• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.616-624
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• 2002

An extraction method of central objects in the color images is proposed, in this paper. A central object is defined as a comparatively consist of the central object in the image. First of all. an input image and its decreased resolution images are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent regions are included by a same region in the decreased resolution image. Then core object regions and core background regions are selected from the inner region and the outer region respectively. Core object regions are the representative regions for the object and are selected by using the information about the information about the region size and location. Each inner regions is classified into foreground or background regions by comparing values of a color histogram intersection of the inner region against the core object region and the core background regions. The core object region and foreground regions consist of the central object in the image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.62-76
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• 1994

In this paper, a segmentation-based coding technique as applied to video sequences is proposed. A proposed method separates an image into contour and texture parts, then the visually-sensitive contour part is represented by chain codes and the visually-insensitive texture part is reconstructed by a representative motion vector of a region and mean of the segmented frame difference. It uses a change detector to find moving areas and adopts variable blocks to represent different motions correctly. For better quality of reconstructed images, the displaced frame difference between the original image and the motion compensated image reconstructed by the representative motion vector is segmented. Computer simulation with several video sequences shows that the proposed method gives better performance than the conventional ones in terms of the peak signal to noise ratio(PSNR) and compression ration.

• Annual Conference of KIPS
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• 2009.11a
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• pp.345-346
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• 2009

The segmentation of vessel including portal vein, hepatic vein and artery, from Computed Tomography (CT) images plays an important role in the therapeutic strategies for hepatic diseases. Representing segmented vessels in three dimensional spaces is extremely useful for doctors to plan liver surgery. In this paper, proposed method is focused on smoothing technique of segmented 3D liver vessels, which derived from 3D region growing approach. A pixel expand algorithm has been developed first to avoid vessel lose and disconnection cased by the next smoothing technique. And then a binary volume filtering technique has been implemented and applied to make the segmented binary vessel volume qualitatively smoother. This strategy uses an iterative relaxation process to extract isosurfaces from binary volumes while retaining anatomical structure and important features in the volume. Hard and irregular place in volume image has been eliminated as shown in the result part, which also demonstrated that proposed method is a suitable smoothing solution for post processing of fine vessel segmentation.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.265-270
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• 2011

In this paper, we propose a method for intensity correction using binarization-based region segmentation in 3D stereoscopic images. In the proposed method, 3D stereoscopic right image is segmented using binarizarion. Small regions in the segmented image are eliminated. For each region in right image, a corresponding region in left image is decided through region matching using correlation coefficient. When region-based matching, in order to prevent overlap between regions, we remove a portion of the area closed to the region boundary using morphological filter. The intensity correction in left and right image can be performed through histogram specification between the corresponding regions. Simulation results show the proposed method has the smallest matching error than the conventional method when we generate the right image from the left image using block based motion compensation.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.83-92
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• 2006

This paper describes an image disparity estimation method using region-based stereo matching. Region-based disparity estimation yields a disparity map as the unit of segmented region. However it estimates disparity imprecisely because it not only has matching errors but also applies an identical way to disparity estimation, which does not consider each type of matched regions. To solve this problem, we proposes a disparity estimation method which considers the type of matched regions. That is, the proposed method classifies whole matched regions into a similar-matched region, a dissimilar-matched region, a false-matched region and a miss-matched region. We then performs proper disparity estimation for each type of matched regions. This method minimizes the error in estimating disparity which is caused by inaccurate matching and also improves the accuracy of disparity of the well-matched regions. The experimental results show the improved accuracy of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.687-696
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• 2014

This paper presents a semantic object extraction method using user's stroke input, color, and depth information. It is supposed that a semantically meaningful object is surrounded with a few strokes from a user, and has similar depths all over the object. In the proposed method, deciding the region of interest (ROI) is based on the stroke input, and the semantically meaningful object is extracted by using color and depth information. Specifically, the proposed method consists of two steps. The first step is over-segmentation inside the ROI using color and depth information. The second step is semantically meaningful object extraction where over-segmented regions are classified into the object region and the background region according to the depth of each region. In the over-segmentation step, we propose a new marker extraction method where there are two propositions, i.e. an adaptive thresholding scheme to maximize the number of the segmented regions and an adaptive weighting scheme for color and depth components in computation of the morphological gradients that is required in the marker extraction. In the semantically meaningful object extraction, we classify over-segmented regions into the object region and the background region in order of the boundary regions to the inner regions, the average depth of each region being compared to the average depth of all regions classified into the object region. In experimental results, we demonstrate that the proposed method yields reasonable object extraction results.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.401-404
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• 2006

Mean shift procedure is applied for the data points in the joint spatial-range domain and achieves a high quality. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the demerit is that the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for over-segmented images. The proposed method converts a over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the proposed method use by merging constraints to decide whether regions is merged or not. After then, we merge the regions in RGB color space for non-processed regions in HSI color space. Experimental results show the superiority in region's segmentation results.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1247-1254
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• 2003

Currently. the north american symptomatic carotid endarterectomy trial, european carotid surgery trial, and common carotid method are used to measure the carotid stenosis for determining candidate for carotid endarterectomy using the projection angiography from different modalities such as digital subtraction angiography. rotational angiography, computed tomography angiography and magnetic resonance angiography. A new computerized carotid stenosis measuring system was developed using MR angiography axial image to overcome the drawbacks of conventional carotid stenosis measuring methods, to reduce the variability of inter-observer and intra-observer. The gray-level thresholding is one of the most popular and efficient method for image segmentation. We segmented the carotid artery and lumen from three-dimensional time-of-flight MRA axial image using gray-level thresholding technique. Using the measured intima-media thickness value of common carotid artery for each cases, we separated carotid artery wall from the segmented carotid artery region. After that, the regions of segmented carotid without artery wall were divided into region of blood flow and plaque. The calculation of carotid stenosis degree was performed as the following; carotid stenosis grading is(area measure of plaque/area measure of blood flow region and plaque) * 100%.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1390-1393
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• 1987

This paper describes the image data compression based on the image decomposition. We reduced the processing time using the segmentation based on the distribution of grey level, and obtained high compression rate using the Huffman run-length coding for the segmented image, and the 2-Dimensional least square curve fitting and the shift coder for each region.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.59-62
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• 2002

Watershed Segmentation with Multiple Merging Conditions in Region Growing Process The watershed segmentation method holds the merits of edge-based and region-based methods together, but still shows some problems such as over segmentation and merging fault. We propose an algorithm which overcomes the problems of the watershed method and shows efficient performance for .general images, not for specific ones. The algorithm segments or merges regions by thresholding the depths of the catchment basins, the similarities and the sizes of the regions. The experimental results shows the reduction of the number of the segmented regions that are suitable to human visual system and consciousness.