• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.845-852
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• 2017

In this paper, we propose a binarization algorithm using LVQ-Merge clustering method for fast and accurate extraction of cells from cell images. The proposed method clusters pixel data of a given image by using LVQ to remove noise and divides the result into two clusters by applying a hierarchical clustering algorithm to improve the accuracy of binarization. As a result, the execution speed is somewhat slower than that of the conventional LVQ or Otsu algorithm. However, the results of the binarization have very good quality and are almost identical to those judged by the human eye. Especially, the bigger and the more complex the image, the better the binarization quality. This suggests that the proposed method is a useful method for medical image processing field where high-resolution and huge medical images must be processed in real time. In addition, this method is possible to have many clusters instead of two cluster, so it can be used as a method to complement a hierarchical clustering algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.97-100
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• 2013

In this paper, we propose a detection method of the character rather than cursive, containing many components of the vertical and horizontal direction in complex background image. The characters have many dense corners but the background has few sparse corners. So we use harris corner detector and cluster the corners by using the position of the detected corners for detecting character regions. To merge or filter character regions, we analysis a histogram of gray image of character regions. In each improved region, we compare histograms of R, G, B channels to detect characters.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1686-1692
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• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.29-37
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• 2015

In this paper, we propose a robust pre-processing module for camera-based Optical Music Score Recognition (OMR) on mobile device. The captured images likely suffer for recognition from many distortions such as illumination, blur, low resolution, etc. Especially, the complex background music sheets recognition are difficult. Through any symbol recognition system, the staff line height and staff line space are used many times and have a big impact on recognition module. A robust and accurate staff line height and staff line space are essential. Some staff line height and staff line space are proposed for binary image. But in case of complex background music sheet image, the binarization results from common binarization algorithm are not satisfactory. It can cause incorrect staff line height and staff line space estimation. We propose a robust staff line height and staff line space estimation by using run-length encoding technique on edge image. Proposed method is composed of two steps, first step, we conducted the staff line height and staff line space estimation based on edge image using by Sobel operator on image blocks. Each column of edge image is encoded by run-length encoding algorithm Second step, we detect the staff line using by Stable Path algorithm and removal the staff line using by adaptive Line Track Height algorithm which is to track the staff lines positions. The result has shown that robust and accurate estimation is possible even in complex background cases.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.410-416
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• 2001

An algorithm of 3-D particle image velocimetry(3D-PIV) was developed for the measurement of 3-D velocity field of complex flows. The measurement system consists of two or three CCD camera and one RGB image grabber. In this study, stereo photogrammetty was applied for the 3-D matching of tracer particles. Epipolar line was used to decect the stereo pair. 3-D CFD data was used to estimate algorithm. 3-D position data of the first frame and the second frame was used to find velocity vector. Continuity equation was applied to extract error vector. The algorithm result involved error vecotor of about 0.13 %. In Pentium III 450MHz processor, the calculation time of cross-correlation for 1500 particles needed about 1 minute.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.901-908
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• 2013

The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.721-723
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• 2007

Recently, the size and location of the acquired container image while the container is loading and unloading in Harbors is not fixed. And it is difficult to get a good image for recognition because of the variation of external environment as those the size of container and where the yard-tractor stop is. In this paper, we estimate where the container ISO-code set is using Top-hat transform from realtime images and get an image to recognize container ISO-code using PAN/TILT/ZOOM camera. We extract the container ISO-code using Top-hat transform and Histogram projection. After binarization, we extract each character from complex background using labeling. We use BP(Backpropagation Network) to recognize extracted characters.

• 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.30-36
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• 2006

In this paper, a new type of written image segmentation based on relative comparison of region areas is proposed. The original image is composed of two distinctive regions; information and background. Compared with this binary original image, the observed one is the gray scale which is represented with complex regions with speckles and noise due to degradation or contamination. For applying threshold or statistical approach, there occurs the region-deformation problem in the process of binarization. At first step, the efficient iterated conditional mode (ICM) which takes the lozenge type block is used for regions formation into the binary image. Secondly the information region is estimated through selecting action and restored its primary state. Not only decision of the attachment to a region but also the calculation of the magnitude of its area are carried on at each current pixel iteratively. All region areas are sorted into a set and selected through the decision parameter which is obtained statistically. Our experiments show that these approaches are effective on ink-rubbed copy image (拓本 'Takbon') and efficient at shape restoration. Experiments on gray scale image show promising shape extraction results, comparing with the threshold-segmentation and conventional ICM method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.44-51
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• 2009

Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.