Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023)

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Peng You Shuaiqi Liu

Proceedings of International Conference on Image, Vision and Intelligent Systems 2023

ICIVIS 2023

Peng You, Shuaiqi Liu, Jun Wang

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English

Springer Nature
25 February 2024

Electronics engineering; Imaging systems & technology; Machine learning; Signal processing; Image processing

Series: Lecture Notes in Electrical Engineering

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.

Edited by: Peng You, Shuaiqi Liu, Jun Wang
Imprint: Springer Nature
Country of Publication: Singapore
Edition: 2024 ed.
Volume: 1163
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9789819708543
ISBN 10: 9819708540
Series: Lecture Notes in Electrical Engineering
Pages: 770
Publication Date: 25 February 2024
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.