Computer vision -- ACCV 2018 Workshops : 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, revised selected papers
Author(s)
Bibliographic Information
Computer vision -- ACCV 2018 Workshops : 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, revised selected papers
(Lecture notes in computer science, 11367 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2019
- Other Title
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ACCV 2018
Available at 1 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Other editors: Hongdong Li, Greg Mori, Konrad Schindler
Includes bibliographical references and author index
Description and Table of Contents
Description
This LNCS workshop proceedings, ACCV 2018, contains carefully reviewed and selected papers from 11 workshops, each having different types or programs: Scene Understanding and Modelling (SUMO) Challenge, Learning and Inference Methods for High Performance Imaging (LIMHPI), Attention/Intention Understanding (AIU), Museum Exhibit Identification Challenge (Open MIC) for Domain Adaptation and Few-Shot Learning, RGB-D - Sensing and Understanding via Combined Colour and Depth, Dense 3D Reconstruction for Dynamic Scenes, AI Aesthetics in Art and Media (AIAM), Robust Reading (IWRR), Artificial Intelligence for Retinal Image Analysis (AIRIA), Combining Vision and Language, Advanced Machine Vision for Real-life and Industrially Relevant Applications (AMV).
Table of Contents
Scene Understanding and Modelling (SUMO) Challenge.- Learning and Inference Methods for High Performance Imaging (LIMHPI).- Attention/Intention Understanding (AIU).- Museum Exhibit Identification Challenge (Open MIC) for Domain Adaptation and Few-Shot Learning.- RGB-D - Sensing and Understanding via Combined Colour and Depth.- Dense 3D Reconstruction for Dynamic Scenes.- AI Aesthetics in Art and Media (AIAM).- Robust Reading (IWRR), Artificial Intelligence for Retinal Image Analysis (AIRIA).- Combining Vision and Language, Advanced Machine Vision for Real-life and Industrially Relevant Applications (AMV).
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