• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.521-527
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• 2013

Rule set is an important step which impacts significantly on accuracy of object-oriented classification result. Therefore, this paper proposes a rule set to extract land cover from Landsat Thematic Mapper (TM) imagery acquired in Donganh, Hanoi, Vietnam. The rules were generated to distinguish five classes, namely river, pond, residential areas, vegetation and paddy. These classes were classified not only based on spectral characteristics of features, but also indices of water, soil, vegetation, and urban. The study selected five indices, including largest difference index max.diff; length/width; hue, saturation and intensity (HSI); normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) based on membership functions of objects. Overall accuracy of classification result is 0.84% as the rule set is used in classification process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.646-648
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• 2012

In this paper, an image detection technique is proposed to extract image features in a mobile terminal environment. To detect objects, the HSI color model of the image is used. The object's corner points are detected using the Harris corner detection method. Finally we detect the object of interest using region growing The experiment results show that the proposed method improves detection performance and reduces the amount of computation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.586-594
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• 2018

In this paper, we propose a livestock theft detection system through moving object classification and tracking method. To do this, first, we extract moving objects using GMM(Gaussian Mixture Model) and RGB background modeling method. Second, it utilizes a morphology technique to remove shadows and noise, and recognizes moving objects through labeling. Third, the recognized moving objects are classified into human and livestock using skeletal features and color similarity judgment. Fourth, for the classified moving objects, CAM (Continuously Adaptive Meanshift) Shift and Kalman Filter are used to perform tracking and overlapping judgment, and risk is judged to generate a notification. Finally, several experiments demonstrate the feasibility and applicability of the proposed method.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.2
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• pp.82-89
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• 2015

This study proposes a new approach to design and control for autonomous mobile robots. In this paper, we describes a fuzzy logic based visual servoing system for an autonomous mobile robot. An existing system always needs to keep a moving object in overall image. This mes difficult to move the autonomous mobile robot spontaneously. In this paper we first explain an autonomous mobile robot and fuzzy logic system. And then we design a fuzzy logic based visual servoing system. We extract some features of the object from an overall image and then design a fuzzy logic system for controlling the visual servoing system to an exact position. We here introduce a shooting robot that can track an object and hit it. It is illustrated that the proposed system presents a desirable performance by a computer simulation and some experiments.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1373-1379
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• 2006

Moving object extraction is one of key research topics for various video services. In this study, a new moving object extraction algorithm is introduced to extract objects using block motion vectors in video data. To do this, 1) a maximum a posteriori probability and Gibbs random field are used to obtain real block motion vectors,2) a 2-D histogram technique is used to determine a global motion, 3) additionally, a block segmentation is fellowed. In the computer simulation results, the proposed technique shows a good performance.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1275-1283
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• 1990

Perception of surfaces from range images plays a key role in 3-D object recognition. Recognition of 3-D objects from range images is performed by matching the perceived surface descriptions with stored object models. The first step of the 3-d object recognition from range images is image segmentation. In this paper, an approach for segmenting 3-D range images into symbolic surface descriptions using spatial Gabor filter is proposed. Since the phase of data has a lot of important information, the phase information with magnitude information can effectively segment the range imagery into regions satisfying a common homogeneity criterion. The phase and magnitude of Gabor filter can represent a unique featur vector at a point of range data. As a result, range images are trnasformed into feature vectors in 3-parameter representation. The methods not only to extract meaningful features but also to classify a patch information from range images is presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.19-22
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• 2000

Noncontact measuring methodology of 3-dimensional profile using CCD camera are very attractive because of it's high measuring speed and its's high sensitivity. Especially, when projecting a grid pattern over the object the captured image have 3 dimensional information of the object. Projection moire extract 3-D information with another grid pattern in front of CCD camera. However phase measuring profilometry(PMP) obtain similar results without additional grid pattern. In this paper, new method for grid pattern generation system by polygonal mirror and Laser Diode. This system is applied the projection moire and the PMP.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.298-301
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• 2007

Computational speed plays key role in background subtraction and shadow detection, because those are only preprocessing steps of a moving object segmentation, tracking and activity recognition. A color intensity variation based approach fastly detect a moving object and extract shadow in a image sequences. The moving object is subtracted from background using meanmax, meanmin thresholds and shadow is detected by decrease limit and correspondence thresholds. The proposed approach relies on the ability to represent shadow cast impact by offline experiment dataset on sub grouped RGB color space.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.