• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.125-132
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• 2012

This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.866-870
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• 2010

This paper presents an improved Snake algorithm that contains additional energy term related to adjacent edges. The suggested algorithm represents the distance between an adjacent edge and the current cell as energy, and extracts object contours more effectively by including the energy tenn to the whole energy function. The adjacent edge-based snake algorithm not only make it possible to detect object boundaries which are concave, but also can detect the boundaries of complex objects without weight adjustment. Experimental results show that the proposed method extracts object boundaries more accurately than other existing methods without loss of speed.

• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.514-527
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• 2009

Wireless sensor networks (WSNs) can be used in various applications for military or environmental purpose. Recently, there are lots of on-going researches for detecting and tracking the spread of continuous objects or phenomena such as poisonous gas, wildfires, earthquakes, and so on. Some previous work has proposed techniques to detect edge nodes of such a continuous object based on the information of all the 1-hop neighbor nodes. In those techniques, however, a number of nodes are redundantly selected as edge nodes, and thus, the boundary of the continuous object cannot be presented accurately. In this paper, we propose a new edge detection method in which edge nodes of the continuous object are detected based on the information of the neighbor nodes obtained via the Localized Delaunay Triangulation so that a minimum number of nodes are selected as edge nodes. We also define the sensor behavior rule for tracking continuous objects energy-efficiently. Our simulation results show that the proposed edge detection method provides enhanced performance compared with previous 1-hop neighbor node based methods. On the average, the accuracy is improved by 29.95% while the number of edge nodes, the amount of communication messages and energy consumption are reduced by 54.43%, 79.36% and 72.34%, respectively. Moreover, the number of edge nodes decreases by 48.38% on the average in our field test with MICAz motes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1424-1433
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• 1995

The edge detection process serves to simplify the analysis of images by drastically reducing the amount of data to be processed, while preserving useful structural informations about object boundaries. At first, this paper proposes an edge detection algorithm to reduce the amount of computation. The gradients of pixels are calculated by using first order differential equations on the pixels with even rows and even columns or odd rows and odd columns, and they are compared with a threshold to decide edges. As a result, the computational complexity is reduced to one third or one forth compared with the provious ones. To enhance the accuracy of edge detection, a method with the adaptive threshold for each pixel window which is calculated by using characteristic values is proposed. In this case, the performance can be improved since the threshold is calculated properly for each window according to the local characteristics of corresponding window.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.889-894
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• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• Journal of the Semiconductor & Display Technology
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• v.17 no.3
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• pp.113-116
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• 2018

This paper proposes a mobile image search system which uses a sensor information of smart phone, and enables running in a variety of environments, which is implemented on Android platform. The implemented system deals with a new image descriptor using combination of the visual feature (CEDD) with EXIF attributes in the target of JPEG image, and image matching scheme, which is optimized to the mobile platform. Experimental result shows that the proposed method exhibited a significant improved searching results of around 80% in precision in the large image database. Considering the performance such as processing time and precision, we think that the proposed method can be used in other application field.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.678-681
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• 2004

In this paper, efficient tracking of a moving object using optimal representative blocks is implemented by a mobile robot with a pan-tilt camera. The key idea comes from the fact that when the image size of moving object is shrunk in an image frame according to the distance between the camera of mobile robot and the moving object, the tracking performance of a moving object can be improved by changing the size of representative blocks according to the object image size. Motion estimation using Edge Detection(ED) and Block-Matching Algorithm(BMA) is often used in the case of moving object tracking by vision sensors. However these methods often miss the real-time vision data since these schemes suffer from the heavy computational load. In this paper, the optimal representative block that can reduce a lot of data to be computed, is defined and optimized by changing the size of representative block according to the size of object in the image frame to improve the tracking performance. The proposed algorithm is verified experimentally by using a two degree-of-freedom active camera mounted on a mobile robot.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.99-106
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• 2009

Though edge detection, an important stage that significantly affecting the performance of image recognition, has been given numerous researches on its execution methods, it still remains as difficult problem and it is one of the components for image recognition applications while it is not the only way to identify an object or track a specific area. This paper, unlike gradient operator using edge detection method, found out edge pixel by referring to 2 neighboring pixels information in binary image and comparing them with pre-defined 4 edge pixels pattern, and detected binary image edge by determining the direction of the next edge detection exploring pixel and proposed method to detect binary image edge by repeating step of edge detection to detect another area edge. When recognizing image, if edge is detected with the use of gradient operator, thinning process, the stage next to edge detection, can be omitted, and with the edge detection algorithm executing time reduced compared with existing area edge tracing method, the entire image recognizing time can be reduced by applying real-time image recognizing system.

• 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.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.495-502
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• 2003

This paper focuses on the implementation of an efficient tracking method of a moving object using optimal representative blocks by way of a pan-tilt camera. The key idea is derived from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the mobile robot camera and the object in motion, the tracking performance of a moving object can be improved by reducing the size of representative blocks according to the object image size. Motion estimations using Edge Detection (ED) and Block-Matching Algorithm (BMA) are regularly employed to track objects by vision sensors. However, these methods often neglect the real-time vision data since these schemes suffer from heavy computational load. In this paper, a representative block able to significantly reduce the amount of data to be computed, is defined and optimized by changing the size of representative blocks according to the size of the object in the image frame in order to improve tracking performance. The proposed algorithm is verified experimentally by using a two degree-of- freedom active camera mounted on a mobile robot.