Papers Material Classification
“Material Classification” 태그가 달린 논문 65편 · 필터 해제
A Splitting-Based Iterative Algorithm for GPU-Accelerated Statistical Dual-Energy X-Ray CT Reconstruction
When dealing with material classification in baggage at airports, Dual-Energy Computed Tomography (DECT) allows characterization of any given material with coefficients based on two attenuative effects: Compton scatterin…
CT ReconstructionGeneral ClassificationGPUMaterial ClassificationLearning Material-Aware Local Descriptors for 3D Shapes
Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to lea…
Material ClassificationRetrievalCoded Illumination and Imaging for Fluorescence Based Classification
The quick detection of specific substances in objects such as produce items via non-destructive visual cues is vital to ensuring the quality and safety of consumer products. At the same time, it is well-known that the fl…
ClassificationGeneral ClassificationMaterial ClassificationSecond-order Democratic Aggregation
Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this pape…
General ClassificationMaterial ClassificationScene UnderstandingTexture SynthesisDeep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resultin…
General ClassificationMaterial ClassificationA Deeper Look at Power Normalizations
Power Normalizations (PN) are very useful non-linear operators in the context of Bag-of-Words data representations as they tackle problems such as feature imbalance. In this paper, we reconsider these operators in the de…
Material ClassificationScene RecognitionZeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper addresses these challenges by presenting: a…
Material ClassificationStochastic OptimizationClassification of Household Materials via Spectroscopy
Recognizing an object's material can inform a robot on the object's fragility or appropriate use. To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing. In this pa…
ClassificationGeneral ClassificationMaterial ClassificationMaterial Recognition+1Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a l…
Material ClassificationMaterial RecognitionThermal Infrared Object TrackingRecognizing Material Properties from Images
Humans rely on properties of the materials that make up objects to guide our interactions with them. Grasping smooth materials, for example, requires care, and softness is an ideal property for fabric used in bedding. Ev…
Material ClassificationMaterial RecognitionScene UnderstandingTransfer LearningMaterial Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on …
General ClassificationMaterial ClassificationMaterial Classification using Neural Networks
The recognition and classification of the diversity of materials that exist in the environment around us are a key visual competence that computer vision systems focus on in recent years. Understanding the identification…
ClassificationGeneral ClassificationMaterial ClassificationTransfer LearningMaterial Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion
This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. We use a key observation that the depth measurement by a ToF camera is distorted in objects with certain materials,…
General ClassificationMaterial ClassificationEvaluating Deep Convolutional Neural Networks for Material Classification
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object de…
ClassificationGeneral Classificationimage-classificationImage Classification+3Transfer Learning for Material Classification using Convolutional Networks
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification relies strongly on hand-engineered featu…
ClassificationDescriptiveGeneral ClassificationMaterial Classification+3Geometry-Informed Material Recognition
Our goal is to recognize material categories using images and geometry information. In many applications, such as construction management, coarse geometry information is available. We investigate how 3D geometry (surface…
3D geometryGeneral ClassificationManagementMaterial Classification+1Material Classification Using Raw Time-Of-Flight Measurements
We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such…
ClassificationGeneral ClassificationMaterial ClassificationProbing the Intra-Component Correlations within Fisher Vector for Material Classification
Fisher vector (FV) has become a popular image representation. One notable underlying assumption of the FV framework is that local descriptors are well decorrelated within each cluster so that the covariance matrix for ea…
General ClassificationMaterial ClassificationDeep Learning for Surface Material Classification Using Haptic And Visual Information
When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface. More importantly, such a haptic signal is complementary…
ClassificationDeep LearningGeneral ClassificationMaterial ClassificationMaterial Classification With Thermal Imagery
Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversit…
ClassificationDiversityGeneral ClassificationMaterial Classification