Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023)
Published on 2. April 2025
Book
Paperback/Softback
XV, 770 pages
978-981-97-0857-4 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Publishing group
Springer Verlag, Singapore
Target group
Professional and scholarly
Illustrations
308 farbige Abbildungen, 74 s/w Abbildungen
XV, 770 p. 382 illus., 308 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 42 mm
Weight
1171 gr
ISBN-13
978-981-97-0857-4 (9789819708574)
DOI
10.1007/978-981-97-0855-0
Schweitzer Classification
Other editions
Additional editions
Peng You | Shuaiqi Liu | Jun Wang
Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023)
Book
02/2024
Springer
€353.09
Shipment within 3-4 weeks
Persons
Content
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.