Irie / Shin / Shibata Frontiers of Computer Vision

30th International Workshop, IW-FCV 2024, Tokyo, Japan, February 19–21, 2024, Revised Selected Papers
Erscheinungsjahr 2024
ISBN: 978-981-97-4249-3
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2143, 161 Seiten, eBook

Reihe: Communications in Computer and Information Science

This book constitutes the revised selected papers from the 30th International Workshop on Frontiers of Computer Vision, IW-FCV 2024, held in Tokyo, Japan, in February 19–21, 2024. IW-FCV 2024 is an annual workshop that brings together researchers in the field of computer vision and artificial intelligence to share their research results. This workshop was started 30 years ago as a way to strengthen networking and share research results between Japanese and Korean researchers. It has since grown in scope and influence and has become an international event since 2017. 

The 12 full papers, carefully reviewed and selected from 61 submissions, primarily focus on the fundamental theories, techniques, and algorithms related to computer vision and image signal processing, with particular emphasis on practical applications. These papers deal with the following topics: Fundamentals and Theory (e.g., image filtering / enhancement / restoration, color and illumination analysis, and image coding), Computer Vision and Image Analysis (e.g., shape from-X, object detection and tracking, and deep learning for computer vision), Applications (e.g., image/video search and retrieval, surveillance, AR/VR/MR/HR, and bio-medical image analysis), and Recognition and Learning (e.g., 2D/3D object recognition, face and gesture recognition, and human pose estimation).

Zielgruppe

Research

Autoren/Hrsg.

Irie, Go

Shin, Choonsung

Shibata, Takashi

Nakamura, Kazuaki

Inhaltsverzeichnis

.- Tackling Background Misclassification in Box-supervised Segmentation: A Background Constraint Approach.
.- Clustering of Face Images in Video by Using Deep Learning.
.- Exploring the Impact of Various Contrastive Learning Loss Functions on Unsupervised Domain Adaptation in Person Re-identification.
.- Automatic Measured Drawing Generation for Mokkan Using Deep Learning.
.- Monocular Absolute 3D Human Pose Estimation with an Uncalibrated Fixed Camera.
.- Technical Skill Evaluation and Training using Motion Curved Surface in Considered Velocity and Acceleration.
.- A Benchmark for 3D Reconstruction with Semantic Completion in Dynamic Environments.
.- Framework for Measuring the Similarity of Visual and Semantic Structures in Sign Languages.
.- Human Facial Age Group Recognizer using Assisted Bottleneck Transformer Encoder.
.- Efficient Detection Model using Feature Maximizer Convolution for Edge Computing. 
.- Spatial Attention Network with High Frequency Component for Facial Expression Recognition.
.- Minor Object Recognition from Drone Image Sequence.