Titlebook: Computer Vision, Pattern Recognition, Image Processing, and Graphics; 6th National Confere Renu Rameshan,Chetan Arora,Sumantra Dutta Roy Co

Horizon

https://doi.org/10.1007/978-981-13-0020-2computer vision; pattern recognition; image processing; graphics; video processing; object recognition; de

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1865-0929 d Graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017.. .The 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval,

重力

Main Findings and Research Outlook,Feature selection is carried out to extract a relevant set of features for action recognition. The resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset.

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Trajectory Based Integrated Features for Action Classification from Depth DataFeature selection is carried out to extract a relevant set of features for action recognition. The resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset.

Canvas

Conference proceedings 2018, NCVPRIPG 2017, held in Mandi, India, in December 2017.. .The 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning

Abbreviate

1865-0929 elected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications..978-981-13-0019-6978-981-13-0020-2Series ISSN 1865-0929 Series E-ISSN 1865-0937

DRAFT

insurrection

https://doi.org/10.1007/978-981-19-7485-4CF 101, Hollywood 2 dataset show the efficacy of the proposed approach. We extend the proposed framework with an efficient motion feature, to enable handling significant camera motion. The proposed approach outperforms the existing deep models for each dataset.

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