Challenges And Applications For Implementing Machine Learning In Computer Vision

Download Challenges And Applications For Implementing Machine Learning In Computer Vision full books in PDF, epub, and Kindle. Read online free Challenges And Applications For Implementing Machine Learning In Computer Vision ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Challenges and Applications for Implementing Machine Learning in Computer Vision

Challenges and Applications for Implementing Machine Learning in Computer Vision
Author :
Publisher : IGI Global
Total Pages : 318
Release :
ISBN-10 : 9781799801849
ISBN-13 : 1799801845
Rating : 4/5 (845 Downloads)

Book Synopsis Challenges and Applications for Implementing Machine Learning in Computer Vision by : Kashyap, Ramgopal

Download or read book Challenges and Applications for Implementing Machine Learning in Computer Vision written by Kashyap, Ramgopal and published by IGI Global. This book was released on 2019-10-04 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.


Challenges and Applications for Implementing Machine Learning in Computer Vision Related Books

Challenges and Applications for Implementing Machine Learning in Computer Vision
Language: en
Pages: 318
Authors: Kashyap, Ramgopal
Categories: Computers
Type: BOOK - Published: 2019-10-04 - Publisher: IGI Global

DOWNLOAD EBOOK

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computer
Machine Learning in Computer Vision
Language: en
Pages: 253
Authors: Nicu Sebe
Categories: Computers
Type: BOOK - Published: 2005-10-04 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of com
Practical Computer Vision Applications Using Deep Learning with CNNs
Language: en
Pages: 421
Authors: Ahmed Fawzy Gad
Categories: Computers
Type: BOOK - Published: 2018-12-05 - Publisher: Apress

DOWNLOAD EBOOK

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural net
Computer Vision in Medical Imaging
Language: en
Pages: 410
Authors: Chi-hau Chen
Categories: Medical
Type: BOOK - Published: 2013-11-18 - Publisher: World Scientific

DOWNLOAD EBOOK

The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of disea
Computer Vision
Language: en
Pages: 599
Authors: Simon J. D. Prince
Categories: Computers
Type: BOOK - Published: 2012-06-18 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.