• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.265-268
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• 2001

In content-based video data retrieval, the representative-frame is usually used. To do that, the skill of detection for scene change is needed. Generally the color histogram comparison is used, but sensitive to light variation and tends to miss the scene change of similar color histogram. This paper shows how to use both color histogram comparison and entropy to prevent the false-positive of scene change occurred by light variation. At the experiments, il is more powerful to light variation to use both color histogram comparison entropy than to use only color histogram comparison.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.127-133
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• 2007

We propose new reversible watermarking method for images. Being reversibility, original image and watermarked message should be recovered exactly. We propose different technique for hiding data to pairs. We use new type of histogram (pair histogram), which shows frequencies of each pair in image. We use histogram shift method for data embedding to pairs. We also propose improved version of method which allow hiding data with good performance for high capacities. This algorithm has better result compare to Tian's difference expansion method based on the Haar wavelet decomposition. For proposed algorithm capacity is higher under same PSNR.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.23-26
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• 2003

The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our four-legged robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1076-1079
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• 2003

The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our simulation program and tested..

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.1
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• pp.30-38
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• 2014

The purpose of this study is to build and manage environment models with line segments from sonar range data on obstacles in unknown and varied environments. The proposed method therefore employs a two-stage data-transform process in order to extract environmental line segments from range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to form a two-dimensional local histogram grid. In the second stage, a line histogram extracted from a local histogram grid is based on a Hough transform, and matching serves as a means of comparing each of the segments on a global line segments map against the line segments to detect the degree of similarity in the overlap, orientation, and arrangement. Each of these tests is formulated by comparing one of the parameters in the segment representation. After the tests, new line segments can be found at maximum-density cells in the line histogram, and they are composed onto the global line segment map. The proposed technique is demonstrated in experiments in an indoor environment.

• 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.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.8
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• pp.369-374
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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 decompression procedure in most cases. This causes additional computations and increases the processing time. In various applications a histogram is one of the most frequently used tools. Efficiency of extracting such histograms will drop down if decompression is involved. We propose a novel scheme for extracting histograms from images that are transformed into the compressed domain by 8x8 DCT(Discrete Cosine Transform). In this scheme an averaged version of original image is obtained by a simple linear combination of DCT coefficients with the sets of coefficients we designed.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.63-69
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• 2023

In this paper, we propose an effective reversible data hiding technique that increases the confidential data hiding amount of the NSAS technique itself by utilizing the characteristics of image. The proposed technique shifts the histogram using multiple zeros of the histogram and hides 2 bits of confidential data at each peak point. Using the proposed technique, the amount of confidential data that can be hidden is doubled compared to the existing technique, and high-quality stego-image can be created. Confidential data can be restored without loss from the stego- image, and the original cover image can be restored without loss. Through experiments, it was confirmed that the proposed technique can hide twice as much confidential data than the existing technique, and the image quality of the stego-image is very good with a maximum of 39.75dB.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.