Fabric surface characterization: assessment of deep learning-based texture representations using a challenging dataset
| Autor: | Yuting Hu, Motaz Alfarraj, Anirudha Sundaresan, Sundaresan Jayaraman, Zhiling Long, Sungmee Park, Ghassan AlRegib |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Surface (mathematics) 010407 polymers Polymers and Plastics business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Materials Science (miscellaneous) Deep learning Image and Video Processing (eess.IV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Texture (music) 01 natural sciences Industrial and Manufacturing Engineering 0104 chemical sciences Characterization (materials science) FOS: Electrical engineering electronic engineering information engineering Hardware_INTEGRATEDCIRCUITS Computer vision Artificial intelligence General Agricultural and Biological Sciences business |
| Zdroj: | The Journal of The Textile Institute. 112:293-305 |
| ISSN: | 1754-2340 0040-5000 |
| Popis: | Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have long been in search of an objective method for assessing fabric hand, which can then be used to engineer fabrics with a desired hand. Recognizing textures and materials in real-world images has played an important role in object recognition and scene understanding. In this paper, we explore how to computationally characterize apparent or latent properties (e.g., surface smoothness) of materials, i.e., computational material surface characterization, which moves a step further beyond material recognition. 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. We introduce a new, large-scale challenging microscopic material surface dataset (CoMMonS), geared towards an automated fabric quality assessment mechanism in an intelligent manufacturing system. We then conduct a comprehensive evaluation of state-of-the-art deep learning-based methods for texture classification using CoMMonS. Additionally, we propose a multi-level texture encoding and representation network (MuLTER), which simultaneously leverages low- and high-level features to maintain both texture details and spatial information in the texture representation. Our results show that, in comparison with the state-of-the-art deep texture descriptors, MuLTER yields higher accuracy not only on our CoMMonS dataset for material characterization, but also on established datasets such as MINC-2500 and GTOS-mobile for material recognition. arXiv admin note: text overlap with arXiv:1905.09907 |
| Databáze: | OpenAIRE |
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