• Journal of the military operations research society of Korea
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• v.17 no.1
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• pp.105-129
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• 1991

Dynamic Time Warping(in short DTW) is a kind of sequence comparison method. It is widely used in human speech recognition. The timing difference between two speech patterns to be compared is removed by warping the time axes of the speech pattern by minimising the time-normalised distance between them. In the process of finding the minimum time-normalised distance. the efficient method is dynamic programming problem. This paper describes the concept of dynamic time warping method, mathematical formulation and an application.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.167-179
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• 2001

This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query precessing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verily the superiority of our method, we perform extensive experiments. The results reseal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.643-649
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• 2016

This paper presents a new image cache algorithm for real-time implementation of high-resolution color image warping. The cache memory is divided into four cache memory modules for simultaneous readout of four input image pixels in consideration of the color filter array (CFA) pattern of an image sensor and CFA image warping. In addition, a pipeline structure from the cache memory to an interpolator is shown to guarantee the generation of an output image pixel at each system clock cycle. The proposed image cache algorithm is applied to an FPGA-based real-time color image warping, and experimental results are presented to show the validity of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.640-642
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• 2003

본 논문은 수표 하단에 인쇄된 자기 데이터를 읽어 들여 수표를 인식할 때 오독률을 최소화하는 효율적인 방법을 소개하고자 한다. 수정된 Dynamic Time Warping 기법으로 왜곡되거나 손상된 파형의 인식률을 높이고, class-selective rejection방법을 통해 오독률을 최소화할 수 있는 효과적인 방법을 제안한다.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.219-226
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• 2000

In this paper, we propose a new novel method for similarity search that supports time warping. Our primary goal is to innovate on search performance in large databases without false dismissal. To attain this goal, we devise a new distance function $D_{tw-lb}$ that consistently underestimates the time warping distance and also satisfies the triangular inequality. $D_{tw-lb}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping. For efficient processing, we employ a multidimensional index that uses the 4-tuple feature vector as indexing attributes and $D_{tw-lb}$ as a distance function. We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments. The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.294-297
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• 2005

Time series are comprehensively appeared and developed in many applications, ranging from science and technology to business and entertainrilent. Similarity search under time warping has attracted much interest between the time series in the large sequence databases. DTW (Dynamic Time Warping) is a robust distance measure and is superior to Euclidean distance for time series, allowing similarity matching although one of the sequences can elastic shift along the time axis. Nevertheless, it is more unfortunate that DTW has a quadratic time. Simultaneously the false dismissals are come forth since DTW distance does not satisfy the triangular inequality. In this paper, we propose an efficient range query algorithmbased on a new similarity search method under time warping. When our range query applies for this method, it can remove the significant non-qualify time series as early as possible before computing the accuracy DTW distance. Hence, it speeds up the calculation time and reduces the number of scanning the time series. Guaranteeing no false dismissals, the lower bounding function is advised that consistently underestimate the DTW distance and satisfy the triangular inequality. Through the experimental result, our range query algorithm outperforms the existing others.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.335-344
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• 2014

As a kind of 2D spatial coordinate transform, image warping is a basic image processing technique utilized in various applications. Though image warping algorithm is composed of relatively simple operations such as memory accesses and computations of weighted average, real-time implementations on embedded vision systems suffer from limited computational power because the simple operations are iterated as many times as the number of pixels. This paper presents a real-time implementation of a look-up table(LUT)-based image warping using an FPGA. In order to ensure sufficient data transfer rate from memories storing mapping LUT and image data, appropriate memory devices are selected by analyzing memory access patterns in an LUT-based image warping using backward mapping. In addition, hardware structure of a parallel and pipelined architecture is proposed for fast computation of bilinear interpolation using fixed-point operations. Accuracy of the implemented hardware is verified using a synthesized test image, and an application to real-time lens distortion correction is exemplified.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.290-297
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• 2003

A time-series database is a set of data sequences, each of which is a list of changing values corresponding to an object. Subsequence matching under time warping is defined as an operation that finds such subsequences whose time warping distance to a given query sequence are below a tolerance from a time-series database. In this paper, we first point out the characteristics of the previous methods for time-series sequence matching under time warping, and then discuss the approaches for applying them to whole matching as well as subsequence matching. Also, we perform quantitative performance evaluation via a series of experiments with real-life data. There have not been such researches in the literature that compare the performances of all the previous methods of subsequence matching under time warping. Thus, our results would be used as a good reference for showing their relative performances.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.357-368
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• 2006

This paper discusses the way of processing time-series subsequence matching under time warping. Time warping enables finding sequences with similar patterns even when they are of different lengths. The prefix-querying method is the first index-based approach that performs time-series subsequence matching under time warping without false dismissals. This method employs the $L_{\infty}$ as a base distance function for allowing users to issue queries conveniently. In this paper, we extend the prefix-querying method for absorbing $L_1$, which is the most-widely used as a base distance function in time-series subsequence matching under time warping, instead of $L_{\infty}$. We also formally prove that the proposed method does not incur any false dismissals in the subsequence matching. To show the superiority of our method, we conduct performance evaluation via a variety of experiments. The results reveal that our method achieves significant performance improvement in orders of magnitude compared with previous methods.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.61-69
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• 2011

Dynamic Time Warping based on Dynamic Programming is the one of the most widely been used to compare the similarity of two patterns. DTW algorithm has two known problems. The one is singularities. And the another problem is the accuracy of the warping path with patterns. Therefore, this paper suggest the solution for DTW algorithm to use a 2nd derivative operator. Laplacian of Gaussian is a kind of a 2nd derivative operator. Consequently, our suggestion method to apply to this operator, more efficient to solve the singularities problems and to secure a accuracy of the warping path. And the result shows a superior ability of this suggested method.