Recognition of indoor scenes using learning

使用学习识别室内场景

• 批准号: 11680404
• 负责人: HILD Michael
• 金额: $ 1.92万
• 依托单位: Osaka Electro-Communication University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11680404/
• 关键词: object recognition; learning; human actions; シーンの認識; 画像解析; 人間の動作

The goal of this research is the development of improved vision-based object recognition methods for indoor scenes. Our research has focused on the following five major topics :(a) Recognition of indoor scene objects from color image sequences using human actions as a primary cue. We developed a system that first recognizes persons' actions ocurring in the scene and then, based on these, recognizes the objects which are manipulated by these persons(b) Recognition of indoor objects based on finger-pointing actions. Finger-pointing actions are detected and used to determine objects in the scene(c) Identification of persons based on human faces in unconstrained indoor environments. Development of a flexible face recognition system, based on a neural network bank(d) Recognition methods for autonomous driving of wheelchairs in outdoor environments(e) Fundamental techniques needed for improving sensor singnals and low-level data measurements in the context of the recognition problemi. Rotational imaging for the reconstruction of 3-D data points of scenesii. Background-frame differencing methods based on color similarityiii. Low-noise image acquistion using contemporary image sensor technology

本研究的目标是开发改进的基于视觉的室内场景目标识别方法。我们的研究主要集中在以下五个方面：（a）利用人类行为作为主要线索从彩色图像序列中识别室内场景对象。我们开发了一个系统，首先识别人的动作发生在场景中，然后，基于这些，识别这些人操纵的对象（B）识别室内物体的手指指向动作的基础上。检测手指指向动作并用于确定场景中的对象。（c）在不受约束的室内环境中基于人脸的人的识别。开发一个灵活的人脸识别系统，基于神经网络银行（d）在户外环境中的轮椅自动驾驶的识别方法（e）在识别问题的背景下，改善传感器信号和低级别数据测量所需的基本技术。利用旋转成像法重建景天科植物三维数据点。基于颜色相似性的背景帧差分方法。采用现代图像传感器技术的低噪声图像采集

项目成果

吉田 一展, 橋本 元信, Michael Hild: "ニューラルネットワーク・バンクを用いた顔方向に依存しない顔認識"電子情報通信学会 技術研究報告. PRMU2001-283. 89-95 (2002)

Kazunobu Yoshida、Motonobu Hashimoto、Michael Hild：“使用神经网络库进行独立于面部方向的面部识别”PRMU2001-283 (2002)。

Michael Hild, Toshinobu Emura: "Which color similarity measure is most effective for background-frame differencing 2"Proceedings of the IS&T/SID Ninth Color Imaging Conference. 168-173 (2001)

Michael Hild、Toshinobu Emura：“哪种颜色相似性测量对于背景帧差异 2 最为有效”IS 会议记录

Michael Hild: "Which color similarity measure is most effective for background-frame differencing?"Proc. of the IS&T/SID Ninth Color Imaging Conference. 1. 168-173 (2001)

Michael Hild：“哪种颜色相似性度量对于背景帧差异最有效？”Proc。

Michael Hild: "On the effectiveness of color similarity measures in background-frame differencing applications"Proc. of the First European Conference on Color in Graphics, Imaging and Vision. 1. 332-337 (2002)

Michael Hild：“关于背景帧差异应用中颜色相似性测量的有效性”Proc。

Michael Hild: "Piecewise-linear, Bounded-SNR Imaging for Low-Noise Image Acquistion"Proc. Of the IASTED International Conference Computer Graphics and Imaging, 93-100, Las Vegas, USA. (2000)

Michael Hild：“用于低噪声图像采集的分段线性、有界 SNR 成像”Proc。