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Greatest papers with code

Deep TEN: Texture Encoding Network

CVPR 2017 zhanghang1989/PyTorch-Encoding

The representation is orderless and therefore is particularly useful for material and texture recognition.

DICTIONARY LEARNING MATERIAL RECOGNITION

Deep Filter Banks for Texture Recognition and Segmentation

CVPR 2015 mcimpoi/deep-fbanks

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

MATERIAL RECOGNITION SCENE RECOGNITION

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks

npj Computational Materials 2019 PV-Lab/AUTO-XRD

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.

DATA AUGMENTATION INTERPRETABLE MACHINE LEARNING MATERIAL CLASSIFICATION MATERIAL RECOGNITION TIME SERIES CLASSIFICATION X-RAY DIFFRACTION (XRD)

Differential Viewpoints for Ground Terrain Material Recognition

22 Sep 2020jiaxue1993/pytorch-material-classification

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.

AUTONOMOUS DRIVING MATERIAL RECOGNITION ROBOT NAVIGATION

Classification of Household Materials via Spectroscopy

10 May 2018kebasaa/SCIO-read

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.

MATERIAL CLASSIFICATION MATERIAL RECOGNITION

Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks

10 Jul 2017healthcare-robotics/mr-gan

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.

MATERIAL RECOGNITION TIME SERIES

Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging

2 Apr 2020Healthcare-Robotics/spectrovision

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.

MATERIAL CLASSIFICATION MATERIAL RECOGNITION