• Journal of Digital Contents Society
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• v.16 no.4
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• pp.583-593
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• 2015

Today, caused by the growth of computer and communication technology, multimedia, especially image data are being used in different application divisions. JPEG2000 that is widely used these days provides a Region-of-Interest(ROI) technique. The extraction of ROI has to be rapidly executed and automatically extracted in a huge amount of image because of being seen preferentially to the users. For this purpose, this paper proposes a method about preferential processing and automatic extraction of ROI using the distribution of edge in the code block of JPEG2000. The steps are the extracting edges, automatical extracting of a practical ROI, grouping the ROI using the ROI blocks, generating the mask blocks and then quantization, ROI coding which is the preferential processing, and EBCOT. In this paper, to show usefulness of the method, we experiment its performance using other methods, and executes the quality evaluation with PSNR between the images not coding an ROI and coding it.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.127-136
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• 2008

A digitized image passes by encoding, storing or transmitting to show it to users. In this process, may be users would want to see a specific region of the image. And depending on the system features or in the case that the resolution of the image is large, it will take a huge time that the image show to the users. In this time, it will be resonable that the part users want to see shows earlier and afterward the other parts show. For this, JPEG2000 standards provide ROI. Although ROI extraction that users specify ROI arbitrarily is the best, people not always participate in doing all the images. There needs an automatic ROI extracting and storing in some images. JPEG2000 should extract and send an ROI automatically when the images is encoded without ROI. This study proposes a method that automatically extracts an ROI, makes the ROI masks, transfers the masked image preferentially and the background. And the study compares and experiments the proposed method and the method not having ROI.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.63-71
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• 2009

In this study, we propose a revised dynamic ROI (Region-of-Interest) coding method in which the focused ROI is automatically extracted without help from users during the recovery process of low DOF (Depth-of-Field) JPEG2000 image. The proposed method creates edge mask information using high frequency sub-band data on a specific level in DWT (Discrete Wavelet Transform), and then identifies the edge code block for a high-speed ROI extraction. The algorithm scans the edge mask data in four directions by the unit of code block and identifies the edge code block simply and fastly using a edge threshold. As the results of experimentation applying for Implicit method, the proposed method showed the superiority in the side of speed and quality comparing to the existing methods.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.915-924
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• 2002

Conventional Systems for parcel sorting consist of two sequences as loading the parcel into conveyor belt system and post-code input. Using bar code information, the parcels to be recorded and managed are recognized. This paper describes a 32 $\times$ 32 sized mini-block inspection to extract bar code Region of Interest (ROI) from the line Charged Coupled Device (CCD) camera capturing image of moving parcel at 2m/sec speed. Firstly, the Min-Max distribution of the mini-block has been applied to discard the background of parcel and region of conveying belts from the image. Secondly, the diagonal inspection has been used for the extraction of letters and bar code region. Five horizontal line scanning detects the number of edges and sizes and ROI has been acquired from the detection. The wrong detected area has been deleted by the comparison of group size from labeling processes. To correct excluded bar code region in mini-block processes and for analysis of bar code information, the extracted ROI 8 boundary points and decline distribution have been used with central axis line adjustment. The ROI extraction and central axis creation have become enable within 60～80msec, and the accuracy has been accomplished over 99.44 percentage.

• Journal of Digital Contents Society
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• v.12 no.1
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• pp.1-9
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• 2011

JPEG2000 offers the technique which compresses the interested regions with higher quality than the background. It is called by an ROI(Region-of-Interest) coding method. In this paper, we use images including the human faces, which are processed uppermost and compressed with high quality. The proposed method consists of 2 steps. The first step extracts some faces and the second one is ROI coding. To extract the faces, the method cuts or scale-downs some regions with $20{\times}20$ window pixels for all the pixels of the image, and after preprocessing, recognizes the faces using neural networks. Each extracted region is identified by ROI mask and then ROI-coded using Maxshift method. After then, the image is compressed and saved using EBCOT. The existing methods searched the ROI by edge distributions. On the contrary, the proposed method uses human intellect. And the experiment shows that the method is sufficiently useful with images having several human faces.

• Journal of Oral Medicine and Pain
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• v.41 no.3
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• pp.85-90
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• 2016

Purpose: The aim of this study was to develop an automated software to extract tooth and pulpal area from sectional cone-beam computed tomography (CBCT) images, which can guarantee more reproducible, objective and time-saving way to measure pulp/tooth volume ratio. Methods: The software program was developed using MATLAB (MathWorks). To determine the optimal threshold for the region of interest (ROI) extraction, user interface to adjust the threshold for extraction algorithm was added. Default threshold was determined after several trials to make the outline of extracted ROI fitting to the tooth and pulpal outlines. To test the effect of starting point location selected initially in the pulpal area on the final result, pulp/tooth volume ratio was calculated 5 times with different 5 starting points. Results: Navigation interface is composed of image loading, zoom-in, zoom-out, and move tool. ROI extraction process can be shown by check in the option box. Default threshold is adjusted for the extracted tooth area to cover whole tooth including dentin, cementum, and enamel. Of course, the result can be corrected, if necessary, by the examiner as well as by changing the threshold of density of hard tissue. Extracted tooth and pulp area are reconstructed three-dimensional (3D) and pulp/tooth volume ratio is calculated by voxel counting on reconstructed model. The difference between the pulp/tooth volume ratio results from the 5 different extraction starting points was not significant. Conclusions: In further studies based on a large-scale sample, the most proper threshold to present the most significant relationship between age and pulp/tooth volume ratio and the tooth correlated with age the most will be explored. If the software can be improved to use whole CBCT data set rather than just sectional images and to detect pulp canal in the original 3D images generated by CBCT software itself, it will be more promising in practical uses.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.31-39
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• 2010

In some image processing system or the users who want to see a specific region of image simply, if a part of the image has higher quality than other regions, it would be a nice service. Specifically in mobile environments, preferential service was needed, as the screen size is small. So, JPEG2000 supplies this function. But this doesn't support the process to extract specific regions or service and does the functions to add some techniques. It is called by ROI(Region-of-Interest). In this paper, we use images including human faces, which are processed most preferentially and compressed with high quality. Before an image is served to the users, it is compressed and saved. Here, the face parts are compressed with higher quality than the background which are relatively with lower quality. This technique can offer better service with preferential transferring of the faces, too. Besides, whole regions of the image are compressed with same quality and after searching the faces, they can be preferentially transferred. In this paper, we use a face extraction approach based on neural network and the preferential processing with EBCOT of JPEG2000. For experimentation, we use images having several human faces and evaluate objectively and subjectively, and proved that this approach is a nice one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.737-747
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• 2012

Recently, in order to support automatic meter reading for conventional metering devices, an image processing-based approach of recognizing the number meter data in the captured meter images has attracted many researchers' interests. Numerical character segmentation is a very critical process for successful recognition. In this paper, we propose an efficient numeric character segmentation method which can segment numeric characters well for any metering device types under diverse illumination environments. The proposed method consists of two consecutive stages; detection of number area containing all numbers as a tight ROI(Region of Interest) and segmentation of numerical characters in the ROI. Detection of tight ROI is achieved in two steps: extraction of rough ROI by utilizing horizontal line segments after illumination enhancement preprocessing, and making the rough ROI more tight through clipping utilizing vertical and horizontal projection about binarized ROI. Numerical character segmentation in the detected ROI is stably achieved in two processes of 'vertical segmentation of each number region' and 'number segmentation in the each vertical segmented number region'. Through the experiments about a homegrown meter image database containing various meter type images of low contrast, low intensity, shadow, and saturation, it is shown that the proposed numeric character segmentation method performs effectively well for any metering device types under diverse illumination environments.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.93-101
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• 2009

This paper suggests a new algorithm automatically searching for Region-of-Interest(ROI) with high speed, using the edge information of high frequency subband transformed with wavelet. The proposed method executes a searching algorithm of 4-direction object boundary by the unit of block using the edge information, and detects ROIs. The whole image is splitted by $64{\times}64$ or $32{\times}32$ sized blocks and the blocks can be ROI block or background block according to taking the edges or not. The 4-directions searche the image from the outside to the center and the algorithm uses a feature that the low-DOF image has some edges as one goes to center. After searching all the edges, the method regards the inner blocks of the edges as ROI, and makes the ROI masks and sends them to server. This is one of the dynamic ROI method. The existing methods have had some problems of complicated filtering and region merge, but this method improved considerably the problems. Also, it was possible to apply to an application requiring real-time processing caused by the process of the unit of block.