• 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 Korea Convergence Society
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• v.6 no.6
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• pp.1-8
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• 2015

In this paper, we proposed image enhancement method using sharpening filter for improving the accuracy of object detection using the existing Grabcut algorithm. GrabCut algorithm is the excellent performance extracting an object within a rectangular window range, but it has the drawback of the inferior performance in image with no clear distinction between background and objects. So, in this paper, reinforcing the brightness and clarity through histogram equalization, and tightening the border of the object using the sharpening filter look better than that extracted result of existing GrabCut algorithm in a similar image of the object and the background. Based on improved Grabcut algorithm, it is possible to obtain an improved result in the image processing convergence technique of character recognition, real-time object tracking and so on.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2417-2422
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• 2015

Mean-Shift tracking using hue has a problem that it fail in the object tracking when background has similar hue to the object. This paper proposes an improved Mean-Shift tracking algorithm using new data instead of a hue. The new data is generated by adaptive mixture of hue and saturation which have low interrelationship . That is, the proposed algorithm selects a main attribute of color that is able to distinguish the object and background well and a secondary one which don't, and places their upper 4 bits on upper 4 bits and lower 4 bits on the mixture data, respectively. The proposed algorithm properly tracks the object, keeping tracking error maximum 2.0~4.2 pixel and average 0.49~1.82 pixel, by selecting the saturation as the main attribute of color under tracking environment that background has similar hue to the object.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.48-55
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• 2005

In this paper, we propose an efficient moving object tracking technique for multi-camera surveillance system. Color CCD cameras used in this system are network cameras with their own IP addresses. Input image is transmitted to the media server through wireless connection among server, bridge, and Access Point (AP). The tracking system sends the received images through the network to the tracking module, and it tracks moving objects in real-time using color matching method. We compose two sets of cameras, and when the object is out of field of view (FOV), we accomplish hand-over to be able to continue tracking the object. When hand-over is performed, we use MHI(Motion History Information) based on color information and M-bin histogram for an exact tracking. By utilizing MHI, we can calculate direction and velocity of the object, and those information helps to predict next location of the object. Therefore, we obtain a better result in speed and stability than using template matching based on only M-bin histogram, and we verified this result by an experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.760-765
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• 2009

Since the features in object-images such as color and shape cannot clearly express the characteristic of objects, those features lead to vagueness of object-image recognition. Recently there have been studied on object-image recognition based on knowledge base in order to reduce the vagueness. However, because images are represented by numerical information but knowledge bases are represented by conceptual information, combining two kinds of information is difficult. In this paper, we compose knowledge base by using ontology to reduce the gap between the two kinds of information, and propose a method for object-image recognition to reduce the vagueness by using information on inner-object. Moreover, we confirm the usefulness of the proposed method through the experiments on object-image recognition in fruit domain.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.547-555
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• 2014

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

• Journal of the Semiconductor & Display Technology
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• v.9 no.3
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• pp.65-69
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• 2010

The stereoscopic vision system is the algorithm to obtain the depth of target object of stereo vision image. This paper presents an efficient disparity matching method using nagao filter, octree color quantization and dynamic programming algorithm. we describe methods for performing color quantization on full color RGB images, using an octree data structure. This method has the advantage of saving a lot of data. We propose a preprocessing stereo matching method based on Nagao-filter algorithm using color information. using the nagao filter, we could obtain effective depth map and using the octree color quantization, we could reduce the time of computation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.95-102
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• 2005

In this paper, we propose a user friendly object-based image retrieval system using the interaction between cortex and hippocampus. Most existing ways of queries in content-based image retrieval rely on query by example or query by sketch. But these methods of queries are not adequate to needs of people's various queries because they are not easy for people to use and restrict. We propose a method of automatic color object extraction using CSB tree map(Color and Spatial based Binary をn map). Extracted objects were transformed to bit stream representing information such as color, size and location by region labelling algorithm and they are learned by the hippocampal neural network using the interplay between cortex and hippocampus. The cells of exciting at peculiar features in brain generate the special sign when people recognize some patterns. The existing neural networks treat each attribute of features evenly. Proposed hippocampal neural network makes an adaptive fast content-based image retrieval system using excitatory learning method that forwards important features to long-term memories and inhibitory teaming method that forwards unimportant features to short-term memories controlled by impression.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.36-36
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• 2013

Globular clusters (GCs) are among the oldest stellar objects in the universe and provide valuable constraints on many aspects of galaxy evolution. GC systems typically exhibit bimodal color distributions the phenomenon of which has been a major topic in the area of GC research. GC color bimodality established a paradigm where scenarios to explain its origin require two GC groups with different formation origins. The GC division, asserted mainly by photometric color bimodality so far, has been viewed as the presence of two distinct metallicity subgroups within individual galaxies. In this study, we make use of spectroscopy of GC systems associated with two giant galaxies, M31 (the Andromeda) and M87 (NGC 4486), to investigate the GC bimodality and the underlying metallicity distributions. Recent spectroscopy on the globular cluster (GC) system of M31 with unprecedented precision witnessed a clear bimodality in absorption-line index distributions of old GCs. Given that spectroscopy is a more detailed probe into stellar population than photometry; the discovery of index bimodality may point to the very existence of dual GC populations. However, here we show that the observed spectroscopic dichotomy of M31 GCs emerges due to the nonlinear nature of metallicity-to-index conversion and thus one does not necessarily have to invoke two separate GC subsystems. We present spectra of 130 old globular clusters (GCs) associated with the Virgo giant elliptical galaxy M87, obtained using the Multi-Object Spectrography (MOS) mode of Faint Object Camera and Spectrograph (FOCAS) on the Subaru telescope. M87 GCs with reliable metallicity measurements exhibit significant inflection along the color-metallicity relations, through which observed color bimodality is reproduced from a broad, unimodal metallicity distribution. Our findings lend further support to this new interpretation of the GC color bimodality, which could change much of the current thought on the formation of GC systems and their host galaxies.