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Material Recognition
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Material recognition focuses on identifying classes, types, states, and properties of materials.
논문
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Shape and Texture Recognition in Large Vision-Language Models
Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner
Temporal Binding Foundation Model for Material Property Recognition via Tactile Sequence Perception
Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots
Integrated Sensing and Communications Framework for 6G Networks
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
Intelligent Parsing: An Automated Parsing Framework for Extracting Design Semantics from E-commerce Creatives
UMat: Uncertainty-Aware Single Image High Resolution Material Capture
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
The joint role of geometry and illumination on material recognition
Deep Learning for Material recognition: most recent advances and open challenges
Multi-Class Zero-Shot Learning for Artistic Material Recognition
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Angular Luminance for Material Segmentation
Differential Viewpoints for Ground Terrain Material Recognition
Face Anti-Spoofing with Human Material Perception
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
Fusion of Convolutional Neural Network and Statistical Features for Texture classification
Material Segmentation of Multi-View Satellite Imagery
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
Classification of Household Materials via Spectroscopy
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
Recognizing Material Properties from Images
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition
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