• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.311-314
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• 1995

This paper presents a method for measuring tool wear parameters based on two dimensional image information. The tool wear images were obtained from an ITV camera with magnifying and lighting devices, and were analyzed using image processing techniques such as thresholding, noise filtering and boundary tracing. Thresholding was used to transform the captured gray scale image into a binary image for rapid sequential image processing. The threshold level was determined using a novel technique in which the brightness histograms of two concentric windows containing the tool wear image were compared. The use of noise filtering and boundary tracing to reduce the measuring errors was explored. Performance tests of the measurement precision and processing speed revealed that the direct method was highly effective in intermittent tool wear monitoring.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.487-490
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• 1999

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based features. The color features are extracted from color histograms of the global image and the wavelet based features are extracted from the invariant moments of the high-pass band image through the spatial-frequency analysis of the wavelet transform. The proposed algorithm, called color and wavelet features based query(CWBQ), is composed of two-step query operations for efficient image retrieval: the coarse level filtering operation and the fine level matching operation. In the first filtering operation, the color histogram feature is used to filter out the dissimilar images quickly from a large image database. The second matching operation applies the wavelet based feature to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1093-1096
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• 1999

In this paper, video image segmentation algorithm based on color histogram and change detector is proposed. Color histograms are calculated from both changed region which is detected in the previous and current frame and unchanged region. With each histogram, modes and valleys are detected. Then, color vectors are calculated by averaging pixels in modes. Markers are extracted by labeling color vectors that represent modes, the watershed algorithm is applied to determine uncertain region. In growing region, the root mean square(RMS) of the distance between average pixel in marker region and adjacent pixel is used as a measure. The proposed algorithm based on color histogram and change detector segments video image fastly and effectively. And simulation results show that the proposed method determines the exact boundary between background and foreground.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.5
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• pp.434-441
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• 2016

Numerical simulations of 1D Burgers equation and 2D sloshing problem were carried out to study numerical discrepancy between serial and parallel computations. The numerical domain was decomposed into 2 and 4 subdomains for parallel computations with message passing interface. The numerical solution of Burgers equation disclosed that fully explicit boundary conditions used on subdomains of parallel computation was responsible for the numerical discrepancy of transient solution between serial and parallel computations. Two dimensional sloshing problems in a rectangular domain were solved using OpenFOAM. After a lapse of initial transient time sloshing patterns of water were significantly different in serial and parallel computations although the same numerical conditions were given. Based on the histograms of pressure measured at two points near the wall the statistical characteristics of numerical solution was not affected by the number of subdomains as much as the transient solution was dependent on the number of subdomains.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.293-299
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• 1995

A methodology is applied to identify tile learning trend related to the safety and availability of U.S. commercial nuclear power plants. The application is intended to aid in reducing likelihood of human errors. To assure that tile methodology ran be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation(TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting tile systems and for events caused by human deficiencies were used. Clustering analysis was used to identify the learning trend in multi-dimensional histograms. A computer rode is developed based on tile K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.12-14
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• 2006

This paper presents a novel scene change detection method using intra prediction mode and edge direction in H.264/AVC. When scene change occurs, there are less temporal correlation between frames, most of macro-blocks encoded in intra mode. Using this property, the method calculates the percentage of intra mode blocks in each predictive frame in order to get candidates of scene change frame. To further find scene change, we obtain edge histogram of each candidates by using eight prediction direction of intra prediction mode in H.264/AVC. We detect scene change frames with $\iota^1$-norm of edge histograms. The experimental results show that the method is efficient and robust.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.21-23
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• 2006

Due to development of Internet network environments and data compression techniques, the size and amount of multimedia data has greatly increased. They are compressed before transmission or storage. Dealing with these compressed data such as video retrieval or indexing requires the decoding procedure most of the time. In video retrieval and indexing a color histogram is one of the most frequently used tools. We propose a novel scheme for extracting color histograms from images transformed into the compressed domain using $8{times}8$ DCT(Discrete Cosine Transform). In this scheme an averaged version of original image is obtained by filtering DCT coefficients with a filter we destined.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.210-215
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• 2009

In this study event-related optical signals were extracted from the prefrontal cortexes using functional near infrared spectroscopy while subjects were carrying out 2-back working memory tasks. Four events such as start, yes, no, and error were considered based on the onsets of the stimulus, positive true responses, positive false responses, and negative responses in the 2-back working memory task, respectively. The optical signals recorded were analyzed by peri-event histograms and power spectrum distributions. The results showed specific characteristics of the event-related optical neuronal signals and an opened possibility of an application to control a non-invasive brain-computer interface system or an object of a virtual reality.

• International Journal of Contents
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• v.6 no.3
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• pp.5-9
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• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.2
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• pp.281-288
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• 2011

In this paper, a variable quantization coefficient scanning for macroblock considering the histogram value of previous macroblock's quantization coefficient is proposed. In order to scan a quantization coefficient, the proposed method makes $4{\times}4$ histograms for various prediction modes($16{\times}16$, $16{\times}8$, $8{\times}16$, $8{\times}8$) by adding 1 if the value of quantization coefficient is not 0. After a final mode decision procedure, the $4{\times}4$ histogram of the final mode will be sorted. Then, quantization coefficients in corresponding macroblock of the next frame are scanned using the sorted order. The experimental results show that the proposed scheme reduces the total bits by approximately 0.01~2.25% with similar PSNR performance compared with the previous method.