• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.628-633
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• 2010

This paper proposes a block matching algorithm to improve the sub-block matching algorithm that uses partial sub-blocks being a great contribution to the block matching. Unlike the conventional algorithm using the one sub-block group the proposed algorithm uses two sub-block groups. The matching using the small group selects a candidate block to be a good possibility of a similar block with a small computation cost and the additional matching using the large group in the selected block decreases a wrong matching. Simulation results show that the proposed algorithm always has good image quality at the same computation cost as compared to the conventional algorithm and it has an outstanding performance at the matching using a few sub-blocks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.6
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• pp.280-298
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• 2008

For efficient content-based image retrieval, diverse visual features such as color, texture, and shape have been widely used. In the case of leaf images, further improvement can be achieved based on the following observations. Most plants have unique shape of leaves that consist of one or more blades. Hence, blade-based matching can be more efficient than whole shape-based matching since the number and shape of blades are very effective to filtering out dissimilar leaves. Guaranteeing rotational invariance is critical for matching accuracy. In this paper, we propose a new shape representation, indexing and matching scheme for leaf image retrieval. For leaf shape representation, we generated a distance curve that is a sequence of distances between the leaf’s center and all the contour points. For matching, we developed a blade-based matching algorithm called rotation invariant - partial dynamic time warping (RI-PDTW). To speed up the matching, we suggest two additional techniques: i) priority queue-based pruning of unnecessary blade sequences for rotational invariance, and ii) lower bound-based pruning of unnecessary partial dynamic time warping (PDTW) calculations. We implemented a prototype system on the GEMINI framework [1][2]. Using experimental results, we showed that our scheme achieves excellent performance compared to competitive schemes.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.959-967
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• 2004

Jain et al. proposed the hybrid matching method which was combined the minutia-based matching method and the filter-bank based matching method. And, their experimental results proved the hybrid matching method was more effective than each of them. However, this hybrid method cannot utilize each peculiar advantage of two methods. The reason is that it gets the matching score by simply summing up each weighted matching score after executing two methods individually. In this paper, we propose new hybrid matching method. It mixes two matching methods during the feature extraction process. This new hybrid method has lower ERR than the filter-bank based method and higher ERR than the minutia-based method. So, we propose the adaptive hybrid scoring method, which selects the matching score in order to preserve the characteristics of two matching methods. Using this method, we can get lower ERR than the hybrid matcher by Jain et al. Experimental results indicate that the proposed methods can improve the matching performance up to about 1% in ERR.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.3
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• pp.32-40
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• 1999

In this paper, we present a fast block matching motion estimation algorithm based on successive elimination algorithm (SEA). Based on the characteristic of center-biased motion vector distribution in the search area, the proposed method improves the performance of the SEA with a reduced the number of the search positions in the search area, In addition, to reduce the computational load, this method is combined with both the reduced bits mean absolute difference (RBMAD) matching criterion which can be reduced the computation complexity of pixel comparison in the block matching and pixel decimation technique which reduce the number of pixels used in block matching. Simulation results show that the proposed method provides better performance than existing fast algorithms and similar to full-search block motion estimation algorithm.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.447-451
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• 2011

Recently, various in-vehicle networks have been developed respectively in order to accomplish their own purposes such as CAN and MOST. Especially, the MOST network is usually adapted to provide entertainment service. The car navigation system is also widely used for guiding driving paths to driver. The position for the navigation system is usually acquired by GPS technology. However, the GPS technique has two serious problems. The first is unavailability in urban canyons. The second is inherent positional error rate. The problems have been studied in many literatures. However, the second still leads to incorrect locational information in some area, especially parallel roads. This paper proposes a performance tuning method of image matching algorithm for the car navigation system. The method utilizes images obtained from in-vehicle MOST network and a real-time image matching algorithm which determines the direction of moving vehicle in parallel section of road. In order to accuracy improvement of image matching algorithm, three conditions are applied. The experimental tests show that the proposed system increases the accuracy.

• ETRI Journal
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• v.41 no.3
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• pp.371-382
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• 2019

Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short-term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self-matching network, used in R-Net, can have a similar effect to coreference resolution because the self-matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an $S^2-Net$ model that adds a self-matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed $S^2-Net$ model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.48-57
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• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.

• Journal of the Korean Ceramic Society
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• v.30 no.10
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• pp.823-828
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• 1993

The performance of ferrite composite microwave absorber has been evaluated as the matching frequency, the matching thickness and the band width. Especially, matching frequency is very important in application of microwave absorber because it determines operating frequency range. In this study, the ferrite composite microwave absorbers consisted Ni-Zn ferrite and Zn2Y ferroxplana were prepared in order to control matching frequency. Then, the variation of the material constants and microwave absorbing characteristics were investigated with various ferrite mixing ratio. The material constants of ferrite composite microwave absorber could be controlled by variation ferrite mixing ratio. Therefore, it was confirm that matching frequency of microwave absorber could be shifted by control of material constants.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.224-241
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• 2015

Multiview plus depth (MVD) videos are widely used in free-viewpoint TV systems. The best-known technique to determine depth information is based on stereo vision. In this paper, we propose a novel local stereo matching algorithm which is radiometric invariant. The key idea is to use a combined matching cost of intensity and gradient based similarity measure. In addition, we realize an adaptive cost aggregation scheme by constructing an adaptive support window for each pixel, which can solve the boundary and low texture problems. In the disparity refinement process, we propose a four-step post-processing technique to handle outliers and occlusions. Moreover, we conduct stereo reconstruction tests to verify the performance of the algorithm more intuitively. Experimental results show that the proposed method is effective and robust against local radiometric distortion. It has an average error of 5.93% on the Middlebury benchmark and is compatible to the state-of-art local methods.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.