13 papers with code • 0 benchmarks • 7 datasets
These leaderboards are used to track progress in Material Recognition
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.)
Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.
A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition.