While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. We introduce a haptic texture database which allows for a systematic analysis of feature candidates. The database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 69 different textures.
More details about the textures and the download link can be found under zeus.lmt.ei.tum.de/downloads/texture/
A detailed description of the hardware setup and the database can be found in the corresponding paper:
- Preprocessing-free Surface Material Classification using Convolutional Neural Networks Pretrained by Sparse Autoencoder. IEEE International Workshop on Machine Learning for Signal Processing, 2015 more… BibTeX
- On the Retrieval of Perceptually Similar Haptic Surfaces. International Workshop on Quality of Multimedia Experience (QoMEX), 2015 more… BibTeX
- Surface Classification Using Acceleration Signals Recorded During Human Freehand Movement. IEEE World Haptics Conference, 2015 more… BibTeX