Topic
Dynamic time warping
About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.
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TL;DR: A unified framework for simultaneously performing spatial segmentation, temporal segmentsation, and recognition is introduced and can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds.
Abstract: Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American sign language (ASL).
392 citations
06 Aug 2002
TL;DR: A novel classification approach for online handwriting recognition is described that combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel that is directly addresses the problem of discrimination by creating class boundaries and thus is less sensitive to modeling assumptions.
Abstract: In this paper we describe a novel classification approach for online handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel. This kernel approach has a main advantage over common HMM techniques. It does not assume a model for the generative class conditional densities. Instead, it directly addresses the problem of discrimination by creating class boundaries and thus is less sensitive to modeling assumptions. By incorporating DTW in the kernel function, general classification problems with variable-sized sequential data can be handled. In this respect the proposed method can be straightforwardly applied to all classification problems, where DTW gives a reasonable distance measure, e.g., speech recognition or genome processing. We show experiments with this kernel approach on the UNIPEN handwriting data, achieving results comparable to an HMM-based technique.
377 citations
TL;DR: An effective fingerprint verification system is presented, which assumes that an existing reference fingerprint image must validate the identity of a person by means of a test fingerprint image acquired online and in real-time using minutiae matching.
Abstract: An effective fingerprint verification system is presented. It assumes that an existing reference fingerprint image must validate the identity of a person by means of a test fingerprint image acquired online and in real-time using minutiae matching. The matching system consists of two main blocks: The first allows for the extraction of essential information from the reference image off-line, the second performs the matching itself online. The information is obtained from the reference image by filtering and careful minutiae extraction procedures. The fingerprint identification is based on triangular matching to cope with the strong deformation of fingerprint images due to static friction or finger rolling. The matching is finally validated by dynamic time warping. Results reported on the NIST Special Database 4 reference set, featuring 85 percent correct verification (15 percent false negative) and 0.05 percent false positive, demonstrate the effectiveness of the verification technique.
376 citations
TL;DR: It is shown in a subset of the George Washington collection that such a word spotting technique can outperform a Hidden Markov Model word-based recognition technique in terms of word error rates.
Abstract: Searching and indexing historical handwritten collections are a very challenging problem. We describe an approach called word spotting which involves grouping word images into clusters of similar words by using image matching to find similarity. By annotating “interesting” clusters, an index that links words to the locations where they occur can be built automatically. Image similarities computed using a number of different techniques including dynamic time warping are compared. The word similarities are then used for clustering using both K-means and agglomerative clustering techniques. It is shown in a subset of the George Washington collection that such a word spotting technique can outperform a Hidden Markov Model word-based recognition technique in terms of word error rates.
368 citations
TL;DR: The algorithm to be developed is essentially identical to one presented by Vintsyuk and later by Bridle and Brown, but the notation and the presentation have been clarified and the computational expenditure per word is independent of the number of words in the input string.
Abstract: This paper is of tutorial nature and describes a one-stage dynamic programming algorithm for file problem of connected word recognition. The algorithm to be developed is essentially identical to one presented by Vintsyuk [1] and later by Bridle and Brown [2] ; but the notation and the presentation have been clarified. The derivation used for optimally time synchronizing a test pattern, consisting of a sequence of connected words, is straightforward and simple in comparison with other approaches decomposing the pattern matching problem into several levels. The approach presented relies basically on parameterizing the time warping path by a single index and on exploiting certain path constraints both in the word interior and at the word boundaries. The resulting algorithm turns out to be significantly more efficient than those proposed by Sakoe [3] as well as Myers and Rabiner [4], while providing the same accuracy in estimating the best possible matching string. Its most important feature is that the computational expenditure per word is independent of the number of words in the input string. Thus, it is well suited for recognizing comparatively long word sequences and for real-time operation. Furthermore, there is no need to specify the maximum number of words in the input string. The practical implementation of the algorithm is discussed; it requires no heuristic rules and no overhead. The algorithm can be modified to deal with syntactic constraints in terms of a finite state syntax.
364 citations