• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.71-79
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• 2017

Recently, Fire watching and dangerous substances monitoring system has been being developed to enhance various fire related security. It is generally assumed that fire flame extraction plays a very important role on this monitoring system. In this study, we propose the fire flame extraction method of Non-Residential Facilities based on core object extraction in image. A core object is defined as a comparatively large object at center of the image. First of all, an input image and its decreased resolution image are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent to boundaries of the image and the rest is not. Then core object regions and core background regions are selected from the inner region and the outer region, respectively. Core object regions are the representative regions for the object and are selected by using the information about the region size and location. Each inner region is classified into foreground or background region by comparing its values of a color histogram intersection of the inner region against the core object region and the core background region. Finally, the extracted core object region is determined as fire flame object in the image. Through experiments, we find that to provide a basic measures can respond effectively and quickly to fire in non-residential facilities.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.75-80
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• 2016

The process of extracting external boundary of an object is a very important process for recognizing an object in the image. The proposed extraction method consists of two processes: External Boundary Extraction and Thinning. In the first step, external boundary extraction process separates the region representing the object in the input image. Then, only the pixels adjacent to the background are selected among the pixels constituting the object to construct an outline of the object. The second step, thinning process, simplifies the outline of an object by eliminating unnecessary pixels by examining positions and interconnection relations between the pixels constituting the outline of the object obtained in the previous extraction process. As a result, the simplified external boundary of object results in a higher recognition rate in the next step, the object recognition process.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.11-23
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• 2011

In this paper, an accurate and fully automatic video object segmentation method is proposed for video conferencing systems in which the real-time performance is required. The proposed method consists of two steps: 1) accurate object extraction on the initial frame, 2) real-time object extraction from the next frame using the result of the first step. Object extraction on the initial frame starts with generating a cumulative edge map obtained from frame differences in the beginning. This is because we can estimate the initial shape of the foreground object from the cumulative motion. This estimated shape is used to assign the seeds for both object and background, which are needed for Graph-Cut segmentation. Once the foreground object is extracted by Graph-Cut segmentation, real-time object extraction is conducted using the extracted object and the double edge map obtained from the difference between two successive frames. Experimental results show that the proposed method is suitable for real-time processing even in VGA resolution videos contrary to previous methods, being a useful tool for immersive video conferencing systems.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.81-86
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• 2003

In this paper, we propose the tracking method of moving object which use extracted object by difference between background image and target image in fixed domain. As a extraction method of object, calculate not pixel of full image but predefined some edge pixel of image to get a position of new object. Since the center area Is excluded from calculation, the extraction time is efficiently reduced. To extract object in the predefined area, get a starting point in advance and then extract size of width and height of object. Central coordinate is used to track moved object.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.53-61
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• 2016

Extracting focused object with saliency map is still remaining as one of the most highly tasked research area around computer vision for it is hard to estimate. Through this paper, we propose enhanced object extraction method based on multi-channel saliency map which could be done automatically without machine learning. Proposed Method shows a higher accuracy than Itti method using SLIC, Euclidean, and LBP algorithm as for object extraction. Experiments result shows that our approach is possible to be used for automatic object extraction without any previous training procedure through focusing on the main object from the image instead of estimating the whole image from background to foreground.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.87-94
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• 2008

In this paper, an extraction method of objects of interest in the color images is proposed. It is possible to extract objects of interest from a complex background without any prior-knowledge based on the proposed method. For object extraction, Gator images that contain information of object location, are created by using Gator filter. Based on the images the initial location of attention windows is determined, from which image features are selected to extract objects. To extract object, I modify the previous method partially and apply the modified method. To evaluate the performance of propsed method, precision, recall and F-measure are calculated between the extraction results from propsed method and manually extracted results. I verify the performance of the proposed methods based on these accuracies. Also through comparison of the results with the existing method, I verily the superiority of the proposed method over the existing method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.115-121
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• 2016

Recently, it has become an important problem to extract semantic objects from videos, which are useful for improving the performance of video compression and video retrieval. In this thesis, an automatic extraction method of moving objects of interest in video is suggested. We define that an moving object of interest should be relatively large in a frame image and should occur frequently in a scene. The moving object of interest should have different motion from camera motion. Moving object of interest are determined through spatial continuity by the AMOS method and moving histogram. Through experiments with diverse scenes, we found that the proposed method extracted almost all of the objects of interest selected by the user but its precision was 69% because of over-extraction.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.163-170
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• 2007

In this paper, We propose a new technique for extraction of moving objects included in compressed video signal. Moving object extraction is used in several fields such as contents based retrieval and target tracking. In this paper, in order to extract moving object blocks, motion vectors and DCT coefficients are used selectively. The proposed algorithm has a merit that it is no need of perfect decoding, because it uses only coefficients on the DCT transform domain. We used three test video sequences in the computer simulation, and obtained satisfactory results.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.395-403
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• 2016

Recently, Image processing has been used in many areas. In the image processing techniques that a lot of research is tracking of moving object in real time. There are a number of popular methods for tracking an object such as HOG(Histogram of Oriented Gradients) to track pedestrians, and Codebook to subtract background. However, object extraction has difficulty because that a moving object has dynamic background in the image, and occurs severe lighting changes. In this paper, we propose a method of object extraction using depth image and color image features based on ROI(Region of Interest). First of all, we look for the feature points using the color image after setting the ROI a range to find the location of object in depth image. And we are extracting an object by creating a new contour using the convex hull point of object and the feature points. Finally, we compare the proposed method with the existing methods to find out how accurate extracting the object is.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.298-303
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• 2014

In this paper, we proposed a robust object extraction and tracking algorithm in the IR image sequence acquired in the sea environment. In order to extract size-invariant object, we detect horizontal and vertical edges by using DWT and combine it to generate saliency map. To extract object region, binarization technique is applied to saliency map. The correspondences between objects in consecutive frames are defined by the calculating minimum weighted Euclidean distance as a matching measure. Finally, object trajectories are determined by considering false correspondences such as entering object, vanishing objects and false object and so on. The proposed algorithm can find trajectories robustly, which has shown by experimental results.