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Third International Conference on Image Processing and Capsule NetworksICIPCN 2022 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Third International Conference on Image Processing and Capsule Networksedited by Joy Iong-Zong Chen, Joao Manuel R. S. Tavares, Fuqian Shi.
Reminder of title:	ICIPCN 2022 /
remainder title:	ICIPCN 2022
other author:	Chen, Joy Iong-Zong.
corporate name:
Published:	Cham :Springer International Publishing :2022.
Description:	xv, 841 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Image processingCongresses.Digital techniques
Online resource:	https://doi.org/10.1007/978-3-031-12413-6
ISBN:	9783031124136$q(electronic bk.)

Third International Conference on Image Processing and Capsule NetworksICIPCN 2022 /
Third International Conference on Image Processing and Capsule NetworksICIPCN 2022 /[electronic resource] :ICIPCN 2022edited by Joy Iong-Zong Chen, Joao Manuel R. S. Tavares, Fuqian Shi. - Cham :Springer International Publishing :2022. - xv, 841 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,v. 5142367-3389 ;. - Lecture notes in networks and systems ;v.1..

This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations. Image processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings. Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.

ISBN: 9783031124136$q(electronic bk.)

Standard No.: 10.1007/978-3-031-12413-6doiSubjects--Topical Terms:

384512
Image processing
--Digital techniques--Congresses.

LC Class. No.: TA1637 / .I57 2022

Dewey Class. No.: 006.42

Third International Conference on Image Processing and Capsule NetworksICIPCN 2022 /
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