• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.671-678
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• 2005

In this paper, we suggest an algorithm that allows us to extract the important obbject from motion pictures and then replace the background with arbitrary images. The suggested technique can be used not only for protecting privacy and reducing the size of data to be transferred by removing the background of each frame, but also for replacing the background with user-selected image in video communication systems including mobile phones. Because of the relatively large size of image data, digital image processing usually takes much of the resources like memory and CPU. This can cause trouble especially for mobile video phones which typically have restricted resources. In our experiments, we could reduce the requirements of time and memory for processing the images by restricting the search area to the vicinity of major object's contour found in the previous frame based on the fact that the movement of major object is not wide or rapid in general. Specifically, we detected edges and used the edge image of the initial frame to locate candidate-object areas. Then, on the located areas, we computed the difference image between adjacent frames and used it to determine and trace the major object that might be moving. And then we computed the contour of the major object and used it to separate major object from the background. We could successfully separate major object from the background and replate the background with arbitrary images.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.233-241
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• 2014

This paper contributes to development of a new method for detecting rear-side vehicles and estimating the positions for blind spot region or providing the lane change information by using vision systems. Because the real image acquired during car driving has a lot of information including the target vehicle and background image as well as the noises such as lighting and shading, it is hard to extract only the target vehicle against the background image with satisfied robustness. In this paper, the target vehicle has been detected by repetitive image processing such as sobel and morphological operations and a Kalman filter has been also designed to cancel the background image and prevent the misreading of the target image. The proposed method can get faster image processing and more robustness rather than the previous researches. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• Journal of the Korean Society of Costume
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• v.51 no.2
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• pp.27-42
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• 2001

The objectives of this study are to give a definition and to find out the background and the pursuits of Saenghwal Hanbok. Conclusions are described as follows : 1. Saenghwal Hanbok and Gaeryang Hanbok are used together at the same tome, but they are different the background and the pursuits. Gaeryang Hanbok was pursued practical aspects - activities, simplification, sanitation, courtesy, economy, and diversity. And then Saenghwal Hanbok was added the pursuits of Minjung's image, traditional image, modern esthetic. 2. The background of Saenghwal Hanbok is divided into two group. One is the Minjung Hanbok in University, and the other is the recreated Hanbok in mass fashion. The former was effected to youth culture, political quarrel of culture movement, anti-government group. The Latter was a tendency toward reviving the tradition. 3. The characters of Saenghwal Hanbok were a national tradition, a resistance. the image of poor Minjung, a revival of the tradition, and a diversity and negotiation of post-modernism.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.834-847
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• 1996

A selective coding scheme is proposed that describes a method for coding image sequences distinguishing bits between background and target region. The suggested method initially estimates global motion parameters and local motion vectors. Then segmentation is performed with a hierarchical clustering scheme and a quadtree algorithm in order to divide the processing image into the backgraound and target region. Finally image coding is done by assigning more bits to the target region and less bits to background so that the target region may be reconstructed with high quality. Simulations show that the suggested algorithm performs well especially in the circumstances where background changes and target regionis small enough compared with that of background.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.615-618
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• 1999

In this paper, a object detection and tracking algorithm is presented which exhibits robust properties for image sequences with complex background. The proposed algorithm is composed of three parts: moving object detection, object tracking, and motion analysis. The moving object detection algorithm is implemented using a temporal median background method which is suitable for real-time applications. In the motion analysis, we propose a new technique for removing a temporal clutter, such as a swaying plant or a light reflection of a background object. In addition, we design a multiple vehicle tracking system based on Kalman filtering. Computer simulation of the proposed scheme shows its robustness for MPEG-7 test image sequences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.46-49
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• 2017

In this letter, we investigate the impact of image downsampling on the performance of background subtraction in Full-HD soccer videos. To this end, we evaluated the performance of background subtraction in terms of both accuracy and computational time. Furthermore, for the sake of completeness, we used two different background subtraction methods under the same experimental setup. For the quantitative comparison, we employed the F-measure and FPS(frames per second). We believe that this study serves as a practically useful benchmark for researchers and practitioners in developing a fast background subtraction algorithm adopted for building real-time intelligent soccer video analysis systems.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.92-101
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• 2005

It is difficult to detect the accurate detection which leads the camera it moves follows in change of the noise or illumination and Also, it could be recognized with backgound if the object doesn't move during hours. In this paper, the proposed method is updating changed background image as much as N＊M pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect moving by computing fixed distance pixel instead of operate all pixel. Also, set up minimum area of object to use boundary point of object abstracted through checking image pixel and motion detect of object. Therefore motion detection is available as is fast and correct without doing checking image pixel every Dame. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.