Papers Material Classification
“Material Classification” 태그가 달린 논문 65편 · 필터 해제
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
Visual recognition of materials and their states is essential for understanding most aspects of the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. However, current i…
Contrastive LearningMaterial ClassificationMaterial RecognitionOne-Shot LearningDepth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size di…
Linear evaluationMaterial ClassificationRepresentation LearningSelf-Supervised Learning+1Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering
Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major …
Image ReconstructionMaterial ClassificationSpectral ReconstructionA Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model trained on existing data underperforms in some settings and propose to …
Material ClassificationMaterial RecognitionMaterial SegmentationScene Parsing+1How well does CLIP understand texture?
We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification d…
Material ClassificationZero-Shot LearningRadar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units
Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance …
ClassificationMaterial ClassificationObject RecognitionMaterial Classification Using Active Temperature Controllable Robotic Gripper
Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One…
ClassificationMaterial ClassificationObject RecognitionEcho-Reconstruction: Audio-Augmented 3D Scene Reconstruction
Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction. These surfaces are often poorly reconstructed and filled with depth discontinuities and hole…
3D Reconstruction3D Scene ReconstructionClassificationDepth Estimation+1Programmable Spectral Filter Arrays using Phase Spatial Light Modulator
Spatially varying spectral modulation can be implemented using a liquid crystal spatial light modulator (SLM) since it provides an array of liquid crystal cells, each of which can be purposed to act as a programmable spe…
Material ClassificationGround material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided add…
Material ClassificationMaterial Segmentationobject-detectionObject DetectionConstruction material classification on imbalanced datasets using Vision Transformer (ViT) architecture
This research proposes a reliable model for identifying different construction materials with the highest accuracy, which is exploited as an advantageous tool for a wide range of construction applications such as automat…
ManagementMaterial ClassificationPower Normalizations in Fine-grained Image, Few-shot Image and Graph Classification
Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines …
Few-Shot LearningGeneral ClassificationGraph ClassificationMaterial Classification+1SimTreeLS: Simulating aerial and terrestrial laser scans of trees
There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of LiDAR point clouds is increasingly rely…
BIG-bench Machine LearningMaterial ClassificationMaterial 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. Construction monitoring has not been an exception; as…
Deep LearningMaterial ClassificationMaterial RecognitionRoof material classification from aerial imagery
This paper describes an algorithm for classification of roof materials using aerial photographs. Main advantages of the algorithm are proposed methods to improve prediction accuracy. Proposed methods includes: method of …
ClassificationGeneral ClassificationMaterial ClassificationVisualizing key features in X-ray images of epoxy resins for improved material classification using singular value decomposition of deep learning features
Although the process variables of epoxy resins alter their mechanical properties, the visual identification of the characteristic features of X-ray images of samples of these materials is challenging. To facilitate the i…
Material ClassificationMultimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leveraging near-infrared spectroscopy and close…
General ClassificationMaterial ClassificationMaterial RecognitionJoint 3D Localization and Classification of Space Debris using a Multispectral Rotating Point Spread Function
We consider the problem of joint three-dimensional (3D) localization and material classification of unresolved space debris using a multispectral rotating point spread function (RPSF). The use of RPSF allows one to estim…
ClassificationGeneral ClassificationMaterial ClassificationFast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallograph…
BIG-bench Machine LearningData AugmentationGeneral ClassificationInterpretable Machine Learning+5Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
Many materials have distinct spectral profiles. This facilitates estimation of the material composition of a scene at each pixel by first acquiring its hyperspectral image, and subsequently filtering it using a bank of s…
ClassificationGeneral ClassificationMaterial Classification