• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.163-170
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• 2013

In general, images of outdoor scenes often contain degradation due to dust, water drop, haze, fog, smoke and so on, as a result they cause the contrast reduction and color fading. Haze removal is not easier problem due to the inherent ambiguity between the haze and the underlying scene. So, we propose a novel method to solve single scene dehazing problem using the region segmentation based on graph algorithm that has used a gradient value as a cost function. We segment the scene into different regions according to depth-related information and then estimate the global atmospheric light. The medium transmission can be directly estimated by the threshold function of graph-based segmentation algorithm. After estimating the medium transmission, we can restore the haze-free scene. We evaluated the degree of the visibility restoration between the proposed method and the existing methods by calculating the gradient of the edge between the restored scene and the original scene. Results on a variety of outdoor haze scene demonstrated the powerful haze removal and enhanced image quality of the proposed method.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.37-46
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• 2010

In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.127-136
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• 2003

In this paper, we propose a robust object tracking algorithm based on model and edge, using deformed template and Level-Set theory. The proposed algorithm can track objects in case of background variation, object flexibility and occlusions. First we design a new potential difference energy function(PDEF) composed of two terms including inter-region distance and edge values. This function is utilized to estimate and refine the object shape. The first step is to approximately estimate the shape and location of template object based on the assumption that the object changes its shape according to the affine transform. The second step is a refinement of the object shape to fit into the real object accurately, by using the potential energy map and the modified Level-Set speed function. The experimental results show that the proposed algorithm can track non-rigid objects under various environments, such as largely flexible objects, objects with large variation in the backgrounds, and occluded objects.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.633-638
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• 2005

The extraction of a face is a very important part for human interface, biometrics and security. In this paper, we applies DCM(Dilation of Color and Motion) filter and Active Contour Models to extract facial outline. First, DCM filter is made by applying morphology dilation to the combination of facial color image and differential image applied by dilation previously. This filter is used to remove complex background and to detect facial outline. Because Active Contour Models receive a large effect according to initial curves, we calculate rotational degree using geometric ratio of face, eyes and mouth. We use edgeness and intensity as an image energy, in order to extract outline in the area of weak edge. We acquire various head-pose images with both eyes from five persons in inner space with complex background. As an experimental result with total 125 images gathered by 25 per person, it shows that average extraction rate of facial outline is 98.1% and average processing time is 0.2sec.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.191-197
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• 2012

In this paper we present creating 3D image from 2D image by extract initial depth values calculated from focal values. The initial depth values are created by using the extracted focal information, which is calculated by the comparison of original image and Gaussian filtered image. This initial depth information is allocated to the object segments obtained from normalized cut technique. Then the depth of the objects are corrected to the average of depth values in the objects so that the single object can have the same depth. The generated depth is used to convert to 3D image using DIBR(Depth Image Based Rendering) and the generated 3D image is compared to the images generated by other techniques.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.947-954
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• 2017

In case of a fire in urban large structures such as super high-rise buildings, evacuation guidance must be provided to the occupants in order to minimize human deaths and injuries. Therefore, it is essential to provide emergency evacuation guidance when a major fire occurs. In order to effectively support evacuation guidance, it is important to identify major items such as fire location, occupant position, escape route, etc. Also, it is important to quickly identify evacuation areas where residents can safely evacuate from a fire. In this paper, we analyze the CCTV video and propose a method of measuring visibility of the evacuation zone from the smoke caused by the fire in order to determine the safety of evacuation area. To do this, we first extract the background video from the smoke video to measure the visibility of the specific area due to smoke. After generating an edge-extracted image for the extracted background video, the degree of visibility is measured by calculating the change in the edge strength due to smoke.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.12-21
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• 2006

In this paper, we propose a technique for extracting attentive objects in images using feature maps, regardless of the complexity of images and the position of objects. The proposed method uses feature maps with edge and color information in order to extract attentive objects. We also propose a reference map which is created by integrating feature maps. In order to create a reference map, feature maps which represent visually attentive regions in images are constructed. Three feature maps including edge map, CbCr map and H map are utilized. These maps contain the information about boundary regions by the difference of intensity or colors. Then the combination map which represents the meaningful boundary is created by integrating the reference map and feature maps. Since the combination map simply represents the boundary of objects we extract the candidate object regions including meaningful boundaries from the combination map. In order to extract candidate object regions, we use the convex hull algorithm. By applying a segmentation algorithm to the area of candidate regions to separate object regions and background regions, real object regions are extracted from the candidate object regions. Experiment results show that the proposed method extracts the attentive regions and attentive objects efficiently, with 84.3% Precision rate and 81.3% recall rate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.105-114
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• 2011

The snake algorithm is widely adopted to track objects by extracting the active contour of the object from background. However, it fails to track the target converging to the background if there exists background whose gradient is greater than that of the pixels on the contour. Also, the contour may shrink when the target moves fast and the snake algorithm misses the boundary of the object in its searching window. To alleviate these problems, we propose an improved algorithm that can track object contour more robustly. Firstly, we propose two external energy functions, the edge energy and the contrast energy. One is designed to give more weight to the gradient on the boundary and the other to reflect the contrast difference between the object and background. Secondly, by computing the motion vector of the contour from the difference of the two consecutive frames, we can move the snake pointers of the previous frame near the region where the object boundary is probable at the current frame. Computer experiments show that the proposed method is more robust to the complicated background than the previously known methods and can track the object with fast movement.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.83-92
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• 2009

In this paper, an extended snake algorithm using color variance energy is proposed for segmenting an interest object in color image. General snake algorithm makes use of energy in image to segment images into a interesting area and background. There are many kinds of energy that can be used by the snake algorithm. The efficiency of the snake algorithm is depend on what kind of energy is used. A general snake algorithm based on active contour model uses the intensity value as an image energy that can be implemented and analyzed easily. But it is sensitive to noises because the image gradient uses a differential operator to get its image energy. And it is difficult for the general snake algorithm to be applied on the complex image background. Therefore, the proposed snake algorithm efficiently segment an interest object on the color image by adding a color variance of the segmented area to the image energy. This paper executed various experiments to segment an interest object on color images with simple or complex background for verifying the performance of the proposed extended snake algorithm. It shows improved accuracy performance about 12.42 %.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1280-1286
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• 2001

In this Study, we try to suggest a system that recognize worker's regular motion more effectively First, based on frame difference that separates still background from movable object to video that make a film of worker's motion. The next, with edge detection, estimating the center of motion could recognize continuous motion. By action cognition system that design in this research films worker's action using fixed CCTV to supplement problem of action awareness system that is applied in existent industry spot, various mountings to get action information minimized. Also, shorten session that need in awareness enforcing action awareness through image subtraction and edge detection between frame to reduce time necessary to draw worker's body part special quality, expense designed inexpensive action cognition system as being efficient.