Adaptive illumination source for multispectral vision system applied to material discrimination

被引:4
|
作者
Conde, Olga M. [1 ]
Cobo, Adolfo [1 ]
Cantero, Paulino [1 ]
Conde, David [1 ]
Mirapeix, Jesus [1 ]
Cubillas, Ana M. [1 ]
Lopez-Higuera, Jose M. [1 ]
机构
[1] Univ Cantabria, Dep TEISA, Photon Engn Grp, E-39005 Santander, Spain
来源
关键词
multispectral system; high-bright LEDs; imaging spectroscopy; automatic discrimination; material characterization;
D O I
10.1117/12.781496
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
A multispectral system based oil a monochrome camera and all adaptive illumination source is presented in this paper. its preliminary application is focused oil material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach. in which the relevant wavelengths for the required discrimination task are selected in advance using, a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based oil Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illiminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illmination produces multispectral imaging with a simple monochrome camera, and cold illmination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] A multispectral machine vision system for invertebrate detection on green leaves
    Liu, Huajian
    Chahl, Javaan Singh
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 150 : 279 - 288
  • [22] A multispectral computer vision system for automatic grading of prostatic neoplasia
    Roula, MA
    Diamond, J
    Bouridane, A
    Miller, P
    Amira, A
    2002 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, PROCEEDINGS, 2002, : 193 - 196
  • [23] Automatic Identification of Defects on Eggshell Through a Multispectral Vision System
    Loredana Lunadei
    Luis Ruiz-Garcia
    Luigi Bodria
    Riccardo Guidetti
    Food and Bioprocess Technology, 2012, 5 : 3042 - 3050
  • [24] Comparison of a multispectral vision system and a colorimeter for the assessment of meat color
    Trinderup, Camilla H.
    Dahl, Anders
    Jensen, Kirsten
    Carstensen, Jens Michael
    Conradsen, Knut
    MEAT SCIENCE, 2015, 102 : 1 - 7
  • [25] A multispectral optical illumination system with precise spatiotemporal control for the manipulation of optogenetic reagents
    Stirman, Jeffrey N.
    Crane, Matthew M.
    Husson, Steven J.
    Gottschalk, Alexander
    Lu, Hang
    NATURE PROTOCOLS, 2012, 7 (02) : 207 - 220
  • [26] A multispectral optical illumination system with precise spatiotemporal control for the manipulation of optogenetic reagents
    Jeffrey N Stirman
    Matthew M Crane
    Steven J Husson
    Alexander Gottschalk
    Hang Lu
    Nature Protocols, 2012, 7 : 207 - 220
  • [27] Illumination system design applied to LC rear projective TV system
    Zhang, ZB
    Weng, ZC
    Chang, J
    Cong, XJ
    OPTICAL DESIGN AND TESTING, 2002, 4927 : 764 - 769
  • [28] What is second-order vision for? Discriminating illumination versus material changes
    Schofield, Andrew J.
    Rock, Paul B.
    Sun, Peng
    Jiang, Xiaoyue
    Georgeson, Mark A.
    JOURNAL OF VISION, 2010, 10 (09):
  • [29] Coarse-to-Fine Adaptive Adjustment for Vision Illumination Inspection System Hard-Under Uncertain Imaging Conditions
    Chang, Fei
    Duan, Yunqiang
    Liu, Min
    Dong, Mingyu
    2019 IEEE SENSORS, 2019,
  • [30] An adaptive parallel computer vision system
    Kim, JM
    Kim, Y
    Kim, SD
    Han, TD
    Yang, SB
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 1998, 12 (03) : 311 - 334