• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.8-17
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• 2008

A new algorithm for automatic extraction of interesting objects is proposed in this paper. The proposed algorithm can be summarized in two steps. First, segmentation of color image that split interesting objects and backgrounds is performed. According to the research stating, 'Humans perceive things by contracting color into three to four essential colors,' a color image is segmented into three regions utilizing k-mean algorithm, followed by annexing the regions when the similarities of them exceeds the critical value based on the calculation of degrees in the histogram similarity, Second, identifying the interesting objects out of the segmented image, partitioned by the image composition theory, is performed. To have a good picture, it is important to adjust positions of interesting objects according to picture composition. Extracting objects is a retro-deduction process using a weighted mask designed upon the triangular composition of picture. To prove the quality of the proposed method, experiments are performed over four hundreds images as well as comparison with recently proposed KMCC and GBIS methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1215-1220
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• 2016

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by linear interpolation, histogram weighted values and median filter after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. In addition, the algorithm was compared with the conventional methods for objectively and used the PSNR(peak signal to noise ratio) as the basis of the determination.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.103-111
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• 2005

This paper presents a novel automated threshold-decision algorithm that uses the mean and standard-deviation values obtained from the difference values of consecutive frames. At first, the calculation of difference values is obtained by the weighted ${\chi}^2$-test algorithm which was modified by joining color histogram to ${\chi}^2$-test algorithm. The weighted ${\chi}^2$-test algorithm can subdivide the difference values by imposing weights according to NTSC standard. In the first step, the proposed automatic threshold-decision algorithm calculates the mean and standard-deviation value from the total difference values, and then subtracts the mean value from the each difference values. In the next step, the same process is performed on the remained difference values, and lastly, the threshold is detected from the mean when the standard deviation has a maximum value. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method efficiently estimates the thresholds and reliably detects scene changes.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.751-758
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• 2021

Objective: Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. Materials and Methods: In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. Results: Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65-95%), 100% (41 out of 41; 95% CI, 91-100%), 95% (59 out of 61; 95% CI, 87-98%), and 0.966 (95% CI, 0.912-1.000), respectively. Conclusion: Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.77-82
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• 2013

The object tracking method using particle filtering has been proved successful since it is based on the Monte Carlo simulation to estimate the posterior distribution of the state vector that is nonlinear and non-Gaussian in the real-world situation. In this paper, we present two nobel methods that can improve the performance of the object tracking algorithm based on the particle filtering. First one is the feedback method that replace the low-weighted tracking sample by the estimated state vector in the previous frame. The second one is an tracking box correction method to find an confidence interval of back projection probability on the estimated candidate object area. An sample propagation equation is also presented, which is obtained by experiments. We designed well-organized test data set which reflects various challenging circumstances, and, by using it, experimental results proved that the proposed methods improves the traditional particle filter based object tracking method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.360-373
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• 2017

This paper presents a novel digital video steganalysis scheme against the spatial domain video steganography technology based on a spatial temporal detector (ST_D) that considers both spatial and temporal redundancies of the video sequences simultaneously. Three descriptors are constructed on XY, XT and YT planes respectively to depict the spatial and temporal relationship between the current pixel and its adjacent pixels. Considering the impact of local motion intensity and texture complexity on the histogram distribution of three descriptors, each frame is segmented into non-overlapped blocks that are $8{\times}8$ in size for motion and texture analysis. Subsequently, texture and motion factors are introduced to provide reasonable weights for histograms of the three descriptors of each block. After further weighted modulation, the statistics of the histograms of the three descriptors are concatenated into a single value to build the global description of ST_D. The experimental results demonstrate the great advantage of our features relative to those of the rich model (RM), the subtractive pixel adjacency model (SPAM) and subtractive prediction error adjacency matrix (SPEAM), especially for compressed videos, which constitute most Internet videos.

• ETRI Journal
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• v.33 no.2
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• pp.219-229
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• 2011

In this paper, we propose a transformation scheme used to analyze online gaming traffic properties and develop a traffic model. We analyze the packet size and the inter departure time distributions of a popular first-person shooter game (Left 4 Dead) and a massively multiplayer online role-playing game (World of Warcraft) in order to compare them to the existing scheme. Recent online gaming traffic is erratically distributed, so it is very difficult to analyze. Therefore, our research focuses on a transformation scheme to obtain new smooth patterns from a messy dataset. It extracts relatively heavy-weighted density data and then transforms them into a corresponding dataset domain to obtain a simplified graph. We compare the analytical model histogram, the chi-square statistic, and the quantile-quantile plot of the proposed scheme to an existing scheme. The results show that the proposed scheme demonstrates a good fit in all parts. The chi-square statistic of our scheme for the Left 4 Dead packet size distribution is less than one ninth of the existing one when dealing with erratic traffic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3670-3676
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• 2011

A cell animation is represented by one background cell, and there is much difference of images when its shot is changed. Also, it does not have a lot of colors since people themselves draw it. In order to effectively detect shot transitions of cell animations while fully considering their intrinsic characteristics, in this paper, we propose a animation shot boundary detection algorithm that utilizes color and block-based histograms step by step. The suggested algorithm first converts RGB color space into HSI color one, and coarsely decides if adjacent frames contains a shot transition by performing color difference operation between two images. If they are considered to have a shot transition candidate, we calculate color histograms for 9 sub-regions of the adjacent images and apply weights to them. Finally, we determine whether there is a real shot transition by analyzing the weighted sum of histogram values. In experiments, we show that our method is superior to others.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1132-1137
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• 2011

In this paper, we propose Modified Multi CAMShift Algorithm(Modified Multi Continuously Adaptive Mean Shift Algorithm) that extracts skin color area and tracks several human body parts for real-time human tracking system. Skin color area is extracted by filtering input image in predefined RGB value range. These areas are initial search windows of hands and face for tracking. Gaussian background model prevents search window expending because it restricts skin color area. Also when occluding between these areas, we give more weights in occlusion area and move mass center of target area in color probability distribution image. As result, the proposed algorithm performs better than the original CAMShift approach in multiple object tracking and even when occluding of objects with similar colors.

• Progress in Medical Physics
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• v.21 no.4
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• pp.354-359
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• 2010

The purpose of this study is to verify the dosimetric effect on real PTV (planning target volume) coverage and safety of OARs (organs at risk) at various image fusion protocol-based radiosurgery plan for pituitary adenomas. Real PTV coverage and its variation was acquired and maximum dose and the volume absorbing above threshold dose were also measured for verifying the safety of optic pathway and brainstem. The protocol that can reduce superior-inferior uncertainty by using both axial and coronal MR (magnetic resonance) image sets shows relatively lower values than that of case using only axial image sets. As a result, the image fusion protocol with both axial and coronal image sets can be beneficial to generate OAR-weighted radiosurgery plan.