Non linear and Dynamic Time Warping classification of morphological patterns identified from Plethysmographic observations in the radial pulse

2011 ◽  
Author(s):  
S. H. Karamchandani ◽  
M. Panju ◽  
H. D. Mustafa ◽  
S. N. Merchant ◽  
U. B. Desai ◽  
...  
Keyword(s):  
Dynamic Time Warping ◽  
Radial Pulse ◽  
Time Warping ◽  
Non Linear ◽  
Dynamic Time ◽  
Morphological Patterns

Continuous Motion Classification and Segmentation Based on Improved Dynamic Time Warping Algorithm

2017 ◽  
Vol 32 (02) ◽  
pp. 1850002 ◽  
Author(s):  
Mingqin Liu ◽  
Xiaoguang Zhang ◽  
Guiyun Xu
Keyword(s):  
Action Recognition ◽  
Dynamic Time Warping ◽  
The Other ◽  
Continuous Image ◽  
Single Image ◽  
Time Warping ◽  
Continuous Action ◽  
Template Sequence ◽  
Dynamic Time

The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1–26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108–119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.

scholarly journals A Comprehensive Survey on On-line Handwriting Recognition Technology and its Real Application to the Nepalese Natural Handwriting

1970 ◽  
Vol 5 (1) ◽  
pp. 31-55 ◽  
Author(s):  
Santosh KC ◽  
Cholwich Nattee
Keyword(s):  
Dynamic Time Warping ◽  
Feature Vector ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Time Warping ◽  
Real Application ◽  
Non Linear ◽  
Comprehensive Survey ◽  
On Line ◽  
Dynamic Time

Handwriting Recognition Technology has been improving much under the purview of pattern recognition and image processing since a few decades. This paper focuses on the comprehensive survey on on-line handwriting recognition system along with the real application by taking Nepali natural handwriting (a real example of one of the cursive handwritings). The survey mainly includes pre-processing, feature vector and similarity measures in between the non-linear 2D sequences of coordinates, and their effective applications. A very highlighting topic "Dynamic Time Warping Algorithm'' (DTW) is introduced, which has been popular in determining the distance between two non-linear sequences ranging from handwriting to speech recognition. Besides these contemporary research issues/areas, stroke number and order free Nepalese natural handwritten recognition system is presented in the second step. Writing one's own style brings unevenness in writing units, which is the most difficult part to classify. Writing units reveal number, shape, size, order of stroke, and speed in writing. Variation in the number of strokes, their order, shapes and sizes, tilting angles and similarities among characters from one another are the important factors, which are to be considered in classification for Nepali. This paper utilizes structural properties of those alphanumeric characters, which have variable writing units. It uses a string of pen tip's positions and tangent angles of every consecutive point as a feature vector sequence of a stroke. We constructed a prototype recognizer that uses the DTW algorithm to align handwritten strokes with stored strokes' templates and determine their similarity. Separate system is trained for original and preprocessed writing samples and achieved recognition rates of 85.87% and 88.59% respectively. This introduces novel real time handwriting recognition on Nepalese alphanumeric characters, which are independent of number of strokes, as well as their order. Key Words: Handwriting Recognition System; Pre-processing; Feature Vector; Dynamic Time Warping; Agglomerating Hierarchical Clustering; Nepali. DOI: 10.3126/kuset.v5i1.2845 Kathmandu University Journal of Science, Engineering and Technology Vol.5, No.1, January 2009, pp 31-55

Dynamic time warping based connectivity classification of event-related potentials

2019 ◽  
Author(s):  
Kadhum Kareem Al-rubaye ◽  
Oğuz Bayat ◽  
Osman Nuri Ucan ◽  
Dilek Göksel Duru ◽  
Adil Deniz Duru
Keyword(s):  
Dynamic Time Warping ◽  
Event Related Potentials ◽  
Time Warping ◽  
Related Potentials ◽  
Dynamic Time

scholarly journals Classification of genomic signals using dynamic time warping

2013 ◽  
Vol 14 (Suppl 10) ◽  
pp. S1 ◽  
Author(s):  
Helena Skutkova ◽  
Martin Vitek ◽  
Petr Babula ◽  
Rene Kizek ◽  
Ivo Provaznik
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time ◽  
Genomic Signals
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