• Korean Journal of Applied Biomechanics
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• v.20 no.2
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• pp.149-154
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• 2010

Intentional draw and fade shots could be good weapons for lowering golf score. But how to make such shots? To investigate deterministic variables generating different projectile paths of shots in square stance was the purpose of this study. Ten right-handed male collegiate athletes, showing 1.3 of averaged handicap, participated in this study. They were asked to intentionally perform three different shots such as the straight shot(control condition), draw shot, and fade shot. Swing path, pelvis rotation angle, thorax rotation angle and left forearm supination angle were determined for dependent variables on impact event at each trial. For statistical analysis one-way repeated measures ANOVA were used. The results showed that swing path was one of main factor making differences among three kind of shots. Straight shot vs. Draw shot, Straight shot vs. Fade shot and Draw shot vs. Fade shot showed differences on swing path. And left forearm supination angle revealed significant difference between draw shot and fade shot, showing a significant larger angle of draw shot than fade shot. No other significant difference was detected for the other variables. We found that the shot characteristics were influenced primarily by swing path and left forearm supination angle.

• Korean Journal of Applied Biomechanics
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• v.22 no.3
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• pp.269-276
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• 2012

The purpose of This study's aim is to examine the difference in the changes of body segment movement, variables for ball quality, and carry at golf driver swing according to the ball quality using comparative analysis. Regarding the impact variables according to the ball quality using the track man and carry, club speed was the fastest at draw shot, ball speed was the fastest at straight shot, and smash factor was the lowest at draw shot. About the vertical launch angle, the fade shot showed the highest launch angle while the max height of the ground and ball was the highest at fade shot. And carry was the longest at draw shot. For the flight time, it was the longest at draw shot. The landing angle was the largest at fade shot. About the club head position change and trajectory, at the overall event point, the fade shot drew a more outer trajectory at the point of the follow through(E6) than the straight or draw shot. Regarding the angular speed of shoulder rotation, at the overall event point, the fade shot showed the greatest angular speed change in the follow through(E6). Also, about the angular speed of pelvic rotation, at the overall event point, the draw shot showed the greatest angular speed change at the point of down swing(E4). Concerning the stance angle change, both straight and fade shots were open as the concept of open stance whereas the draw shot was close as that of close stance. Regarding the previous study, the most important factor of deciding Ball Quality is the club face angle's open and close state at Impact. In short, the Ball Quality and carry were decided by this factor.

• Korean Journal of Applied Biomechanics
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• v.19 no.2
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• pp.311-322
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• 2009

Using the 3-D analysis, this study winpared and analyzed the 'fade-shot swing' which is one of the golf technique. The subjects of this study were 3 male pro golfers they experimented with only a 7 iron. The purpose was to enhance their performance by producing the important kinematical parameters, finding out the features in them and providing the data to a coach and players. As a result, the position of the club head showed from the outside orbit to the inside orbit. When position of the center of mass moved backwards, the probability of the failure of the fade-shot increased. Cocking angle had an effect on easing the wrist for a smooth follow-through after the impact. It showed that the changes in the shoulder movement was made first and followed by the waist. The hip joint angular velocity achieved a smooth fade-shot motion due to the hitting delay also the velocity of the club-head was faster when uncocking was released at the time of impact.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5902-5909
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• 2011

In this paper, we propose a new method to detect abrupt and gradual shot transitions of video data by using certainty factors. The abrupt transitions denotes cuts and the gradual transitions fade in, fade out, dissolve, horizontal wipes, vertical wipes, Barn Doors, and Iris Rounds. The suggested method first extracts representative features for each shot transition and determines corresponding shot transitions by integrating all the extracted features and inferring adequate transitions. To verify the performance of the proposed shot transition method, experimental results show that the suggested method can detect shot transitions more accurately than existing methods.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.207-210
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• 2000

본 논문에서는 신호를 해석하는데 유용한 웨이블렛 변환을 적용하여 장면전환 요소 중 cut과 fade를 검출하는 알고리즘을 제안한다. 제안된 방법은 웨이블렛 저대역 부밴드로부터 각 프레임의 히스토그램을 구한 후 이전 프레임과 현재 프레임사이의 히스토그램 차를 구하여 이 값이 임계값 이상이면 급격한 장면전환(abrut shot transition)인 cut으로 분류한다. 다음으로 페이드인(fade in)이나 페이드 아웃(fade out)등 컷의 지점이 불분명한 점진적 장면전환(gradual scene transition)을 검출하기 위하여 고대역 부밴드에서 추출한 에지성분에 모멘트를 계산하여 인접한 프레임 사이의 변동율을 분석하여 값이 증가하면 페이드 인을 검출하고 반면에 감소하면 페이드 아웃을 검출하게된다. 성능평가를 위하여 실제의 비디오 분할에 적용한 결과 웨이블렛 적용 방법론이 매우 높은 Precision을 갖는다는 것을 알 수 있으며 윤곽정보에 모멘트 정보를 더함으로써 기존의 방법보다 정확한 페이드(fade) 구간을 검출할 수 있었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.195-198
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• 2000

비디오에서 적은 수의 대표 화면으로 그 비디오의 내용을 요약할 수 있다는 것은 효율적인 비디오 브라우징 및 비디오 검색에 있어서 매우 중요하다. 다양한 종류의 셔트(shot) 추출 방법이 제시되어 왔다. 다양한 종류의 셔트 추출 방법 중에서 칼라 히스토그램을 이용하는 방법이 가장 많이 사용되어 왔다. 그러나 칼라 히스토그램을 이용하는 방법은 fade effect, zoom effect 등과 같이 특별한 효과가 들어있는 비디오에서는 적절하지 못한 결과를 종종 초래한다. 이 논문에서는 블록을 이용한 fade와 zoom 효과가 있는 영역을 검출하는 방법을 제시한다. 대부분의 칼라 히스토그램 방법은 인접한 프레임간 또는 일정한 거리가 떨어져 있는 프레임간의 차이를 이용하였다. 이 논문에서는 차이를 구하고자 하는 프레임간의 거리를 변동시기는 방법을 이용하여 구함으로써 그 성능을 개선하였고, 또한 단순히 두 프레임만을 비교하는 것이 아니라 일정한 수의 프레임을 그룹핑 하여 하나의 블록으로 만들고, 그 블록에서 히스토그램 차이의 평균 및 중간 값을 이용하면 hard cut과 fade같은 효과가 한 블록 내에 같이 있는 경우 더욱 효과적으로 셔트를 추출할 수 있다.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1252-1261
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• 2011

In this paper, we propose the comparison method of pattern similarity for video segmentation algorithm. The shot boundary type is categorized as 2 types, abrupt change and gradual change. The representative examples of gradual change are dissolve, fade-in, fade-out or wipe transition. The proposed method consider the problem to detect shot boundary as 2-class problem. We concentrated if the shot boundary event happens or not. It is essential to define similarity between frames for shot boundary detection. We proposed 2 similarity measures, within similarity and between similarity. The within similarity is defined by feature comparison between frames belong to same shot. The between similarity is defined by feature comparison between frames belong to different scene. Finally we calculated the statistical patterns comparison between the within similarity and between similarity. Because this measure is robust to flash light or object movement, our proposed algorithm make contribution towards reducing false positive rate. We employed color histogram and mean of sub-block on frame image as frame feature. We performed the experimental evaluation with video dataset including set of TREC-2001 and TREC-2002. The proposed algorithm shows the performance, 91.84% recall and 86.43% precision in experimental circumstance.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.403-412
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• 2005

In this paper, we propose an effective method for detecting and classifying shot transitions in video sequences. The proposed method detects and classifies shot transitions including cuts, fades and dissolves by compensating camera operations in video sequences, so that our method prevents false positives resulting from camera operations. Also, our method eliminates local moving objects in the process of compensating camera operations, so that our method prevents errors resulting from moving objects. In the experiments, we show that our shot transition approach can work as a promising solution by comparing the proposed method with previously known methods in terms of performance.

• 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.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.611-618
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• 2003

In this paper, we describe a fuzzy inference approach for detecting and classifying shot transitions in video sequences. Our approach basically extends FAM (Fuzzy Associative Memory) to detect and classify shot transitions, including cuts, fades and dissolves. We consider a set of feature values that characterize differences between two consecutive frames as input fuzzy sets, and the types of shot transitions as output fuzzy sets. The inference system proposed in this paper is mainly composed of a learning phase and an inferring phase. In the learning phase, the system initializes its basic structure by determining fuzzy membership functions and constructs fuzzy rules. In the inferring phase, the system conducts actual inference using the constructed fuzzy rules. In order to verify the performance of the proposed shot transition detection method experiments have been carried out with a video database that includes news, movies, advertisements, documentaries and music videos.