• ETRI Journal
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• 제46권2호
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• pp.307-322
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• 2024

Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multi-branch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2901-2919
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• 2012

Recent research in MEMS (Micro-Electro-Mechanical Systems) and wireless communication has enabled tracking of continuous objects, including fires, nuclear explosions and bio-chemical material diffusions. This paper proposes an energy-efficient scheme that detects and tracks different dynamic shapes of a continuous object (i.e., the inner and outer boundaries of a continuous object). EBCO (Efficient Boundary detection and tracking of Continuous Objects in WSNs) exploits the sensing capabilities of sensor nodes by automatically adjusting the sensing range to be either a boundary sensor node or not, instead of communicating to its neighboring sensor nodes because radio communication consumes more energy than adjusting the sensing range. The proposed scheme not only increases the tracking accuracy by choosing the bordering boundary sensor nodes on the phenomenon edge, but it also minimizes the power consumption by having little communication among sensor nodes. The simulation result shows that our proposed scheme minimizes the energy consumption and achieves more precise tracking results than existing approaches.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.279-286
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• 2000

The objective of this research is to detect the papaya fruits on tree in an orchard. The detection of papaya on natural background is difficult because colors of fruits and background such as leaves are similarly green. We cannot separate it from leaves by color information. Therefore, this research will use shape information instead. First, we detect an interested object by detecting its boundary using edge detection technique. However, the edge detection will detect every objects boundary in the image. Therefore, shape description technique will be used to describe which one is the interested object boundary. The good shape description should be invariant in scaling, rotating, and translating. The successful concept is to use Fourier series, which is called "Fourier Descriptors". Elliptic Fourier Descriptors can completely represent any shape, which is selected to describe the shape of papaya. From the edge detection image, it takes a long time to match every boundary directly. The pre-processing task will reduce non-papaya edge to speed up matching time. The deformable template is used to optimize the matching. Then, clustering the similar shapes by the distance between each centroid, papaya can be completely detected from the background.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제28B권7호
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• pp.578-589
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• 1991

In this paper, there is proposed a novel concept of Incremintal Circle Transform which can describe the boundary contour of a two-dimensional object without object without occlusions. And a pattern recognition algorithm to determine the posture of an object is developed with the aid of line integral and similarity transform. Also, It is confirmed via experiments that the algorithm can find the posture of an object in a very fast manner independent of the starting point for boundary coding and the position of the object.

• Journal of the Korea Society of Computer and Information
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• 제18권5호
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

• Journal of Institute of Control, Robotics and Systems
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• 제14권1호
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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.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2126-2133
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• 2014

We present a scalable object tracking framework, which is capable of removing shadows and tracking the people. The framework consists of background subtraction, fuzzy based shadow removal and boundary tracking algorithm. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects, and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects and shadows are processed differently in order to supply an object-based selective update. Experimental results demonstrate that the proposed method is able to track the object boundaries under significant shadows with noise and background clutter.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제31권4C호
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• pp.407-416
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• 2006

It is difficult for the movement detection of an object through a camera to detect exact movement because of unnecessary noises and changes of the light. It can be recognized as a background, when there is no movement after the inflow of an object. Therefore, It is necessary to fast search algorithm for tracking and extract of object that is realtime image. In this thesis, we evaluate the difference of the input vision based on initial image and replace some pixels in process of time. When there is a big difference between background image and input image, we decide it is the point of the time of the object input and then extract boundary point of it. The extracted boundary point detects precise movement of the object by creating minimum block of it and searching block that maintaining distance. The designed and embodied system shows more than 95% accuracy in the performance test.

• The KIPS Transactions:PartB
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• 제15B권6호
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• pp.501-506
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• 2008

The classical snake algorithm has a problem in detecting the boundary of an object with deep concavities. While the GVF method can successfully detect boundary concavities, it consumes a lot of time computing the energy map. In this paper, we propose an algorithm to reduce the computation time and improve performance in detecting the boundary of an object with high concavity. We define the degree of complexity of object boundary as the local curvature. If the value of the local curvature is greater than a threshold value, new snake points are added. Simulation results on several different test images show that our method performs well in detecting object boundary and requires less computation time.