computer-vision-8

Material Classification

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Classification of Household Materials via Spectroscopy

C. Kemp, Zackory Erickson, S. Chernova, Nathan Luskey•Wed May 09 2018

It is found that spectroscopy poses a promising approach for material classification during robotic manipulation, and how a PR2 robot can leverage spectrometers to estimate the materials of everyday objects found in the home is demonstrated.

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

Felipe Oviedo, T. Buonassisi, Brian L. DeCost, A. Kusne, Zekun Ren, S. Tian, Shijing Sun, C. Settens, Zhe Liu, N. T. P. Hartono, R. Savitha, Giuseppe Romano•Mon Nov 19 2018

The sparse-data problem intrinsic to novel materials development is overcome by coupling a supervised machine-learning approach with a physics-based data augmentation strategy and, using this approach, XRD spectrum acquisition and analysis occurs under 5 minutes, with accuracy comparable to human expert labeling.

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A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing

Paul Upchurch, Ransen Niu•Wed Jul 20 2022

It is shown that a model trained on the data outperforms a state-of-the-art model across datasets and viewpoints, and is proposed to address this with a large-scale dataset of 3.2 million dense segments on 44,560 indoor and outdoor images.

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Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

N. Bianchi-Berthouze, Youngjun Cho, Nicolai Marquardt, S. Julier•Mon Mar 05 2018

Deep Thermal Imaging is introduced, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment using a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures and a deep neural network classifies these textures into material types.

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Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization

B. Kailkhura, Pin-Yu Chen, Shiyu Chang, Sijia Liu, Pai-Shun Ting, Lisa Amini•Thu May 24 2018

Two accelerated versions of ZO-SVRG utilizing variance reduced gradient estimators are proposed, which achieve the best rate known for ZO stochastic optimization (in terms of iterations) and strike a balance between the convergence rate and the function query complexity.

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

C. Kemp, Zackory Erickson, S. Chernova, Eliot Xing, Bharat Srirangam•Wed Apr 01 2020

A multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging, that enables robots to estimate the materials of household objects and a neural network architecture that learns a compact multi-modal representation of spectral measurements and texture images is presented.

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Roof Material Classification from Aerial Imagery

R. Solovyev•Wed Apr 22 2020

Main advantages of the algorithm are proposed methods to improve prediction accuracy and special set of features of second level models that are used in addition to specific predictions of neural networks.

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SimTreeLS: Simulating aerial and terrestrial laser scans of trees

J. Underwood, F. Westling, M. Bryson•Mon Nov 23 2020

An open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters, suggesting the simulated data is significantly more similar to real data than a sample-based control.

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How well does CLIP understand texture?

Subhransu Maji, Chenyun Wu•Mon Mar 21 2022

This work analyzes CLIP’s ability to perform zero-shot learning on various texture and material classification datasets, and its ability to represent compositional properties of texture such as red dots or yellow stripes on the Describable Texture in Detail dataset.

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Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification

Rajkumar Saini, M. Liwicki, Prakash Chandra Chhipa, Richa Upadhyay, Seiichi Uchida, Lars Lindqvist, R. Nordenskjöld•Mon Oct 17 2022

The proposed method enables self-supervised learning of representations without labels on the 3DPM dataset by exploiting depth maps and inductive transfer and showcases improved performance generalization on linear evaluation.

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