计算机类|期刊】SCI期刊专刊截稿信息4条

人工智能

Pattern Recognition Letters

Virtual Special Issue on Pattern Recognition Techniques for Non Verbal Human Behavior (NVHB)

全文截稿: 2018-07-31

影响因子: 1.995

想发高水平论文同志可以直接略过；

2)
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
  

   http://www.ccis2k.org/iajit/index.php 

   ISSN: 1683-3198
   Index: SCI   IF：0.39

3)
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
   ISSN: 1319-8025
   Index: SCI

4)
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
   ISSN: 0020-7160
   Index: SCI

5)
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
   ISSN: 0377-0427
   Index: SCI

6)
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
   ISSN: 0129-0657
   Index: SCI

7)
INTEGRATED COMPUTER-AIDED ENGINEERING
   ISSN: 1069-2509
   Index: SCI
http://www.iospress.nl/journal/i ... aided-engineering/#

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/pattern-recognition-letters/

Fundamental Cues for Non-Verbal behavioral are human communication and interaction. Despite Significant advances in recent years, state of the art human-machine systems still falls short in sensing, analyzing and fully understanding cues naturally expressed in everyday settings. Two of the most important non-verbal cues, evidenced by a large body of work in experimental psychology and behavioral sciences, are visual behavior and body language. 

Widely anticipated in HCI is that computing will move to the background, weaving itself into the fabric of our everyday living and projecting the human user into the foreground. To realize this goal, next-generation computing will need to develop human-centered user interfaces that respond readily to naturally occurring, multimodal, human communication. These interfaces will need the capacity to perceive, understand, and respond appropriately to human intentions and cognitive- emotional states as communicated by social and affective signals. 

Motivated by this visionof the future, automated analysis of nonverbal behavior has attracted increasing attention in diverse disciplines, including psychology, computer science, linguistics, and neuroscience. Promising approaches have been reported, especially in the areas of facial expression and multimodal communication. Yet, increasing evidence suggests that deliberate or posed behavior differs in appearance and timing from that which occurs in daily life. Approaches to automatic behavior analysis that have been trained on deliberate and typically exaggerated behaviors may fail to generalize to the complexity of expressive behavior found in real-world settings. 

This Virtual Special Issue (VSI) intends to bring together researchers and developers from academic fields and industries worldwide working in the broad areas of computer vision and promote community-wide discussion of ideas that will influence and foster continued research in this field for the betterment of human mankind. 

Papers submitted to this VSI and accepted for publication will be spread through several regular issues, since each accepted paper will be published as soon as possible without waiting until all submissions to the VSI are in final status. The accepted papers will also be gathered as part of a VSI that will be available exclusively online and will be gradually built up as the individual articles are published online. 

Recommended topics are given below: 
- Intelligent visual surveillance 
- Deep learning based Gait recognition 
- Machine Learning approaches 
- Semi supervised learning based behavior analysis 
- Deep learning for facial expression behavior 
- Real world application of behavior analysis 
- Time-critical techniques to understand gestural behavior 
- Sensor data interpretation for live behavior analysis

Fundamental Cues for Non-Verbal behavioral are human communication and interaction. Despite Significant advances in recent years, state of the art human-machine systems still falls short in sensing, analyzing and fully understanding cues naturally expressed in everyday settings. Two of the most important non-verbal cues, evidenced by a large body of work in experimental psychology and behavioral sciences, are visual behavior and body language.
非言语行为的基本线索是人际交流和互动。尽管近年来取得了巨大的进步，但最先进的人机系统在感知、分析和充分理解日常环境中自然表达的信号方面仍然存在不足。实验心理学和行为科学的大量工作证明了两个最重要的非语言暗示，即视觉行为和肢体语言。
Widely anticipated in HCI is that computing will move to the background, weaving itself into the fabric of our everyday living and projecting the human user into the foreground. To realize this goal, next-generation computing will need to develop human-centered user interfaces that respond readily to naturally occurring, multimodal, human communication. These interfaces will need the capacity to perceive, understand, and respond appropriately to human intentions and cognitive- emotional states as communicated by social and affective signals.
在人机交互中，人们普遍期望计算机能移动到背景中，编织到我们日常生活的结构中，并将人类用户投射到前景中。为了实现这一目标，下一代计算将需要开发以人为中心的用户界面，对自然发生的、多模态的、人类的通信做出反应。这些接口将需要感知、理解和响应人类意图和认知情绪状态的能力，这种能力是由社会和情感信号传递的。
Motivated by this visionof the future, automated analysis of nonverbal behavior has attracted increasing attention in diverse disciplines, including psychology, computer science, linguistics, and neuroscience. Promising approaches have been reported, especially in the areas of facial expression and multimodal communication. Yet, increasing evidence suggests that deliberate or posed behavior differs in appearance and timing from that which occurs in daily life. Approaches to automatic behavior analysis that have been trained on deliberate and typically exaggerated behaviors may fail to generalize to the complexity of expressive behavior found in real-world settings.
出于这一展望未来，非言语行为自动分析吸引了越来越多的关注，在不同的学科，包括心理学、计算机科学、语言学和神经科学。有前途的方法已被报道，特别是在面部表情和多模态通信领域。然而，越来越多的证据表明，故意或摆出的行为在外表和时间上与日常生活中所发生的不同。自动行为分析的方法，经过深思熟虑的，通常是夸张的行为训练，可能无法推广到在现实世界中设置的表达行为的复杂性。
This Virtual Special Issue (VSI) intends to bring together researchers and developers from academic fields and industries worldwide working in the broad areas of computer vision and promote community-wide discussion of ideas that will influence and foster continued research in this field for the betterment of human mankind.
这个虚拟的特殊问题（VSI）旨在汇集研究人员和开发人员从学术领域和行业在世界各地的计算机视觉方面的工作，促进社区广泛讨论的想法会影响和人类对人类的改良培育继续在这一领域的研究。
Papers submitted to this VSI and accepted for publication will be spread through several regular issues, since each accepted paper will be published as soon as possible without waiting until all submissions to the VSI are in final status. The accepted papers will also be gathered as part of a VSI that will be available exclusively online and will be gradually built up as the individual articles are published online.
论文提交本VSI和接受出版将通过几个常规问题的蔓延，因为每个接受的论文将尽快公布没有等到所有提交给逆变器在最终状态。该接受的论文将收集的VSI将只提供在线和将逐渐建立起来的个人物品在网上公布的部分。
Recommended topics are given below:
专题推荐如下：
- Intelligent visual surveillance
智能视觉监控
- Deep learning based Gait recognition
基于步态识别的深度学习
- Machine Learning approaches
机器学习方法
- Semi supervised learning based behavior analysis
基于行为分析的半监督学习
- Deep learning for facial expression behavior
-面部表情行为的深层学习
- Real world application of behavior analysis
-行为分析现实世界中的应用
- Time-critical techniques to understand gestural behavior
时间临界技术理解手势的行为
- Sensor data interpretation for live behavior analysis
对于生活行为分析传感器数据的解释