Content-Based Haptic Texture Retrieval

基于内容的触觉纹理检索

基本信息

项目摘要

While stroking a rigid tool over the surface of an object, vibrations are induced on the tool. Measured with an accelerometer, the corresponding acceleration signals carry important information about the surface texture and can be used to classify or to recognize the surface material. In the proposed research project we plan to go beyond texture recognition. Our objective is to develop methods and algorithms, which allow for the retrieval of perceptually similar textures from a database of available textures. A tangible future application for texture retrieval is product browsing and customization, for example for the selection of materials for car interiors or for furniture. Based on personal preferences, a subset of materials possessing a haptic feel similar to the one chosen before may be retrieved. We refer to this novel concept as Content-Based Haptic Texture Retrieval (CBHTR).Essential ingredients of CBHTR are mathematical fingerprints representing the texture, so-called features, that are robust against external influences. When a human strokes a rigid tool over an object surface, the applied force, the scan-velocity or the inclination between the tool and the surface might vary drastically during the surface exploration and between subsequent exploration sessions. Such scan-time parameters heavily influence the nature of the acquired acceleration signals. Scan-invariant features for tool-mediated haptic texture exploration are currently lacking. Once scan-invariant features are available, robust texture recognition systems can be built. Texture retrieval systems, which are able to return perceptually similar materials, require features that additionally capture the perceptual similarity between textures. Features that are inspired by the way humans perceive textures are promising candidates along this line. Together, the development of robust and perceptually relevant features constitutes the first main objective of this proposal.The second objective is to apply and evaluate these features in a prototypical texture recognition and texture retrieval system, where the latter is supposed to find all perceptually similar textures in a database. To this end, appropriate combinations of features and machine learning approaches need to be identified. A systematic evaluation using subjective similarity scores plays a special role in this context.Finally, in order to further improve the retrieval performance, we additionally plan to fuse the information sensed during the interaction by other low-cost sensors like cameras and microphones with the information gathered with the accelerometer.
当用刚性工具抚摸物体表面时,工具上会产生振动。通过加速度计测量,相应的加速度信号携带有关表面纹理的重要信息,可用于对表面材料进行分类或识别。在拟议的研究项目中,我们计划超越纹理识别。我们的目标是开发方法和算法,允许从可用纹理数据库中检索感知上相似的纹理。纹理检索的一个切实的未来应用是产品浏览和定制,例如选择汽车内饰或家具的材料。基于个人喜好,可以检索具有与之前选择的材料类似的触觉感觉的材料子集。我们将这个新颖的概念称为基于内容的触觉纹理检索(CBHTR)。CBHTR 的基本成分是表示纹理的数学指纹,即所谓的特征,它们对外部影响具有鲁棒性。当人类在物体表面上敲击刚性工具时,所施加的力、扫描速度或工具与表面之间的倾斜度可能在表面探索期间以及后续探索会话之间发生巨大变化。这种扫描时间参数严重影响所采集的加速度信号的性质。目前缺乏用于工具介导的触觉纹理探索的扫描不变特征。一旦扫描不变特征可用,就可以构建强大的纹理识别系统。纹理检索系统能够返回感知相似的材料,需要额外捕获纹理之间感知相似性的特征。受人类感知纹理方式启发的特征是沿着这条线的有希望的候选者。总之,鲁棒性和感知相关特征的开发构成了该提案的第一个主要目标。第二个目标是在原型纹理识别和纹理检索系统中应用和评估这些特征,后者应该在数据库中找到所有感知相似的纹理。为此,需要确定特征和机器学习方法的适当组合。使用主观相似性分数的系统评估在这种情况下发挥着特殊的作用。最后,为了进一步提高检索性能,我们还计划将其他低成本传感器(如摄像头和麦克风)在交互过程中感测到的信息与加速度计收集的信息融合起来。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multimodal Feature-Based Surface Material Classification
  • DOI:
    10.1109/toh.2016.2625787
  • 发表时间:
    2017-04-01
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    Strese, Matti;Schuwerk, Clemens;Steinbach, Eckehard
  • 通讯作者:
    Steinbach, Eckehard
Deep Learning for Surface Material Classification Using Haptic and Visual Information
使用触觉和视觉信息进行表面材料分类的深度学习
  • DOI:
    10.1109/tmm.2016.2598140
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    7.3
  • 作者:
    Zheng Haitian;Fang Lu;Ji Mengqi;Strese Matti;Ozer Yigitcan;Steinbach Eckehard
  • 通讯作者:
    Steinbach Eckehard
Content-based surface material retrieval
  • DOI:
    10.1109/whc.2017.7989927
  • 发表时间:
    2017-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Matti Strese;Yannik N. Böck;E. Steinbach
  • 通讯作者:
    Matti Strese;Yannik N. Böck;E. Steinbach
Toward high-fidelity haptic interaction with virtual materials: A robotic material scanning, modelling, and display system
实现与虚拟材料的高保真触觉交互:机器人材料扫描、建模和显示系统
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Professor Dr.-Ing. Eckehard Steinbach其他文献

Professor Dr.-Ing. Eckehard Steinbach的其他文献

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{{ truncateString('Professor Dr.-Ing. Eckehard Steinbach', 18)}}的其他基金

Teleoperation over 5G Networks: Enabling Haptic Interaction in Mobile Audio-Visual Communications
通过 5G 网络进行远程操作:在移动视听通信中实现触觉交互
  • 批准号:
    391986140
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Efficient Recording and Playback of Haptic Interaction
触觉交互的高效记录和回放
  • 批准号:
    171279129
  • 财政年份:
    2010
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Echtzeitübertragung und Streaming von Video in paketorientierten drahtlosen Mehrbenutzer-Netzwerken
面向分组的多用户无线网络中视频的实时传输和流传输
  • 批准号:
    5408272
  • 财政年份:
    2004
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes

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Reality-based Interaction用户界面模型和评估方法研究
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