• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1350-1353
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• 2015

In this paper, we have researched about how to detect the fish robot objects in aquarium. We had used designed fish robots DOMI ver1.0, which had researched and developed for aquarium underwater robot. The model of the robot fish is analysis to maximize the momentum of the robot fish and the body of the robot is designed through the analysis of the biological fish swimming. We are planned to non-external equipment to find the position and manipulated the position using creating boundary to fish robot to detect the fish robot objects. Also, we focused the detecting fish robot in aquarium by using boundary algorithm. In order to the find the object boundary, it is filtering the video frame to picture frames and changing the RGB to gray. Then, applied the boundary algorithm stand of equations which operates the boundary for objects. We called these procedures is kind of image processing that can distinguish the objects and background in the captured video frames. It was confirmed that excellent performance in the field test such as filtering image, object detecting and boundary algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.174-177
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• 2009

In object tracking, the template matching methods have been developed and frequently used. It is fast enough, but not robust to an object with the variation of size and shape. In order to overcome the limitation of the template matching method, this paper proposes a template update technique. After finding an object position using the correlation-based adaptive predictive search, the proposed method selects blocks which contain object's boundary. It estimates the motion of boundary using block matching, and then updates template. We applied it to IR image sequences including an approaching object. From the experimental results, the proposed method showed successful performance to track object.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.577-580
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• 1999

Padding is a technique that enables applying conventional discrete cosine transform to encode boundary blocks of arbitrarily shaped objects by assigning imaginary values to the pixels that are not included in the object. Padding prevents the increase of high frequency DCT coefficients. However, in some boundary blocks, too many padded pixels are coded due to a small portion of object pixels. To reduce the number of padded pixels and to improve coding efficiency, we propose a block merging method for texture coding. The proposed mothed searches the shape information of boundary blocks and excludes the 4$\times$4 pixels of 8$\times$8 blocks if all the 4$\times$4 pixels are in the background region, and merges the remained 4$\times$4 pixels into new 8$\times$8 blocks. Experimental results show that our proposed method yields a rate-distortion gain about 0.5~1.6㏈ compared to conventional padding method, LPE

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.361-368
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• 2006

In this paper we present a snake-based scheme for efficiently detecting contours of objects with boundary concavities. The proposed method is composed of two steps. First, the object's boundary is detected using the proposed snake model. Second, snake points are optimized by inserting new points and deleting unnecessary points to better describe the object's boundary. The proposed algorithm can successfully extract objects with boundary concavities. Experimental results have shown that our algorithm produces more accurate segmentation results than the conventional algorithm.

• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.134-142
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• 2019

As the 4th industry has been widely distributed, there is a need for a process of real-time image recognition in various fields such as identification of company employees, security maintenance, and development of military weapons. Therefore, in this paper, we will propose an algorithm that effectively recognizes a test object by comparing it with the DB model. The proposed object recognition system first expresses the outline of the test object as a set of vertices with the distances of predefined length or more. Then, the degree of matching of the structures of the two objects is calculated by examining the distances to the outline of the DB model from the vertices constituting the test object. Because the proposed recognition algorithm uses the outline of the object, the recognition process is easy to understand, simple to implement, and a satisfactory recognition result is obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4386-4404
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• 2016

Most existing salient object detection algorithms commonly employed segmentation techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q's adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1819-1826
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• 2010

This paper proposes the image segmentation algorithm to extracts a fish object from a fish image for fish image retrieval. The conventional algorithm using gray level similarity causes wrong image segmentation result in the boundary area of the object and the background with similar gray level. The proposed algorithm uses the reinforced edge and the adaptive block-based threshold for the boundary area with weak contrast and the virtual object to improve the eroded or disconnected object in the boundary area without contrast. The simulation results show that the percentage of extracting the visual-fine object from the test images is under 90% in the conventional algorithm while it is 97.7% in the proposed algorithms.

• 전자공학회논문지 IE
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• v.46 no.1
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• pp.7-15
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• 2009

In this paper, automatic structure boundary extraction is proposed using terrestrial LIDAR (Light Detection And Ranging) in 3-dimensional data. This paper describes an algorithm which does not use pictures and pre-processing. In this algorithm, an efficient decimation method is proposed, considering the size of object, the amount of LIDAR data, etc. From these decimated data, object points and non-object points are distinguished using distance information which is a major features of LIDAR. After that, large and small values are extracted using local variations, which can be candidate for boundary. Finally, a boundary line is drawn based on the boundary point candidates. In this way, the approximate boundary of the object is extracted.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.9-14
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• 1999

The object boundary of an image plays an important role for image interpretation. In this paper, we introduce a concept of hierarchical mesh-based image segmentation for finding object boundaries. In each hierarchical layer, we employ neighborhood searching and boundary tracking methods to refine the initial boundary estimate. We also apply a local region growing method to define closed contours. Experimental results indicate that reliable segmentation of objects can be accomplished by the pro-posed tow complexity technique.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.54-61
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• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.