• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.339-342
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• 2005

We present an efficient block-based motion estimation algorithm with motion analysis. The motion analysis determines a size of search pattern and a maximum repeated count of search pattern. In case of large movement in large image, we reduce search points and the local minimum which caused by low performance. The proposed algorithm employs with searching step of 2. The first step determines an initial search point with neighbor block vector and a size of initial search pattern. The second step determines a size of search pattern and a maximum repeated count with motion analysis. We improve motion prediction accuracy while reducing required computational complexity compared to other fast block-based motion estimation algorithms.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.22-31
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• 2004

This paper deals with the dynamic hand gesture recognition based on computer vision using neural networks. This paper proposes a global search method and a local search method to recognize the hand gesture. The global search recognizes a hand among the hand candidates through the entire image search, and the local search recognizes and tracks only the hand through the block search. Dynamic hand gesture recognition method is based on the skin-color and shape analysis with the invariant moment and direction information. Starting point and ending point of the dynamic hand gesture are obtained from hand shape. Experiments have been conducted for hand extraction, hand recognition and dynamic hand gesture recognition. Experimental results show the validity of the proposed method.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1097-1100
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• 1999

Due to the significant computation of full search in motion estimation, extensive research in fast motion estimation algorithms has been carried out. However, most of the algorithms have the degradation in predicted images compared with the full search algorithm. To reduce an amount of significant computation while keeping the same prediction quality of the full search, we propose a fast block-matching algorithm based on gradient magnitude of reference block without any degradation of predicted image. By using Taylor series expansion, we show that the block matching errors between reference block and candidate block are proportional to the gradient magnitude of matching block. With the derived result, we propose fast full search algorithm with adaptively determined scan direction in the block matching. Experimentally, our proposed algorithm is very efficient in terms of computational speedup and has the smallest computation among all the conventional full search algorithms. Therefore, our algorithm is useful in VLSI implementation of video encoder requiring real-time application.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1178-1186
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• 2019

In recent years, interest in drones has grown, and many countries are developing them into a strategic industry of the future. Drones are not only used in industries such as logistics and agriculture but also in various public sectors such as life rescue, disaster investigation, traffic control, and firefighting. One of the most important tasks of a drone is to accurately identify targets in these applications. Target recognition may vary depending on the search environment of the drone. Therefore, this study tests and analyzes the drone's target recognition performance according to changes in the search environment such as the search altitude and the search angle. In addition, we propose a new algorithm that improves upon the disadvantages of the Haar cascade method, which is the existing algorithm that recognizes the target by analyzing a captured image.

• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.168-177
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• 2012

The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients' cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of these resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.7
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• pp.37-42
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• 2008

In this paper, a real-time multiview video coding system using fast disparity estimation is proposed. In the multiview encoder, adaptive disparity-motion estimation (DME) for an effective 3-dimensional (3D) processing are proposed. That is, by adaptively predicting the mutual correlation between stereo images in the key-frame using the proposed algorithm, the bandwidth of stereo input images can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and adaptive disparity vectors. Also, in multiview decoder, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (DSA) for real-time multiview video processing is proposed. The proposed IVR can reduce a processing time of disparity estimation by selecting adaptively disparity search range. Accordingly, the proposed multiview video coding system is able to increase the efficiency of the coding rate and improve the resolution.

• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.135-140
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• 1999

This paper describes a affine transform coding which is reducing long time to take on image encoding by using scaling method and limited search area technique. For evaluating its performance, the proposed algorithm compare with Jacquin's method using traditional affine transform coding methods. Simulation results, the proposed algorithm considerably reduces encoding time by using scaling method and limited search area method. Also, the proposed algorithm provides much shorter encoding time with a little degradation of the decoded image quality than Jacquin's method.

• Convergence Security Journal
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• v.9 no.2
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• pp.85-89
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• 2009

This study to solve these problems a search system has been designed by combining the two methods. Also the search and manage image data by handheld devices such as portable PDA or smart phone, a system has been designed application to an embedded system. Once this is used, multimedia data can be efficiently searched and utilized by handheld devices.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.135-135
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks that have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, we have a global search for entire image and then have tracking the object through local search when the object is recognized.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.153-162
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• 1995

When vector quantization is used in low rate image coding (e.g., R<0.5), the primary problem is the tremendous computational complexity which is required to search the whole codebook to find the closest codevector to an input vector. Since the number of code vectors in a vector quantizer is given by an exponential function of the dimension. i.e., L=2$^{nR}$ where Rn. To alleviate this problem, a multiple shell structure of hypercube feature maps (MSSHFM) is proposed. A binary HFM of k-dimension is composed of nodes at hypercube vertices and a multiple shell architecture is constructed by surrounding the k-dimensional hfm with a (k+1)-dimensional HFM. Such a multiple shell construction of nodes inherently has a complete tree structure in it and an efficient partial search scheme can be applied with drastically reduced computational complexity, computer simulations of still image coding were conducted and the validity of the proposed method has been verified.