Granular Video Computing: With Rough Sets, Deep Learning And In Iot
This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
1138076708
Granular Video Computing: With Rough Sets, Deep Learning And In Iot
This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
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Granular Video Computing: With Rough Sets, Deep Learning And In Iot

Granular Video Computing: With Rough Sets, Deep Learning And In Iot

by Debarati Bhunia Chakraborty, Sankar Kumar Pal
Granular Video Computing: With Rough Sets, Deep Learning And In Iot

Granular Video Computing: With Rough Sets, Deep Learning And In Iot

by Debarati Bhunia Chakraborty, Sankar Kumar Pal

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Overview

This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.

Product Details

ISBN-13: 9789811227110
Publisher: World Scientific Publishing Company, Incorporated
Publication date: 03/03/2021
Pages: 256
Product dimensions: 6.00(w) x 9.00(h) x 0.63(d)

Table of Contents

Introduction: Video Processing, Granular Computing, Rough Sets, Deep Learning and IoT; Partial Supervised Tracking; Unsupervised Tracking; Unsupervised Occlusion Handling; Trustability Measures of Tracking Algorithms; Object Recognition and Deep Learning; Video Conceptualization
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