• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.81-86
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• 2005

It is not easy to find and extract a moving object from its background. The extraction method is specific as what it is and how its environment is. So recently the more general method which is less affected by its environmental elements is required. So, In this paper we report on the moving object extraction algorithm using the features of the interlaced-image-capturing method which is adopted in the CCD Camera, an afterimage for exposing time and the fact that an afterimage has same color level. Unlike much of existing algorithms it is use oかy one stationary picture to apply this algorithm.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.425-428
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• 2003

Recently, the IDS(Intrusion Detection System) using a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in the environment with abrupt illumination change. In this paper, we propose an effective moving edge extraction algorithm from a sequence image. The proposed algorithm extracts edge segments from current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of moving object in the noisy environment with abrupt illumination change.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.101-109
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• 2014

It is an essential element for the establishment of image processing related systems to find the exact contour from the image of an arbitrary object. In particular, if a vision system is established to inspect the products in the automated production process, it is very important to detect the contours for standardized shapes such lines and curves. In this paper, we propose a prototype adaptive dynamic contour model, p-Snake with improved contour extraction algorithms by adding the prototype energy. The proposed method is to find the initial contour by applying the existing Snake algorithm after Sobel operation is performed for prototype analysis. Next, the final contour of the object is detected by analyzing prototypes such as lines and circles, defining prototype energy and using it as an additional energy item in the existing Snake function on the basis of information on initial contour. We performed experiments on 340 images obtained by using an environment that duplicated the background of an industrial site. It was found that even if objects are not clearly distinguished from the background due to noise and lighting or the edges being insufficiently visible in the images, the contour can be extracted. In addition, in the case of similarity which is the measure representing how much it matches the prototype, the prototype similarity of contour extracted from the proposed p-ACM is superior to that of ACM by 9.85%.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.780-791
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• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.11-20
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• 2000

This paper presents the method of extracting the contour and shape parameters for moving object tracking in traffic scenes. The contour is extracted by applying difference image method in reduction image and the features are extracted from original image to grow the accuracy of tracking. We used features such as circle distribution, center moment, and maximum and minimum ratio. Data association problem is solved by these features. Kalman filters are used for moving object tracking on real time. The simulation results indicate that the proposed algorithm appears to generate feature vectors good enough for multiple vehicle tracking.

• Journal of Advanced Navigation Technology
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• v.11 no.1
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• pp.59-63
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• 2007

In this paper, an image segmentation algorithm is described to extract both the contour line and the inner grid patterns of body in case of ambiguous environment. The binary method using a threshold is used to extract image boundary. To reduce image noise, the $3{\times}3$ hybrid high pass filter adjusted for applying 3D information extraction of complicated shape object is proposed. This hybrid high pass filter algorithm can be applied to extract complicated shape object such as 3D body shape, CAD system, and factory automation since the processing time for image denoising is shorter than the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1861-1873
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• 1996

This paper describes the content-based image retrieval system that is implemented to retrieve images using constituent rate of lines and Bezier curves. We proposed the line and Bezier curve extraction algorithm which extracts lines and curve that are fitted on the contour information of images. For this extration, it was necessary to remove internal area of the proprocessed object within images and to approximate its contour to polygon, and proposed retrevial algorithm which gets the simularity using the consitituent rate of lines and curves and perform the simularity matching.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.683-695
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• 1996

In this paper, we implemented the content-based image retrieval system that indexes and retrieves the images by acquiring contour information of images and by extracting lines from the object. For this purpose, we proposed the advanced line extraction algorithm called FSLHT(Flexible SLHT) which covers drawback of SLHT(Straight Line Hough Transform)andapplied aDP(Dynamic Programming)algorithm to getadesirable similarity ofimages by lines. We estimated the contour features as a key value of sampled region to compensate for the problem that image contours are heavily depend on the noise. When performing the Hough transform we calculated the directionality based on the perceptual organization and transformed according to this direction to overcome the problem of time consuming and discontinuity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.268-275
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• 2002

This paper proposes a region-based segmentation algorithm fur extracting a video object by using the cost of potential of frame-difference energies. In the first step of a region-based segmentation using spatial intensity, each frame is segmented into a partition of homogeneous regions finely so that each region does not contain the contour of a video object. The fine partition is used as an initial partition for the second step of spatio-temporal segmentation. In spatio-temporal segmentation, the homogeneity cost for each pair of adjacent regions is computed which reflects the potential between the frame-difference energy on the common contour and the frame-difference energy of the lower potential region of the two. The pair of adjacent regions whose cost is minimal then is searched. The two regions of minimum cost ale merged, which result in updating the partition. The merging is recursively performed until only the contours remain which have Same difference energies of high potential. In the fecal step of post-processing, the video object is extracted removing the contours inside the object.