• Journal of Broadcast Engineering
• /
• v.12 no.2
• /
• pp.128-136
• /
• 2007

Since the multi-view video is a set of video sequences captured by multiple array cameras for the same three-dimensional scene, it can provide multiple viewpoint images using geometrical manipulation and intermediate view generation. Although multi-view video allows us to experience more realistic feeling with a wide range of images, the amount of data to be processed increases in proportion to the number of cameras. Therefore, we need to develop efficient coding methods. One of the possible approaches to multi-view video coding is to generate an intermediate image using view interpolation method and to use the interpolated image as an additional reference frame. The previous view interpolation method for multi-view video coding employs fixed size block matching over the pre-determined disparity search range. However, if the disparity search range is not proper, disparity error may occur. In this paper, we propose an efficient view interpolation method using initial disparity estimation, variable block-based estimation, and pixel-level estimation using adjusted search ranges. In addition, we propose a multi-view video coding method based on H.264/AVC to exploit the intermediate image. Intermediate images have been improved about $1{\sim}4dB$ using the proposed method compared to the previous view interpolation method, and the coding efficiency have been improved about 0.5 dB compared to the reference model.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.1
• /
• pp.127-132
• /
• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.5A
• /
• pp.717-725
• /
• 2000

Within the last several years, there has been a large number of algorithms developed for face recognition. The majority of these algorithms have been view- and projection-based algorithms. Our definition of projection is not restricted to projecting the image onto an orthogonal basis the definition is expansive and includes a general class of linear transformation of the image pixel values. The class includes correlation, principal component analysis, clustering, gray scale projection, and matching pursuit filters. In this paper, we perform a detailed analysis of this class of algorithms by evaluating them on the FERET database of facial images. In our experiments, a projection-based algorithms consists of three steps. The first step is done off-line and determines the new basis for the images. The bases is either set by the algorithm designer or is learned from a training set. The last two steps are on-line and perform the recognition. The second step projects an image onto the new basis and the third step recognizes a face in an with a nearest neighbor classifier. The classification is performed in the projection space. Most evaluation methods report algorithm performance on a single gallery. This does not fully capture algorithm performance. In our study, we construct set of independent galleries. This allows us to see how individual algorithm performance varies over different galleries. In addition, we report on the relative performance of the algorithms over the different galleries.

• Korean Journal of Remote Sensing
• /
• v.32 no.5
• /
• pp.471-482
• /
• 2016

This paper presents an extraction method for water body which uses block-based image partitioning and extension of water body boundaries to improve the performance of supervised classification for water body extraction. The Mahalanobis distance image is created by computing the spectral information of Normalized Difference Water Index (NDWI) and Near Infrared (NIR) band images over a training site within the water body in order to extract an initial water body area. To reduce the effect of noise contained in the Mahalanobis distance image, we apply mean curvature diffusion to the image, which controls diffusion coefficients based on connectivity strength between adjacent pixels and then extract the initial water body area. After partitioning the extracted water body image into the non-overlapping blocks of same size, we update the water body area using the information of water body belonging to water body boundaries. The update is performed repeatedly under the condition that the statistical distance between water body area belonging to water body boundaries and the training site is not greater than a threshold value. The accuracy assessment of the proposed algorithm was tested using KOMPSAT-2 images for the various block sizes between $11{\times}11$ and $19{\times}19$. The overall accuracy and Kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.1
• /
• pp.47-54
• /
• 2008

In this paper, we propose an auto-detection algorithm of basal cell carcinoma(BCC) from the protophorphyrin IX(PpIX) fluorescence image induced by appling the methyl 5-aminolaevulinate(MAL) ointment-induced protophorphyrin IX(PpIX) to the skin tumour area and then shining the wood lamp on the area. The proposed algorithm first generates 3 mask areas-tumor area, suspected tumor area and tumor free area and then applies local watershed algorithm to the turner and the suspected tumor areas to make small watershed regions that include similar luminance value pixels. Next, small watershed regions are merged by hierarchical queue based fast region merging that uses the difference between the average luminance values of adjacent watershed regions as a region merging criterion and finally BCC regions are detected. 50 tissue samples are acquired from the tumour regions of 10 patients with BCC that are extracted by using the proposed algorithm and are performed pathological examination by expert dermatologist. Experiment result shows the rate of tumor detection from BCC lesion using presurgical in vivo of MAL-indeuced PpIX fluorescence has high sensitivity 94.1% and relatively high specificity 82.6%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.1
• /
• pp.41-49
• /
• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Investigative Magnetic Resonance Imaging
• /
• v.3 no.2
• /
• pp.146-153
• /
• 1999

Purpose : To evaluate the proton MR spectroscopy (MRS) findings of early and late ischemic infarcts and to compare these MRS findings with clinical symptoms. Materials and Methods : We obtained MRs spectra of 28 consecutive patients with early ischemic infarct (15 me, 13 women) between 2-10 (mean 6.2) days after stroke onset. Follow-up MRS was carried out between 20-32 (mean 25) days in 12 patients. The MRs spectra were acquired at 1.5T MR unit using single voxel technique with PRESS sequence, TR of 2000ms, TE of 288 (144)ms, and voxel size of 2cm x 2cm x 2cm in the three areas; an infarct lesion, the brain parenchyma adjacent to the lesion, and contralateral normal brain parenchyma. The NAA/creatine, choline/creatine, and lactate/creatine ratios were calculated in each spectrum. The spectra of MRS were compared with clinical symptoms. Results : In early infarct, decreased NAA/creatine ratio (n=22) and increased lactate/creatine ratio (n=25) were found in the infarct lesion. Choline/creastine ratio was within normal range (n=25). On follow-up MRS in late stage, NAA/creatine ratio in the infarct lesion decreased further (n=5), did not change (n=6), or increased (n=1). Lactate/creatine ratio became less elevated (n=10), or did not changed (n=2). Choline/creatine ratio had a trend for increase. The decreased NAA/creatine and increased lactate/creatine ratios were correlated well with the severity of symptoms, respectively. Conclusion : Decreased NAA/creatine and increased lactate/creatine ratios were common MRS findings characteristic in early ischemic infarct and correlated well with clinical severity. On follow-up MRS in late stage, NAA/creatine ratio decreased further or did not change, and lactate/creatine ratio became less elevated.

• Restorative Dentistry and Endodontics
• /
• v.35 no.3
• /
• pp.188-197
• /
• 2010

The purpose of this study was to compare radiopacity and radiographic discriminability of various FRC-Posts. Six FRC-Posts were investigated ; 1) FRC Postec Plus (Ivoclar Vivadent AG, Schaan, Liechtenstein), 2) Snowlight (Carbotech, Lewis center, OH, USA), 3) Dentin Post (Komet Brasseler, Lamgo, Germany), 4) Rely-X Fiber Post (3M ESPE, St.paul, MN, USA), 5) D.T.-Light Post (BISCO, Schaumburg, IL,USA), 6) Luxapost (DMG, Hamburg, Germany) The radiographs of each post with a reference 1 mm / 2 mm aluminum step-wedge was taken using digital sensor. The optical density were calculated by gray value of $10{\times}10$ pixel and compared in mm Al equivalent at five points. Six maxillary incisors of similar radiopacity were used. Radiographs of posts in Mx. incisors of lingual side of dry mandible were taken. We showed radiographs and asked the questionnaire to 3 radiologists, 3 endodontists, 3 general practitioners. The questionnaire was comprised of choices of the highest, lowest radiopaque individual post and the choices of best discriminable post at apical, coronal area. The following results were obtained. 1. Each post system showed various radiopacity. 2. There was change of discriminability between each post and simulated specimens regardless of examiner. Although each post showed various radiopacity, the difference of radiopacity did not affect on discriminability.