. $\textbullet$ We could find in experiment that snake using Gaussian External Force is fast in time but low in accuracy and snake using Gradient Vector Flow by Chenyang Xu and Jerry L. Prince is high in accuracy but slow in time. $\textbullet$ In this paper, we presented a new active contour model, GGF snake, for segmentation of endoscopic image. Proposed GGF snake made up for the defects of the traditional snakes in contour initialization and boundary...

• Speech Sciences
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• v.9 no.4
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• pp.265-274
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• 2002

In this paper we propose an automatic segmentation system that outputs the time alignment information of phoneme boundary using Viterbi search with HMM (Hidden Markov Model) and corrects these results by an UVS (unvoiced/voiced/silence) classification algorithm. We selecte a set of 39 monophones and a set of 647 extended phones for HMM models. For the UVS classification we use the feature parameters such as ZCR (Zero Crossing Rate), log energy, spectral distribution. The result of forced alignment using the extended phone set is 11% better than that of the monophone set. The UVS classification algorithm shows high performance to correct the segmentation results.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.246-251
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• 1998

This paper proposes a new building segmentation method from 1m resolution imagery using an Active Contour Model, known as "Balloons". The original balloons, which was designed by Cohen(Cohen, 1991) to extract features from medical images, are modified for building segmentation. The proposed method consists of two phases. Firstly, building boundaries are extracted by balloons with a given position on buildings from an operator. Since balloons actively adjust their shapes according to the boundaries, there is no more shape limitations on detecting buildings. Secondly, buildings are segmented by connecting the corners detected from the building boundaries, because most buildings, which are man-made objects, are effectively described by polygons. The test results show that most buildings are segmented efficiently and easily. The proposed method is new and timely as 1m resolution spaceborne imagery will be available in the very near future. The proposed method can be used fur operational building segmentation from such imagery.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.16.2-21
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• 1998

Old movies are often corrupted by randomly located blotches and scratches. In this paper were present an efficient method for detection and removal of these distortions. The presented method is composed of two separate steps: the detection process and the restoration process. In the detection process, blotch locations are detected through global motion segmentation, the sequential approach to motion segmentation, a robust model-fit criterion and so on, we form the algorithm for the algorithm for the global motion segmentation tuned to the blotch detection problem. In the restoration process, the missing data of the detected blotch areas are temporally extrapolated from the corresponding image areas at the preceding or the succeeding image frame with considering the global motion segmentation results. We apply the presented method to moving image sequences distorted by artificial blotches. The method works very well and provides a subjective improvement of picture quality.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.27-31
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• 2003

This paper proposes a robust on-line signature verification system based on a new segmentation method and fusion scheme. The proposed segmentation method resolves the problem of segment-to-segment comparison where the variation between reference signature and input signature causes the errors in the location and the number of segments. In addition, the fusion scheme is adopted, which discriminates genuineness by calculating each feature vector's fuzzy membership degree yielded from the proposed segmentation method. Experimental results show that the proposed signature verification system has lower False Reject Rate(FRR) for genuine signature and False Accept Rate(FAR) for forgery signature.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.974-976
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• 1995

Segmentation of image data is an important problem in computer vision, remote sensing, and image analysis. Most objects in the real world have textured surfaces. Segmentation based on texture information is possible even if there are no apparent intensity edges between the different regions. There are many existing methods for texture segmentation and classification, based on different types of statistics that can be obtained from the gray-level images. In this paper, we use a neural network model --- ART-2 (Adaptive Resonance Theory) for textures in an image, proposed by Carpenter and Grossberg. In our experiments, we use Walsh matrix as feature value for textured image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.547-555
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• 2014

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3479-3492
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• 2022

Longitudinal quantification of brain changes due to development, aging or disease plays an important role in the filed of personalized-medicine applications. However, due to the temporal variability in shape and different imaging equipment and parameters, estimating anatomical changes in longitudinal studies is significantly challenging. In this paper, a longitudinal Magnetic Resonance(MR) brain image segmentation algorithm proposed by combining intensity information and anisotropic smoothness term which contain a spatial smoothness constraint and longitudinal consistent constraint into a variational framework. The minimization of the proposed energy functional is strictly and effectively derived from a fast optimization algorithm. A large number of experimental results show that the proposed method can guarantee segmentation accuracy and longitudinal consistency in both simulated and real longitudinal MR brain images for analysis of anatomical changes over time.