Improved Material Recognition Using Neural Networks

被引：0
|
作者：
Awan, M. Umer Shahzad [1 ]
Shahzad, Amir [1 ]
Shin, Dong Ryeol [1 ]
机构：
[1] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon, South Korea
来源：
2018 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN ARTIFICIAL INTELLIGENCE (ICAAI 2018) | 2015年
关键词：
Material recognition; SIFT; Color SIFT; LBP; K-mean; Perimeter; Solidity; Edge detection; Flickr Material Database; Neural network; TEXTURE;
D O I：
10.1145/3292448.3292460
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Material recognition is a significant subtask in computer vision that has many different applications like autonomous cars in transport engineering, auto-lifting in robotics and even for some sort of autonomous-surgeries in medical field. In this paper, we suggest an improved method to recognize the materials of different categories from one photo image that is captured in an unidentified illumination and view-angle. For that purpose, several different features that cover dynamic material appearance in detail and all gathered information will be used by neural network for recognition. Our proposed method significantly improved in contrast to several leading alternatives including SIFT, Color SIFT, LBP, k-mean, and others. Additionally, we use a material database of Flickr Material Database ( FMD) to analyze our method for identifying materials of different categories including foliage, plastic, wood, glass, leather, metal, paper, fabric, water and stone.
引用
收藏
页码：20 / 23
页数：4
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