• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.271-277
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• 2017

In the image processing, foreground image extraction is mainly applied to recognize a moving object or an object. In the game, the objects included in the foreground image can be mainly characters, non player characters, items, and the like. These objects can be the player's primary concern with objects that are the target of players' movement, attack, defense, and collection. In this background, this research is a study to extract players' interest areas. To this end, first, the foreground image is extracted. Second, the extracted foreground image is accumulated for a certain period of time, and the image is displayed as a result image. The accumulated foreground image according to the play time helps to know the location and frequency of screen appearance of game objects. This study can help players design their interest areas and design an efficient UX/UI.

• Journal of the Korea Computer Graphics Society
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• v.20 no.3
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• pp.1-8
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• 2014

We propose a method to compose a foreground object into a background image, where the foreground object is a part (or a region) of an image taken by a front-facing camera and the background image is a whole image taken by a back-facing camera in a smart phone at the same time. Recent high-end cell-phones have two cameras and provide users with preview video before taking photos. We extract the foreground object that is moving along with the front-facing camera using the optical flow during the preview. We compose the extracted foreground object into a background image using a simple image composition technique. For better-looking result in the composed image, we apply a border smoothing technique using a weighted-border mask to blend transparency from background to foreground. Since constructing and grouping pixel-level dense optical flow are quite slow even in high-end cell-phones, we compute a mask to extract the foreground object in low-resolution image, which reduces the computational cost greatly. Experimental result shows the effectiveness of our extraction and composition techniques, with much less computational time in extracting the foreground object and better composition quality compared with Poisson image editing technique which is widely used in image composition. The proposed method can improve limitedly the color bleeding artifacts observed in Poisson image editing using weighted-border blending.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.176-178
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• 2005

In the images of a video camera employed for surveillance, detecting targets by extracting foreground image is of great importance. The foreground regions detected, however, include not only moving targets but also their shadows. This paper presents a novel technique to detect shadow pixels in the foreground image of a video camera. The image characteristics of video cameras employed, a web-cam and a CCD, are first analysed in the HSV color space and a pixel-level shadow detection technique is proposed based on the analysis. Compared with existing techniques where unified criteria are used to all pixels, the proposed technique determines shadow pixels utilizing a fact that the effect of shadowing to each pixel is different depending on its brightness in background image. Such an approach can accommodate local features in an image and hold consistent performance even in changing environment. In experiments targeting pedestrians, the proposed technique showed better results compared with an existing technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.441-445
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• 2011

In this paper, we propose an algorithm for generating panoramic videos using fixed multiple cameras. We estimate a background image from each camera. Then we calculate perspective relationships between images using extracted feature points. To eliminate stitching errors due to different image depths, we process background images and foreground images separately in the overlap regions between adjacent cameras by projecting regions of foreground images selectively. The proposed algorithm can be used to enhance the efficiency and convenience of wide-area surveillance systems.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.317-321
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• 2013

This paper proposes a detection algorithm for view reversal in a stereoscopic video using a disparity map and motion vector field. We obtain the disparity map of a stereo image was obtained using a specific stereo matching algorithm and classify the image into the foreground and background. Next, the motion vector field of the image on a block basis was produced using a full search algorithm. Finally, the stereo image was considered to be reversed when the foreground moved toward the background and the covered region was in the foreground. The proposed algorithm achieved a good detection rate when the background was covered sufficiently by its moving foreground.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.243-248
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• 2013

In this paper, depth of foreground is analysed by focus and color analysis grouping for 2D/3D video conversion and depth of foreground progressing method is preposed by using focus and motion information. Candidate foreground image is generated by estimated movement of image focus information for extracting foreground from 2D video. Area of foreground is extracted by filling progress using color analysis on hole area of inner object existing candidate foreground image. Depth information is generated by analysing value of focus existing on actual frame for allocating depth at generated foreground area. Depth information is allocated by weighting motion information. Results of previous proposed algorithm is compared with proposed method from this paper for evaluating the quality of generated depth information.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.449-455
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• 2014

Detection of moving objects is a fundamental task in most of the computer vision applications, such as video surveillance, activity recognition and human motion analysis. This is a difficult task due to many challenges in realistic scenarios which include irregular motion in background, illumination changes, objects cast shadows, changes in scene geometry and noise, etc. In this paper, we propose an foreground extraction algorithm based on codebook, a database of information about background pixel obtained from input image sequence. Initially, we suppose a first frame as a background image and calculate difference between next input image and it to detect moving objects. The resulting difference image may contain noises as well as pure moving objects. Second, we investigate a codebook with color and brightness of a foreground pixel in the difference image. If it is matched, it is decided as a fault detected pixel and deleted from foreground. Finally, a background image is updated to process next input frame iteratively. Some pixels are estimated by input image if they are detected as background pixels. The others are duplicated from the previous background image. We apply out algorithm to PETS2009 data and compare the results with those of GMM and standard codebook algorithms.

• International Journal of Contents
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• v.9 no.1
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• pp.6-10
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• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.479-486
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• 2016

In this paper, the algorithm which can convert a 2D image into a 3D Model will be discussed. The 2D picture drawn by a user is scanned for image processing. The Canny algorithm is employed to find the contour. The waterfront algorithm is proposed to find foreground image area. The foreground area is segmented to decompose the complex shapes into simple shapes. Then, simple segmented foreground image is converted into 3D model to become a complex 3D model. The 3D conversion formular used in this paper is also discussed. The generated 3D model data will be useful for 3D animation and other 3D contents creation.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.77-80
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• 2001

In general, the informations of the inner image that user interested in are limited to a special domain. In this paper, as using Wavelet Transform for dividing image into high frequency and low frequency, We can separate foreground including many data. After calculating object boundary of separated part, We extract special features using Color Coherence Vector. According to results of this experiment, the method of comparing data extracting foreground features is more effective than comparing data extracting features of entire image when we extract the image user interested in.