• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.327-332
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• 2002

The main problems of computational complexity in object tracking are definition of objects, segmentations and identifications in non-structured environments with erratic movements and collisions of objects. The object's information as a region that corresponds to objects without discriminating among objects are considered. This paper describes the algorithm that, automatically and efficiently, recognizes and keeps tracks of interest-regions selected by users in video or camera image sequences. The block-based feature matching method is used for the region tracking. This matching process considers only dominant feature points such as corners and curved-edges without requiring a pre-defined model of objects. Experimental results show that the proposed method provides above 96% precision for correct region matching and real-time process even when the objects undergo scaling and 3-dimen-sional movements In successive image sequences.

• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.262-270
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• 2002

Although most image indexing schemes ate based on global image features, they have limited discrimination capability because they cannot capture local variations of the image. In this paper, we propose a new region-based image retrieval system that can extract important regions in the image using salient point extraction and image segmentation techniques. Our experimental results show that color and texture information in the region provide a significantly improved retrieval performances compared to the global feature extraction methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1591-1602
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• 1995

A hybrid approach which includes edge- and region-based methods is considered. The modified non-linear Laplacian(MNL) filter is used for feature extraction. The matching algorithm has three steps which are edge, signed region, and residual region matching. At first, the edge points are matched using the sign and direction of edges. Then, the disparity is propagated from edge to inside region. A variable window is used to consider the local method which give accurate matched points and area-based method which can obtain full-resolution disparity map. In addition, a new relaxation algorithm for considering matching possibility derived from normalized error and regional continuity constraint is proposed to reduce the mismatched points. By the result of simulation for various images, this algorithm is insensitive to noise and gives full- resolution disparity map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.117-134
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• 2002

This paper presents a computational method for a confidence region of identified parameters which are affected by measurement noise and error contained in prescribed parameters. The method is based on sensitivities of the identified parameters with respect to model parameter error and measurement noise along with the law of error propagation. By conducting numerical experiments on simple models, it is confirmed that the confidence region coincides well with the results of numerical experiments. Furthermore, the optimum arrangement of sensor locations is evaluated when uncertainty exists in prescribed parameters, based on the concept that square sum of coefficients of variations of identified results attains minimum. Good agreement of the theoretical results with those of numerical simulation confirmed validity of the theory.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.447-451
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• 2010

In this paper, we propose a simple but effective algorithm to increase the speed of Emphysema region classification. Emphysema region classification method based on CT image consumes a lot of time because of the large number of subregions due to the large size of CT image. Some of the sub-regions contain no Emphysema and the classification of these regions is worthless. To speed up the classification process, we create an algorithm to select Emphysema region candidates and only use these candidates in the Emphysema region classification instead of all of the sub-regions. First, the lung region is detected. Then we threshold the lung region and only select the dark pixels because Emphysema only appeared in the dark area of the CT image. Then the thresholded pixels are clustered into a region that called the Emphysema pre-detected region or Emphysema region candidate. This region is then divided into sub-region for the Emphysema region classification. The experimental result shows that Emphysema region classification using predetected Emphysema region decreases the size of lung region which will result in about 84.51% of time reduction in Emphysema region classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.771-781
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• 1997

In order to solve the problem of uncovered background region due to the region-due to the region-based motion estimation, this paper presents a new method which generates the uncovered region memory using motion estimation and shows the application of the algorithm for very low bit rate video coding. The proposed algorithm can be briefly described as follows it detects the changed region by using the information of FD(frame difference) and segmentation, and then as for only that region the backward motion estimation without transmission of shape information is done. Therefore, from only motion information the uncovered background region memory is generated and updated. The contents stored in the uncovered background region memory are referred whenever the uncovered region comes into existence. The regions with large prediction error are transformed and coded by using DCT. As results of simulation, the proposed algorithm shows the superior improvement in the subjective and objective image quality due to the remarkable reduction of transmission bits for prediction error.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2331-2334
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• 2003

Generally, skin color information has been widely used at the face region extraction step of the face region recognition process. But many experimental results show that they are very sensitive to the given threshold range which is used to extract the face regions at the input image. In this paper, we propose a face region extraction algorithm based on an adaptive range decision for skin color. First we extract the pixels which are regarded as the candidate skin color pixels by using the given range for skin color extraction. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face region. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Korean Journal of Computational Design and Engineering
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• v.7 no.2
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• pp.75-80
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• 2002

This paper details a robust and efficient algorithm for point classification with respect to a polygon in 2D real number domain. The concept of tolerance makes this algorithm robust and consistent. It enables to define‘on-boundary’ , which can be interpreted as either‘in-’or‘out-’side region, and to manage rounding errors in floating point computation. Also the tolerance is used as a measure of reliability of point classifications. The proposed algorithm is based on a ray-intersection technique known as the most efficient, in which intersections between a ray originating from a given test point and the boundary of a region are counted. An odd number of intersections indicates that the point is inside region. For practical examples the algorithm is most efficient because most edges of the polygon region are processed by simple bit operations.