Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022)

2023
ISBN: 978-981-99-0925-4
Verlag: Springer Nature Singapore

Buch, Englisch, Band 1019, 1052 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1635 g

Reihe: Lecture Notes in Electrical Engineering

This book is a collection of the papers accepted by the ICIVIS 2022—The International Conference on Image, Vision and Intelligent Systems, held on August 15–17, 2022, in Jinan, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state of practice in the topics covered by this conference proceedings.

Zielgruppe

Research

Autoren/Hrsg.

You, Peng

Chen, Zhenxiang

Li, Heng

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Denoising Supervision Based Generative Adversarial Networks for MRI Super-Resolution Reconstruction.- 3D Pot-Shaped Object Localization Based on RGB-Image and Point Clouds.- Edge Matching of Image with Large Viewpoint Variation Based on Relative Motion Direction of Edge Corners.- Research on Image Recognition in Complex Background Based on Improved Bilinear CNN Model.- High Precision Odometer and Mapping Algorithm Based on Multi Lidar Fusion.- Classification Model for Easily Confused Breast Ultrasound Image Based on Shape and Texture Features.- Automated Identification for High Voltage Isolation Switch via Generative Adversarial Network.- Fundus Image Registration Based on Improved Feature Description.- Split dynamic target counting framework based on Yolov5+Deepsort optimization.- Clustering of Cervical Histopathology Images based on Minimum Spanning Tree.