• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.177-181
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• 1999

This paper presents an algorithm for building detection from aerial image using segment measure function and line relation. In the detection algorithm proposed, edge detection, linear approximation and line linking are used and then line measure function is applied to each line segment in order to improve the accuracy of linear approximation. Parallelisms, orthogonalities are applied to the extracted liner segments to extract building. The algorithm was applied to aerial image and the buildings were accurately detected.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1074-1084
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• 1995

In this paper, we investigate edge extraction and segmentation of range images. We first discuss problems that arise in the conventional region-based segmentation methods and edge-based ones using principal curvatures, then we propose an edge-based algorithm. In the proposed algorithm, we extract edge contours by using the Gaussian filter and directional derivatives, and segment a range image based on extracted edge contours, Also we present the problem that arises in the conventional thresholding, then we propose a new threshold selection method. To solve the problem that local maxima of the first- and second- order derivatives gather near step edges, we first find closed roof edge contours, fill the step edge region, and finally thin edge boundaries. Computer simulations with several range images show that the proposed method yields better performance than the conventional one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2785-2791
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• 2015

In this paper, neddle detection algorithm for the removal of needle stuck into skin in oriental clinic is presented. First, in the proposed method, potential candidate areas of each needle are selected by using the morphological open operation in a gray image, and the false candidates are removed by considering their area size. Next, edge points are extracted using canny edge detector in selected candidate areas, line segments are approximated using the edge points. Based on the direction of line segment and the distance between two line segments, two main line segments of the needle are extracted. The final verification of needle is accomplished by using the morphological analysis of these two line segments. In the experiments, the detection rate of proposed method reaches to 97.5% for the 16 images containing 119 needles.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.11-20
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• 2007

This paper proposes a new efficient method to detect ellipses using a segment merging based Randomized Hough Transform. The key idea of the proposed method is to separate single line segments from an edge image, to estimate ellipses from any pair of the single line segments using Randomized Hough Transform (RHT), and to merge the ellipses. This algorithm is able to accuracy estimate the number of ellipses and largely improves the computational time by reducing iterations.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.116-123
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• 1990

Most algorithms utilized the horizontal epipolar lines for solving the correspondence problem in 3-D computer vision. However, the requirement is very difficult to be satisfied in real situations. In this paper, we propose a binocular-stereo matching algorithm, based on line matching method, which does not require the horizontal epipolar lines of the extreme pixels of a given line segment and two circles whose radius is equal to the maximum disparity. And we use the features including the direction of line segments, edge strength and cross-correlation for line matching. The experimental results show that the proposed algorithm can be a useful tool for solving the correspondence problem in 3-D computer vision.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.133-140
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• 2002

This paper presents a method for line segment grouping used for detection of various building roofs. First, by using edge preserving filtering. noise is eliminated and then images are segmented by watershed algorithm, which preserves location of edge pixels. To extract line segments between control points from boundary of each region, we calculate curvature of each pixel on the boundary and then find the control points. Line linking is performed according to direction and length of line segments and finally the location of line segments is adjusted using gradient magnitudes of all pixels of the line segment. The algorithm has been applied to aerial imagery and the results show accurate building roof detection.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.587-589
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• 1995

The safe navigation of a mobile robot requires the recognition of the environment in terms of vision processing. To be guided in the given path, the robot should acquire the information about where the wall and corridor are located. Also unexpected obstacles should be detected as rapid as possible for the safe obstacle avoidance. In the paper, we assume that the mobile robot should be navigated in the flat surface. In terms of this assumption we simplify the correspondence problem by the free navigation surface and matching features in that coordinate system. Basically, the vision processing system adopts line segment of edge as the feature. The extracted line segments of edge out of both image are matched in the free nevigation surface. According to the matching result, each line segment is labeled by the attributes regarding obstacle and free surface and the 3D shape of obstacle is interpreted. This proposed vision processing method is verified in terms of various simulations and experimentation using real images.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.444-447
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• 2008

In this paper, we proposed the recognition system of car license plates to mitigate traffic problems. The processing sequence of the proposed algorithm is as follows. At first, a license plate segment is extracted from an acquired car image using morphological features and color information, and noises are eliminated from the extracted license plate segment using line scan algorithm and Grassfire algorithm, and then individual codes are extracted from the license plate segment using edge tracking algorithm. Finally the extracted individual codes are recognized by an FCM algorithm. In order to evaluate performance of segment extraction and code recognition of the proposed method, we used 100 car images for experiment. In the results, we could verify the proposed method is more effective and recognition performance is improved in comparison with conventional car license plate recognition methods.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.73-76
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• 2001

A segmentation is a prior processing for medical image analysis and 3D reconstruction. This Paper provides the method to segment 3D Visible Human color images. Firstly, the reference images that have a initial curve are segmented using Balloon and the results are propagated to the adjacent images. In the propagation processing, the result of the adjacent slice is modified by Edge-limited SRG Finally, the 3D Balloon improves the segmentation results of each 2D slice. the proposed method's performance was verified through the experiments to segment thigh muscles of Visible Human color images.