• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.829-836
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• 2018

This study attempts to detect a shot boundary in films(or dramas) based on the length of a sequence. As films or dramas use scene change effects a lot, the issues regarding the effects are more diverse than those used in surveillance cameras, sports videos, medical care and security. Visual techniques used in films are focused on the human sense of aesthetic therefore, it is difficult to solve the errors in shot boundary detection with the method employed in surveillance cameras. In order to define the errors arisen from the scene change effects between the images and resolve those issues, the mixed algorithm based upon color histogram, edge histogram, and optical flow was implemented. The shot boundary data from this study will be used when analysing the configuration of meaningful shots in sequences in the future.

• Structural Engineering and Mechanics
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• v.29 no.6
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• pp.709-729
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• 2008

This paper describes a three-dimensional dynamic finite element analysis of two/multiple shots impacting on a metallic component. The model is validated against a published numerical study. An extensive parametric study is conducted to investigate the effect of shot impacting with overlap on the resulting residual stress profile within the component, including time interval between shot impacts, separation distance between the impacting points, and impacting velocity of successive shots. Several meaningful conclusions can be drawn regarding the effect of shot impacting with overlap.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.510-520
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• 2000

For content-based video indexing and retrieval, it is necessary to segment video data into video shots and then select key frames or representative frames for each shot. However, it is very difficult to select key frames automatically because the task of selecting meaningful frames is quite subjective. In this paper, we propose a new approach in selecting key frames based on visual contents such as region information and their temporal variations in the shot. First of all, we classify video shots into panning shots, zooming shots, tilting shots or no camera motion shots by detecting camera motion information in video shots. Then, in each category, we apply appropriate fuzzy rules to select key frames based on meaningful content in frame. Finally, we control the number of key frames in the selection process by adjusting the degree of detail in representing video shots.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.102-112
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• 2000

The previous video structuring techniques are mainly limited to shot or shot group level. However, the shot level structure couldn't provide semantics within a video. So, researches on high level structuring are going on for getting over the drawbacks of shot level structure, recently. To overcome the drawbacks of shot level structure, we propose video scene segmentation technique based on color and motion features. For considering various color distribution, each shot is divided into sub-shots based on color feature. A key frame is extracted from each sub-shot. The motion feature in a shot is extracted from MPEG-1 video's motion vector. Moreover adaptive weights based on motion's property in search range are applied to color and motion features. The experiment results of proposed technique show the excellence in view of the over-segmentation and the reflection of semantics, comparing with those of previous techniques. The proposed technique decomposes video into meaningful hierarchical structure and provides video browsing or retrieval based on scene.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.63-70
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• 2020

In this paper, we propose a globally successful animation rhythm type. Based on the study of spatial variables in Campbell's heroic narrative and the study on the duration of shots in the video, the rhythm of the shot according to the spatial narrative of was analyzed. Through this, three conclusions were drawn. First, the basis for the visualization of narrative intensity is possible through the shot density of narrative, Second, a shot rhythm type was presented, which was presented as an ascending type, a descending type, a mountain type, a depressed type, and a complex type, and the characteristics of the narrative were analyzed. Third, the strength of the shot rhythm shown in the hero narrative spatial map model was divided into top, middle, and bottom, and the measurement criteria of narrative strength were presented. This study is meaningful in that it visualized and typified artistic emotions as objective data in the flow of animation content focused on philosophical and qualitative methods.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.402-411
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• 2000

It is an important step in video indexing and retrieval to detect shot boundaries on video data. Some approaches are proposed to detect shot changes by computing color histogram differences or the variances of DCT coefficients. However, these approaches do not consider the content or meaningful features in the image data which are useful in high level video processing. In particular, it is desirable to detect these features from compressed video data because this requires less processing overhead. In this paper, we propose a new method to detect shot boundaries from MPEG data using region flow and color information. First, we reconstruct DC images and compute region flow information and color histogram differences from HSV quantized images. Then, we compute the points at which region flow has discontinuities or color histogram differences are high. Finally, we decide those points as shot boundaries according to our proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.881-887
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• 2007

In this paper, we proposed a method for content based automatic summarization of basketball game videos. For meaningful summary, we used the score information in basketball videos. And the score information is obtained by recognizing the digits on the score caption and analyzing the variation of the score. Generally, important events of basketball are the 3-point shot, one-sided runs, the lead changes, and so on. We have detected these events using score information and made summaries and highlights of basketball video games.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.738-744
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• 2007

In this paper, we proposed a method for content based automatic summarization of basketball game videos. For meaningful summary, we used the score information in basketball videos. And the score information is obtained by recognizing the digits on the score caption and analyzing the variation of the score. Generally, important events of basketball are the 3-point shot, one-sided runs, the lead changes, and so on. We have detected these events using score information and made summaries and highlights of basketball video games.

• 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.

• Korean Journal of Applied Biomechanics
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• v.19 no.2
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• pp.287-296
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• 2009

This is written to present basic shot information among iron clubs and information of weight shift, because previous study conducted on all clubs was rut enough. This article is about shot analysis of iron club 3,4,5,6,7,8,9 and weight movement, conducted on four skilled golf players and four students who major in golf and are less than three years. The analysis of long and short irons showed statistically meaningful different results for all the participants but iron clubs of 3,4,5 didn't in head speed and ball speed. The skilled group showed better results in head speed, ball speed and direction, Launch angle than the unskilled group. In weight shift movement analysis, the unskilled group moved, on back swing top, their more weight(65.36%) but the skilled group was on their left foot (70.21%)when clubs were on impact moment. It shows that the skilled group put on power more efficiently by moving weight.