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Smart computer vision

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Smart computer visionedited by B. Vinoth Kumar ... [et al.].
其他作者:	Kumar, B. Vinoth.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	x, 358 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer vision.
電子資源:	https://doi.org/10.1007/978-3-031-20541-5
ISBN:	9783031205415$q(electronic bk.)

Smart computer vision
Smart computer vision[electronic resource] /edited by B. Vinoth Kumar ... [et al.]. - Cham :Springer International Publishing :2023. - x, 358 p. :ill. (some col.), digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8609. - EAI/Springer innovations in communication and computing..

Chapter 1: A Systematic Review on Machine Learning based Sports Video Summarization Techniques -- Chapter 2: Shot Boundary Detection from Lecture Video Sequences using Histogram of Oriented Gradients and Radiometric Correlation -- Chapter 3: Detection of Road Potholes using Computer Vision and Machine Learning Approaches to Assist the Visually Challenged -- Chapter 4: Shape Feature Extraction Techniques for Computer Vision Applications -- Chapter 5: GLCM Feature Based Texture image classification using Machine learning algorithms -- Chapter 6: Progress in Multimodal Affective Computing: From Machine Learning to Deep Learning -- Chapter 7: Content based Image Retrieval using Deep features and Hamming Distance -- Chapter 8: Bio Inspired CNN approach for diagnosing COVID-19 using images of Chest X-ray -- Chapter 9: Initial Stage Identification of Covid-19 using Capsule Networks -- Chapter 10: Deep Learning in Auto Encoder Framework and Shape Prior for Hand Gesture Recognition -- Chapter 11: Hierarchical based Semantic segmentation of 3D point cloud using deep learning -- Chapter 12: Convolution Neural Network and Auto-Encoder hybrid scheme for Automatic Colorization of Gray-Scale images -- Chapter 13: Deep learning based Open Set Domain Hyper spectral Image Classification using dimension reduced spectral features -- Chapter 14: An Effective Diabetic Retinopathy Detection using Hybrid Convolutional Neural Network Models -- Chapter 15: Modified Discrete Differential Evolution with Neighbourhood Approach for Grayscale Image Enhancement -- Chapter 16: Swarm-based methods applied to computer vision.

This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field. Introduction to theoretical foundations and practical solution techniques for computer vision applications; Includes a variety of case studies emphasizing social and research perspectives in computer vision; Features contributors form industry, academia, and researchers with a variety of perspectives.

ISBN: 9783031205415$q(electronic bk.)

Standard No.: 10.1007/978-3-031-20541-5doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634 / .S53 2023

Dewey Class. No.: 006.37

Smart computer vision
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505 0 $a Chapter 1: A Systematic Review on Machine Learning based Sports Video Summarization Techniques -- Chapter 2: Shot Boundary Detection from Lecture Video Sequences using Histogram of Oriented Gradients and Radiometric Correlation -- Chapter 3: Detection of Road Potholes using Computer Vision and Machine Learning Approaches to Assist the Visually Challenged -- Chapter 4: Shape Feature Extraction Techniques for Computer Vision Applications -- Chapter 5: GLCM Feature Based Texture image classification using Machine learning algorithms -- Chapter 6: Progress in Multimodal Affective Computing: From Machine Learning to Deep Learning -- Chapter 7: Content based Image Retrieval using Deep features and Hamming Distance -- Chapter 8: Bio Inspired CNN approach for diagnosing COVID-19 using images of Chest X-ray -- Chapter 9: Initial Stage Identification of Covid-19 using Capsule Networks -- Chapter 10: Deep Learning in Auto Encoder Framework and Shape Prior for Hand Gesture Recognition -- Chapter 11: Hierarchical based Semantic segmentation of 3D point cloud using deep learning -- Chapter 12: Convolution Neural Network and Auto-Encoder hybrid scheme for Automatic Colorization of Gray-Scale images -- Chapter 13: Deep learning based Open Set Domain Hyper spectral Image Classification using dimension reduced spectral features -- Chapter 14: An Effective Diabetic Retinopathy Detection using Hybrid Convolutional Neural Network Models -- Chapter 15: Modified Discrete Differential Evolution with Neighbourhood Approach for Grayscale Image Enhancement -- Chapter 16: Swarm-based methods applied to computer vision.
520 $a This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field. Introduction to theoretical foundations and practical solution techniques for computer vision applications; Includes a variety of case studies emphasizing social and research perspectives in computer vision; Features contributors form industry, academia, and researchers with a variety of perspectives.
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