• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.59-64
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• 2001

In this paper it was proposed method that solve problems of method to segment region of zero disparity and algorithm that extract binocular disparity to estimate position of object by vergence movement of moving stereo cameras experimented to compare those. There was not change of density value almost in region that change of critcal value was not found almost in image, because a high critical value was set so that critical value may be kipt changelessly about all small regions in studied treatise so far. The corresponding points were extracted wrongly by the result. By because the characteristics of small region was evaluated by autocorrelation and the critical value was established that may be proportional to the autocorrelation value, it was confirmed that corresponding points are not extracted almost by mistake and binocular disparity could by extracted with high speed.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.541-548
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• 2000

In the MPEG-4 standardization phase, many methods for coding the irregular shaped VOP (video object Plane) have been researched. Texture coding is one of interesting research items in the MPEG-4. There are the Low pass extrapolation (LPE) padding, the shape adaptive DCT (SA-DCT), and the Extension-Interpolation (EI)/2D-DCT proposed in [1] as texture coding methods. the EI/2D-DCT is the method extending and interpolating luminance values from and Arbitrarily Shaped (AS) image segment into an 8 x 8 block and transforming the extended and interpolated luminance values by the 8x8 DCT. although the EI/2D-DCT and the SA-DCT work well in coding the As image segments. they are degraded since they use one-dimensional (1-D) methods such as the 1D-EI and the 1D-DCT in the two-dimensional (2-D) space. in this paper, we analyze the performance of the EI/2D-DCTand propose a new non-symmetric sig-sag scanning method, which non-symmetrically scans the quantized coefficients in the DCT domain to improve the EI/2D-DCT.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.249-254
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• 2007

A mobile robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. We focus on how to detect a object region well using image processing algorithm because it gives robots the ability of working for human. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. Shape information and signature algorithm are used to segment the objects from background regardless of shape changes. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.132-140
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• 2007

In this paper, we proposed a new technique to encode and decode the digital hologram. Since the digital hologram (or fringe pattern) is generated by interference of light, it has much different property from natural 2D (2 dimensional) images. First, we acquisite optical-sensed or computer-generated hologram by digital type, and then extract a chrominance component. The extracted digital hologram for coding is separated into segments to use multi-view properties. The segmented hologram shows the similar characteristics with picturing an object with 2D cameras in various point of view. Since fringe pattern is visually observed like as noise, we expect that the fringe pattern has poor coding efficiency. To obtain high efficiency, the segment is transformed with DCT (Discrete Cosine Transform) which resembles hologram generation process with high performance. Each transformed segment passes the 3D scanning process according to time and spatial correlation, and is organized into a video stream. Since the segment which correspond to frame of a video stream consists of the transformed coefficients with wide range of value, it is classified and re-normalized. Finally it is compressed with coding tools. The proposed algorithm illustrated that it has better properties for reconstruction of 16 times higher compression rate than the previous researches.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.595-598
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• 2007

Vast amount of XML documents raise interests in how they will be used and how far their usage can be expanded. This paper has two central goals: 1) easy and fast retrieval of XML documents or relevant elements; and 2) efficient and stable management of large-size XML documents. The keys to develop such a practical system are how to segment a large XML document to smaller fragments and how to store them. In order to achieve these goals, we designed SCOF(Service-centric Object Fragmentation) model, which is a semi-decomposition method based on conversion rules provided by XML database managers. Keyword-based search using SCOF model then retrieves the specific elements or attributes of XML documents, just as typical XML query language does. Even though this approach needs the wisdom of managers in XML document collection, SCOF model makes it efficient both retrieval and management of massive XML documents.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.17-24
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• 2006

In this paper, an algorithm for anchor frame detection in news video is proposed, which consists of four steps. In the first step, the cumulative histogram method is used to detect shot boundaries in order to segment a news video into video shots. In the second step, skin color information is used to detect face regions in each shot boundary. In the third step, color information of upper body regions is used to extract anchor object, which produces candidate anchor frames. Then, from the candidate anchor frames, a graph-theoretic cluster analysis algorithm is utilized to classify the news video into anchor-person frames and non-anchor frames. Experiment results have shown the effectiveness of the proposed algorithm.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.357-363
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• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.103-113
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• 2008

Vast amount of XML documents raise interests in how they will be used and how far their usage can be expanded, This paper has two central goals: 1) easy and fast retrieval of XML documents or relevant elements; and 2) efficient and stable management of large-size XML documents, The keys to develop such a practical system are how to segment a large XML document to smaller fragments and how to store them. In order to achieve these goals, we designed SCOF(Service-centric Object Fragmentation) model, which is a semi-decomposition method based on conversion rules provided by XML database managers. Keyword-based search using SCOF model then retrieves the specific elements or attributes of XML documents, just as typical XML query language does. Even though this approach needs the wisdom of managers in XML document collection, SCOF model makes it efficient both retrieval and management of massive XML documents.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.73-82
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• 1996

In this paper, an efficient algorithm for recognizing and positioning occuluded objects in a two-dimensional plane is presented. Model objects and unknown input image are approximated by polygonal boundaries, which are compactly represented by shape functions of the polygons. The input image is partitioned into measningful segments whose end points are at the locations of possible occlusion - i.e. at concave vertices. Each segment is matched against known model objects by calculating a matching measure, which is defined as the minimum euclidean distance between the shape functions. An O(mm(n+m) algorithm for computing the measure is presentd, where n and m are the number of veritces for a model and an unknown object, respectively. Match results from aprtial segments are combined based on mutual compatibility, then are verified using distance transformation and translation vector to produce the final recognition. The proposed algorithm is invariant under translation and rotation of objects, which has been shown by experimental results.