• 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.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.883-900
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• 2017

In this paper block based blind secure gray image watermarking scheme based on discrete wavelet transform and singular value decomposition is proposed. In devising the proposed scheme, security is given high importance along with other two requirements: robustness and imperceptibility. The use of discrete wavelet transform not only improves robustness but the selection of bands with high tolerance towards noise caused an improvement in terms of imperceptibility. The robustness further improved due to the involvement of singular vectors along with singular values in watermark embedding and extraction process. Finally, to achieve security, the selected DWT band is decomposed into smaller blocks and random blocks are chosen for modification. Furthermore, the elements of left and right singular vectors of selected blocks are chosen based on their dependence upon each other for watermark embedding. Various experiments using different images as host and watermark were conducted to examine and validate the proposed technique. Additionally, the proposed technique is tested against various attacks like compression, affine transformation, cropping, translation, X shearing, scaling, Y shearing, filtering, blurring, different kinds of noises, histogram equalization, rotation, etc. Lastly, the proposed technique is compared with state-of-the-art watermarking techniques and their comparison shows significant improvement of proposed scheme over existing techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.5
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• pp.96-104
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• 2000

It is well known that combustion stability under idle and part-load conditions directly affect fuel economy and exhaust emission. In practice, there have been a lot of studies so that a significant improvement in combustion stability has been achieved in this research field. However, applying published results to the development process of mass production engine, there are still many problems which are solved previously. In this study, initial flame behavior and flame propagation characteristic were investigated statistically in order to optimize combustion chamber shapes in the development stage of mass production S.I. engine. To the purpose, the authors applied the flame image capturing system to single cylinder optical engine. The captured flame images were effectively analyzed by using the image processing program which was developed by the authors and adopted new threshold algorithm instead of conventional histogram analysis. In addition, the cylinder pressure was also measured simultaneously to compare evaluated flame results with cylinder pressure data in terms of the combustion characteristics, combustion stability, and cycle-to-cycle combustion variability.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• Korean Journal of Human Ecology
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• v.1 no.2
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• pp.113-118
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• 1998

Clothing shape is principally described in seven factors that are composed of clothing design, clothing material, clothing size, pattern design, sewing method and body motion etc.. The aims of this study was to measurement out-line space-shape variation of clothing fitness with somato type by using the image processing. The subjects for direct anthropometric measurements were 248 female college students aged from 19 to 22. The data were statistically analyzed by principal analysis and cluster analysis. The results were obtained three somato type. Also I made skirts in order to analyzed to the out-line space-shape variation of clothing fitness with body. The effect of somato type on the shape of flare skirts was determined by the out-line space-shape variation of clothing fitness with body. The out-line space-shape variation of clothing fitness with body was observed between the node number and amplitudes of clothing wave form and node number was determined at the maxim of space-shape amplitude, and the space-shape amplitudes have related with aspect ratio of cross-sectional shape. Results for flare skirts show changes in amplitude and mean with fabrics, somato type. therefore gray-level histogram are correlated with changes out-line space-shape, differences in drape spacing and related fabric properties and their somato type. (Korean J Human Ecology 1(2):113∼110 1998)

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.338-347
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• 2005

A computer-based low back muscle evaluation system was designed to simultaneously acquire, process, display, quantify, and correlate electromyographic(EMG) activity with muscle force, and range of motion(ROM) in the lumbar muscle of human. This integrated multi-channel system was designed around notebook PC. Each channel consisted of a time and frequency domain block, and T-F(time-frequency) domain block. The captured data in each channel was used to display and Quantify : raw EMG, histogram, zero crossing, turn, RMS(root mean square), variance, mean, power spectrum, median frequency, mean frequency, wavelet transform, Wigner-Ville distribution, Choi-Williams distribution, and Cohen-Posch distribution. To evaluate the performance of the designed system, the static and dynamic contraction experiments from lumbar(waist) level of human were done. The experiment performed in five subjects, and various parameters were tested and compared. This system could equally well be modified to allow acquisition, processing, and analysis of EMG signals in other studies and applications.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.151-156
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• 2018

In this paper, an efficient reversible watermarking algorithm is proposed. By using the proposed algorithm, it is possible to increase the amount of copyright-related information to be embedded in cover image. Depending on the spatial locality and surface characteristics, it is possible to precisely predict the pixel value using neighboring pixels. If the predicted pixel value almost the same as the pixel value of the cover image, the differential value between the predicted pixel value and the pixel value of cover image is very small. So, the frequency is increased greatly at the peak point of histogram of difference sequence. Thus, it is possible to increase greatly the amount of secret data to be embedded in cover image. The experimental results show that the proposed watermarking algorithm is superior to the previous algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.