Material Recognition
15 papers with code • 0 benchmarks • 9 datasets
Material recognition focuses on identifying classes, types, states, and properties of materials.
Benchmarks
These leaderboards are used to track progress in Material Recognition
MINC
MCubeS
LabPics
TransProteus
SpectroVision
MatSim
MatSeg
OpenSurfaces
Latest papers with no code
Multi-Class Zero-Shot Learning for Artistic Material Recognition
After experimenting with a range of hyper-parameters, we produce a model which is capable of correctly identifying the materials used on pieces from an entirely distinct museum dataset.
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition.
Angular Luminance for Material Segmentation
We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
Face Anti-Spoofing with Human Material Perception
In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception [1], intending to extract discriminative and robust features for FAS.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
We formulate the problem as a very fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task.
Fusion of Convolutional Neural Network and Statistical Features for Texture classification
Texture image classification is one of the challenging problems that have various applications such as remote sensing, material recognition, and computer-aided medical diagnosis, etc.
Through-Wall Object Recognition and Pose Estimation
Yet unlike optical images, RF images are difficult to understand by a human.
Material Segmentation of Multi-View Satellite Imagery
The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions.
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.
