• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.196-205
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• 2000

As the number of cars is suddenly increasing, the number of cars on road exceeds the capacity of the road. In order to disperse the stream of traffic, there are many approaches for calculating the degree of congestion using traffic monitoring camera, and analyzing the velocity or the number of moving objects. Since those methods use background image, it is necessary to prepare the proper background image. In this paper, we proposed the algorithm to calculate the degree of congestion without background image. We perform DCT to the road image to obtain the edge information of cars, and then use it for calculating the degree of congestion.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.83-83
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• 2000

Recently, Vision system has being used all around industry. Sensor systems are used for Mark Reader, for example, optical scanning is proximity sensor system, have many disadvantages, such as, lacking user interface and difficulty to store original specimens. In contrast with this, Vision systems for Mark Reader has many advantages, including function conversion to achieve other work, high accuracy, high speed, etc. In this thesis, we have researched the development of Mark Reader by using a Vision system. The processing course of this s)'stem is consist to Image Pre-Processing such as noise reduction, edge detection, threshold processing. And then, we have carried out camera calibration to calibrate images which are acquired from camera. After searching for reference point within scanning area(60pixe1${\times}$30pixe1), we have calculated points crossing by using line equations. And then, we decide to each ROI(region of interest) which are expressed by four points. Next we have converted absolute coordinate into relative coordinate for analysis a translation component. Finally we carry out Mark Reading with images classified by six patterns. As a result of experiment which follows the algorithm has proposed, we have get error within 0.5% from total image.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.1
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• pp.33-38
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• 2001

In this paper, we present an image retrieval system using shape features. The preprocessing to gain shape feature includes edge extraction using chain code. The shape features consist of center of mass, standard deviation, ratio of major axis and minor axis length. The similarity is estimated as comparing the features of query image with the features of images in database. Thus, the candidates of images are retrieved according to the order of similarity. The result of an experimentation is dullness for scale, rotation and translation. We evaluate the performance of shape features for image retrieval on a database with over 170 images. The Recall and the Precision is each 0.72 and 0.83 in the result of average experiment. So the proposed method is presented useful method.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2005.11a
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• pp.45-53
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• 2005

A novel method for evaluating the anisotropy of the deterministic features in a stochastic 2D data is introduced. The ability of the wavelet transform for the identification of the abrupt discontinuities could be used to characterize the boundary of the deterministic area in a 2D stochastic data, such as flocs in paper structure. The one-dimensional wavelet transform with a small-scale range in MD and CD could quantify the amount of the edge in both directions, depending on the intensity of each floc. The flocs that are aligned in the MD direction result in a higher value of local wavelet energy in the CD direction. Therefore, the ratio of the total wavelet energy in CD and MD directions can be used as a new anisotropy index. This index is a measure of the floc-orientation and can provide an excellent tool to obtain the orientation distribution and the major oriented angle of flocs. Various simulated images and real stochastic data such as local gloss variation of printed image and formation image, have been tested and the results show this analysis method is very reliable to measure the anisotropy of the deterministic features.

• Journal of Digital Contents Society
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• v.5 no.4
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• pp.289-294
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• 2004

For the purpose of image analysis, we usually take the application method relying on the various mathematical theories. On the respect of image as two variable function one may uses the gradient vector or several type of energy functions induced by the conventional (partial) derivative. We also have used the tangent plane or curvature vector from the concept of differential geometry {**]. However, these mathematical tools my assume that the given function should be sufficiently smoothing enough to depict every local variation continuously. But the real application of these mathematical methods to the natural images or phenomena may occur the ill-posed problem. In this paper, we have defined the weak derivative as a loose form of the derivative so that it my applied to the irregular case with less ill-posed problem.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1511-1514
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• 2002

This paper presents content adaptive image watermark embedding using stochastic visual model based on multiwavelet transform. To embedding watermark, the original image is decomposed into 4 levels using a discrete multiwavelet transform, then a watermark is embedded into the JND(just noticeable differences) of the image each subband. The perceptual model is applied with a stochastic approach fer watermark embedding. This is based on the computation of a NVF(noise visibility function) that have local image properties. The perceptual model with content adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the JND. This method uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The experiment results of simulation of the proposed watermark embedding method using stochastic visual model based on multiwavelet transform techniques was found to be excellent invisibility and robustness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.765-767
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• 2013

Modern societies need image processing technology as mobile phones, computers and multimedia etc. are supplied, and image signal processing is now applied in many fields. However, images are damaged by impulse noise from various sources; to restore the damaged images from impulse noise standard median filter has been used as a typical method, but it makes errors at edge area lowering image quality. Therefore, in this paper average filter algorithm, in which mask is processed with multiple partition to remove impulse noise, is proposed. Simulation showed that the proposed method is superior in noise removal property to the existing ones.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.92-101
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• 1993

This paper describes algorithms to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the charateristics of license plates (the boundary information of license plates). Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the gray level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the probelm of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the provlem of the time requirement by processing in real time such that practical application is possible.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.