Overview

This book constitutes the refereed proceedings of ICIVIS2023, held in Baoding, China, in August 2023. The papers included in the proceedings have been carefully reviewed and selected from the submitted manuscripts in the areas of image, vision and intelligent systems. This book provides a reference for theoretical innovative problems as well as recent practical solutions and applications for the state-of-the-art results in image, vision and intelligent systems. The intended audience of the book includes researchers, professors, experts, practitioners and professionals in the field of image, vision and intelligent systems worldwide.

Full Product Details

Author:   Peng You ,  Shuaiqi Liu ,  Jun Wang
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Nature
Edition:   2024 ed.
Volume:   1163
ISBN:  

9789819708543

ISBN 10:   9819708540
Pages:   770
Publication Date:   25 February 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

High Order Conditional Random Field based Cervical Cancer Histopathological Image Classification.- Skin Cancer Image Identification Using Deep Convolutional Neural Networks.- Deep Learning-based Prediction of Myelosuppression in Lymphoma Patients during Chemotherapy Using Multimodal Radiological Images with Subcutaneous Adipose Tissue.- Retrievable Image Encryption Based on Adaptive Block Compressed Sensing.- Research on Prostate Cancer Pathological Image Classification Method based on Vision Transformer.- Multi-Disease Detection and Segmentation of Chest CT Images Based on Coarse-to-Fine Pipeline Models.- Dual Branch Image-guided Network with Multi-stage Iterative Refinement for Depth Completion.- CT images super-resolution reconstruction Using Bi-Level Routing Attention and Consecutive Dilated Convolutions.- ASPCD-UNet: An improved network for change detection.- SE-UNet: Channel Attention based UNet for Water body  Segmentation from SAR Image.

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