• 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 Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.141-148
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• 1994

This paper presents a new method for reducing the blocking artifacts there by increasing the prediction gains of the block-based motion compensation keeping the amount of the motion information to be transmitted intact. The new method improves the motion compensation along the edges of moving objects by segmenting the motion field at the pixel resolution based on the model that the motion compensated image is the maximum a poseriori estimate of the current frame.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.81-87
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• 1999

In this research, we propose a field mismatch compensation method for interlaced scan image and a image restoration technique for removing motion blur. In order to compensate field mismatch, the edge classification-based linear interpolation technique and the method using the object-based motion compensation are described. We also propose an edge estimation method and an motion-based image segmentation algorithm. For removing motion blur, we adopt an adaptive iterative image restoration method using the motion-based segmentation result to improve the quality of restored image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2669-2688
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• 2016

An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.817-823
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• 2004

This paper describes a three dimensional motion recognition algorithm and a system which adopts the algorithm for non-contact human-computer interaction. From sequence of stereos images, five feature regions are extracted with simple color segmentation algorithm and then those are used for three dimensional locus calculation precess. However, the result is not so stable, noisy, that we introduce principal component analysis method to get more robust motion recognition results. This method can overcome the weakness of conventional algorithms since it directly uses three dimensional information motion recognition.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1129-1132
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• 2005

In this paper, we proposed real-time stereoscopic image conversion algorithm using object segmentation and motion parallax. The proposed algorithm separates objects using luminance of image, extracts moving object among objects of the image using motion parallax and generates depth map. Parallax process is done based on the depth map. The proposed method has been evaluated using visual test and APD(Absolute Parallx Difference) for comparing the stereoscopic image of the proposed method with that of MTD. The proposed method offers realistic stereoscopic conversion effect regardless of the direction and velocity of the 2-D image.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.1
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• pp.47-54
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• 2013

This work deals with automatic motion detection for with surveillance tracking that aims to provide high-lighting movable objects which is discriminated from moving backgrounds such as moving trees, etc. For this aim, we perform a false background region detection together with an initial foreground detection. The false background detection detects the moving backgrounds, which become eliminated from the initial foreground detection. This false background detection is done by performing the bimodal segmentation on a deformed image, which is constructed using the information of the dominant colors in the background.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.88-97
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• 2000

This paper presents motion estimation method using region segmentation information Motion estimation which is very difficult to be implemented only by software because of intensive computation cost, is implemented by special-purpose hardware in real-time applications In this paper, we propose region based motion estimation algorithm which can reduce the computation cost by using region segmentation information and setting the variable search window compared with FSMA algorithm Secondly, another proposed algorithm is to segment semantic region like face for selective coding and transfer of semantic region using segmented region information This work alms to improving the subjective quality of skin color region or face region m the picture that has slow motion and IS mainly composed of one or two speakers of video conference and video telephony applications.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.671-680
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• 2008

Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where ow line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of repeated frames within temporal distances, we consider similarities between neighboring frames as well as all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.