• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.601-608
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• 2007

2 Dimensional Gel Electrophoresis(2DGE) is an essentialmethodology for analysis on the expression of various proteins. For example, information for the location, mass, expression, size and shape of the proteins obtained by 2DGE can be used for diagnosis, prognosis and biological progress by comparison of patients with the normal persons. Protein spot matching for this purpose is comparative analysis of protein expression pattern for the 2DGE images generated under different conditions. However, visual analysis of protein spots which are more than several hundreds included in a 2DGE image requires long time and heavy effort. Furthermore, geometrical distortion makes the spot matching for the same protein harder. In this paper, an iterative algorithm is introduced for more efficient spot matching. Proposed method is first performing global matching step, which reduces the geometrical difference between the landmarks and the spot to be matched. Thus, movement for a spot is defined by a weighted sum of the movement of the landmark spots. Weight for the summation is defined by the inverse of the distance from the spots to the landmarks. This movement is iteratively performed until the total sum of the difference between the corresponding landmarks is larger than a pre-selected value. Due to local distortion generally occurred in 2DGE images, there are many regions in whichmany spot pairs are miss-matched. In the second stage, the same spot matching algorithm is applied to such local regions with the additional landmarks for those regions. In other words, the same method is applied with the expanded landmark set to which additional landmarks are added. Our proposed algorithm for spot matching empirically proved reliable analysis of protein separation image by producing higher accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.471-480
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• 1999

This paper presents design and performance improvement of a new digital tomosynthesis (DTS) system for object-detector synchronous rotation. Firstly, a new DTS system, called OSDR (Object-Detector Synchronous Rotation) is suggested and designed to acquire X-ray digital images. Secondly, the shape distortion of DTS images generated by an image intensifier is modeled. And a new synthesis algorithm, which overcomes the limitations of the existing synthesis algorithm, is suggested to improve the sharpness of the synthesized image. Also an artifact analysis of the DTS system is performed. Thirdly, some performance indices, which evaluate quantitatively performance improvement, are defined. And the experimental verification of the performance improvement is accomplished by the ODSR system newly designed. The advantages of the ODSR system are expressed quantitatively, compared with an existing system.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.60-67
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• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

• Journal of Biosystems Engineering
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• v.23 no.3
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• pp.291-304
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• 1998

A control algorithm fir the unmanned vehicles was developed using image information received through a CCD camera that acquires more powerful information over the wide range of wave-length comparing with other sensors and was applied to a speed-sprayer. The algorithm consisted of straight mode for passing along with middle of two tree-rows and turning mode for changing from a row to another row. In case of turning mode, two marks of colored papers were employed to indicate turning point and to decide turning direction for various orchard situations. The method of analysis and image would be differed according to camera's tilt-angle and position that is set on the speed-sprayer. Hence, it analyzed the point of difference by making camera's up and downward tilt-angle.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.797-799
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• 1999

In this paper, the edge intensification and detection algorithm which is one of image processing operations is considered. Edge detection algorithm is the most useful and important method for image processing or image analysis. The vision system based on these processing and concerned in specific project is proposed and is applied to the inverted pendulum in order to automatically acquire the angles between the bar and the perpendicular reference line. In this paper, the angles that are obtained from some images of computer vision system can offer useful informations for control of real inverted pendulum system. Next, the inverted pendulum will be controlled by the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.102-108
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• 2004

In this paper, we studied a vibration problem that is critical and common to most precision measurement systems. For micro mechanical part measurements, results obtained from the vision-based precision measurement system may contain errors due to the vibration. In order to defeat this generic problem, for the current study, a PC based image processing technique was used first, to assess the effect of the vibration to the precision measurement and second, to develop an in-situ calibration algorithm that automatically compensate the measurement results in real time. We used a set of stereoscopic CCD cameras to acquire the images for the dimensional measurement and the reference measurement. The mapping function was obtained through the in-situ calibration to compensate the measurement results and the statistical analysis for the actual results is provided in the paper. Based on the current statistical study, it is expected to obtain high precision results for the micro measurement systems.

• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.134-142
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• 2019

As the 4th industry has been widely distributed, there is a need for a process of real-time image recognition in various fields such as identification of company employees, security maintenance, and development of military weapons. Therefore, in this paper, we will propose an algorithm that effectively recognizes a test object by comparing it with the DB model. The proposed object recognition system first expresses the outline of the test object as a set of vertices with the distances of predefined length or more. Then, the degree of matching of the structures of the two objects is calculated by examining the distances to the outline of the DB model from the vertices constituting the test object. Because the proposed recognition algorithm uses the outline of the object, the recognition process is easy to understand, simple to implement, and a satisfactory recognition result is obtained.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.73-80
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• 2019

In this paper, we propose an effective dynamic range compression (DRC) method of infrared images. A histogram of infrared images has narrow dynamic range compared to visible images. Hence, it is important to apply the effective DRC algorithm for high performance of an infrared image analysis. The proposed algorithm for high dynamic range divides an infrared image into the overlapped blocks and calculates Shannon's entropy of overlapped blocks. After that, we classify each block according to the value of entropy and apply adaptive histogram modification method each overlapped block. We make an intensity mapping function through result of the adaptive histogram modification method which is using standard-deviation and maximum value of histogram of classified blocks. Lastly, in order to reduce block artifact, we apply hanning window to the overlapped blocks. In experimental result, the proposed method showed better performance of dynamic range compression compared to previous algorithms.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.64-69
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• 2019

With the advent of the fourth industry, the need for office automation with automatic character recognition capabilities is increasing day by day. Therefore, in this paper, we study a character recognition algorithm that effectively recognizes a new experimental data character by using learning data characters. The proposed algorithm computes the degree of similarity that the structural regions of learning data characters match the corresponding regions of the experimental data character. It has been confirmed that satisfactory results can be obtained by selecting the learning data character with the highest degree of similarity in the matching process as the final recognition result for a given experimental data character.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.