• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.643-654
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• 2003

This paper proposes slippage and rotation sensing method in tactile image of robot griper. To overcome the demerits of inaccurate taxel positional sensing generated by previous moment method and edge ＆ line method according to constraints of taxet number changing or minimum taxel number, the proposed method classified the sensing method into two classes such as pixel status analysis and decision factor determination. The decision factor determines taxel threshold for filtering and sensing method choice based on moment method and edge ＆ line method. Computer simulations and experiment result show that the proposed method enhances the slippage and rotation sensing than previous methods for tactile image.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.332-337
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• 2007

In the early twenty-first century, minimally invasive surgery is the mainstay of various kinds of surgical fields. Surgeons gave percutaneously surgical treatment of the screw directly using a fluoroscopic view in the past. The latest date, they began to operate the fractured carpal bone surgery using Computerized Tomography (CT). Carpal bones composed of wrist joint consist of eight small bones which have hexahedron and sponge shape. Because of these shape, it is difficult to grasp the shape of carpal bones using only CT image data. Although several image segmentation studies have been conducted with carpal bone CT image data, more studies about carpal bone using CT data are still required. Especially, to apply the software implemented from the studies to clinical fIeld, the outcomes should be user friendly and very accurate. To satisfy those conditions, we propose modified multi-seed region growing segmentation method which uses simple threshold and the canny edge detector for finding edge information more accurately. This method is able to use very easily and gives us high accuracy and high speed for extracting the edge information of carpal bones. Especially, using multi-seed points, multi-bone objects of the carpal bone are extracted simultaneously.

• Proceedings of the KSRS Conference
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• v.2
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• pp.643-646
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• 2006

This paper addresses the approach to extract linear features from satellite imagery using an efficient segmentation method. The extraction of linear features from satellite images has been the main concern of many scientists. There is a need to develop a more capable and cost effective method for the Iranian map revision tasks. The conventional approaches for producing, maintaining, and updating GIS map are time consuming and costly process. Hence, this research is intended to investigate how to obtain linear features from SPOT satellite imagery. This was accomplished using a discontinuity-based segmentation technique that encompasses four stages: low level bottom-up, middle level bottom-up, edge thinning and accuracy assessment. The first step is geometric correction and noise removal using suitable operator. The second step includes choosing the appropriate edge detection method, finding its proper threshold and designing the built-up image. The next step is implementing edge thinning method using mathematical morphology technique. Lastly, the geometric accuracy assessment task for feature extraction as well as an assessment for the built-up result has been carried out. Overall, this approach has been applied successfully for linear feature extraction from SPOT image.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.365-371
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• 2000

To restore image degraded by out-of-focus blur and additive noise, an iterative restoration is used. Acceleration parameter is usually applied equally to all over the image without considering the local characteristics of degraded images. As a result, the conventional methods are not effective in restoring severely degraded edge region and shows slow convergence rate. To solve this problem we propose an adaptive iterative restoration according to local degradation, in which the acceleration parameter has low value in flat region that is less degraded and high value in edge region that is more degraded. Through experiments, we verified that the proposed method showed better results with fast convergence rate, showed Visually better image in edge region and lower MSE than the conventional methods.

• Journal of Korean Port Research
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• v.13 no.2
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• pp.303-312
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• 1999

The automated gate operating system is developed in this paper that controls the information of container at gate in the ACT. This system can be divided into three parts and consists of container identifier recognition car plate recognition container deformation perception. We linked each system and organized efficient gate operating system. To recognize container identifier the preprocess using LSPRD(Line Scan Proper Region Detection)is performed and the identifier is recognized by using neural network MBP When car plate is recognized only car image is extracted by using color information of car and hough transform. In the port of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container. Thirdly edge is fitted into line segment so that container deformation is perceived. As a results of the experiment with this algorithm superior rate of identifier recognition is shown and the car plate recognition system and container deformation perception that are applied in real-time are developed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.193-196
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• 2010

In this paper, we propose a method to classify the types of vehicle as full, medium, or small size. The proposed method is composed of three steps. First, after obtaining vehicle contour from template candidate image, edge distance template is created by distance transform of the vehicle's contour. Second, the vehicle type of input image is classified as the type of template which has minimal edge distance with input image. The edge distance value means the measurement of distance between input image and template at each pixel which is part of vehicle contour. Experimental results demonstrate that our method presented a good performance of 80% about test images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.965-970
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• 2010

In this paper, a iterative image restoration method is proposed to restore for noisy-blurred images. In conventional method, regularization is usually applied to all over the without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solvethis problem we proposed an adaptive regularization iterative restoration using directional regularization operator considering edges in four directions and the regularization operator with no direction for flat regions. We verified that the proposed methods showed better results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.45-51
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• 2006

Recently, the IDS(Intrusion Detection System) using a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in the environment with abrupt illumination change. In this paper, we propose an effective moving edge extraction algerian from a sequence image. The proposed algorithm extracts edge segments from current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of moving object in the noisy environment with abrupt illumination change.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.155-163
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• 1999

In this paper, we propose novel scene change detection and key frame selection techniques, which use two feature images, i.e., DC and edge images, extracted directly from MPEG compressed video. For fast edge image extraction. we suggest to utilize 5 lower AC coefficients of each DCT. Based on this scheme, we present another edge image extraction technique using AC prediction. Although the former is superior to the latter in terms of visual quality, both methods all can extract important edge features well. Simulation results indicate that scene changes such as cut. fades, and dissolves can be correctly detected by using the edge energy diagram obtained from edge images and histograms from DC images. In addition. we find that our edge images are comparable to those obtained in the spatial domain while keeping much lower computational cost. And based on HVS, a key frame of each scene can also be selected. In comparison with an existing method using optical flow. our scheme can select semantic key frames because we only use the above edge and DC images.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.32-37
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• 2016

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.