Files and links (1)
SubmittedOpen Access (License Unspecified), Open
Conference proceeding
Reflectance Hashing for Material Recognition
2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), v 7-12-, pp 3071-3080
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance of a material surface which we refer to as a reflectance disk is capturing using a unique optical camera. The pixel coordinates of these reflectance disks correspond to the surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of realworld materials.
Metrics
Details
- Title
- Reflectance Hashing for Material Recognition
- Creators
- Hang Zhang - Rutgers, The State University of New JerseyKristin Dana - Rutgers, The State University of New JerseyKo Nishino - Drexel UniversityIEEEHua Zhang - Electrical and Computer Engineering
- Publication Details
- 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), v 7-12-, pp 3071-3080
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000387959203011
- Scopus ID
- 2-s2.0-84959254716
- Other Identifier
- 991019173575104721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence