发帖
雷达卡
摘要翻译：
手势识别主要是基于对人类智力功能的分析。手势识别的主要目标是建立一个能够识别特定人类手势的系统，并利用它们来传递信息或进行设备控制。手势为表达一个人的想法提供了一种单独的语言补充方式。会话中与手势相关的信息有程度、语篇结构、时空结构。现有的方法主要可分为基于数据手套的方法和基于视觉的方法。一个重要的面部特征点是鼻尖。因为鼻子是脸部最高的突出点。除此之外，它不受面部表情的影响。鼻子的另一个重要功能是它能够指示头部的姿势。鼻子位置的知识将使我们能够将未知的三维人脸与人脸数据库中的人脸对齐。眼睛检测分为眼睛位置检测和眼睛轮廓检测。现有的眼睛检测工作可以分为两大类：传统的基于图像的被动检测方法和基于红外的主动检测方法。前者利用眼睛的强度和形状进行检测，后者假设眼睛在近红外照明下有反射，产生亮/暗瞳孔效应。传统的方法大致可分为三类：基于模板的方法、基于外观的方法和基于特征的方法。本文的目的是比较各种人体手势识别系统，在环境中不需要任何身体介质，直接将机器与人的智慧连接起来。
---
英文标题：
《Survey on Various Gesture Recognition Techniques for Interfacing
Machines Based on Ambient Intelligence》
---
作者：
Har*****h C, Karthik R. Shastry, Manoj Ravindran, M.V.V.N.S. Srikanth,
Naveen Lakshmikhanth
---
最新提交年份：
2010
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Human-Computer Interaction 人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
--
一级分类：Computer Science 计算机科学
二级分类：Robotics 机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
--
---
英文摘要：
Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree,discourse structure, spatial and temporal structure. The approaches present can be mainly divided into Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions.Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detection can be classified into two major categories: traditional image-based passive approaches and the active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupil effect. The traditional methods can be broadly classified into three categories: template based methods,appearance based methods and feature based methods. The purpose of this paper is to compare various human Gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment.
---
PDF链接：
https://arxiv.org/pdf/1012.0084
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
京公网安备 11010802022788号
