• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2044-2047
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• 2003

In this paper, we propose efficient motion vector refinement algorithm for frame-rate reduction transcoding. The proposed algorithm is to set the search range for motion refinement based on the incoming motion vector. The algorithm calculates the importance of motion vector of the skipped frame and then selects two motion vector to set search range. Through this process, we determine the accuracy of incoming motion vector and set the search range lot refinement adaptively by means of the accuracy. In experiments, we show efficiency of our algorithm to reduce the search points for refinement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9B
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• pp.1604-1608
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• 2000

The three-step search (TSS) algorithm for block motion estimation has been widely used in real-time video coding due to the simplicity of the algorithm significant reduction of computationl cost and good performance. In this paper an improved three-step search (ITS) algorithm is proposed to improve the performance of the TSS algorithm. Simulation results show that in terms of motion compensation errors the proposed ITSS outperforms some popular fast search algorithms while it has the lower computational complexity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.184-189
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• 1997

This paper introduces a new combined method by genetic algorithm and random tabu search method as optimization algorithm. Genetic algorithm can search the global optimum and tabu search method is very fast in speed. The optimizing ability of new combined method is identified by comparing other optimizing algorithm and used for optimum design of damping plate.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.57-65
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• 2007

In the fast block matching algorithm. search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image qualify. In this paper, we propose a new fast block matching algorithm using the flat-hexagon search pattern that ate solved disadvantages of the diamond pattern search algorithm(DS) and the hexagon-based search algorithm(HEXBS). Our proposed algorithm finds mainly the motion vectors that not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the DS and HEXBS, the proposed f)at-hexagon search algorithm(FHS) improves about $0.4{\sim}21.3%$ in terms of average number of search point per motion vector estimation and improves about $0.009{\sim}0.531dB$ in terms of PSNR(Peak Signal to Noise Ratio).

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.167-170
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• 2000

In this paper, we present a fast algorithm for the motion estimation using the efficient selection of an initial search position. In the method, we select the initial search position using the motion vector from the subsmpled images, the predicted motion vector from the neighbor blocks, and the (0,0) motion vector. While searching the candidate blocks, we use the spiral search pattern with the successive elimination algorithm(SEA) and the partial distortion elimination(PDE). The experiment results show that the complexity of the proposed algorithm is about 2∼3 times faster than the three-step search(TSS) with the PSNR loss of just 0.05［dB］∼0.1［dB］ than the full search algorithm PSNR. The search complexity can be reduced with quite a few PSNR loss by controling the number of the depth in the spiral search pattern.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.960-968
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• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.157-163
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• 2017

The binary and interpolation search algorithms are the most famous among search area algorithms, the former running in $O(log_2n)$ on average, and the latter in $O(log_2log_2n)$ on average and O(n) at worst. Also, the interpolation search use only the probability of key value location without priori information. This paper proposes another search algorithm, which I term a 'hybrid block and interpolation search'. This algorithm employs the block search, a method by which MSB index of a data is determined as a block, and the interpolation search to find the exact location of the key. The proposed algorithm reduces the search range with priori information and search the reduced range with uninformed situation. Experimental results show that the algorithm has a time complexity of $O(log_2log_2n_i)$, $n_i{\simeq}0.1n$ both on average and at worst through utilization of previously acquired information on the block search. The proposed algorithm has proved to be approximately 10 times faster than the interpolation search on average.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.131-139
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• 2007

This paper proposed a multi-resolution algorithm. Its search point and complexity were compared with those of block match algorithm. Also the speed up comparison was made with the block match algorithm. The proposed multi-resolution NTSS-3 Level algorithm was compared again with its targets, TSS-3 Level algorithm and NTSS algorithm. The comparison results showed that the NTSS-3 Level algorithm was superior in search point and speed up. Accordingly, the proposed NTSS-3 Level algorithm was two to three times better in search point and two to four times better in complexity calculation than those of the compared object, the block match algorithm. In speed up, the proposed NTSS-3 Level algorithm was two times better. Accordingly, the proposed multi-resolution NTSS-3 Level algorithm showed PSNR ration portion excellency in search point and speed up.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.