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E-raamat: Computer Vision Technology in the Food and Beverage Industries

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Computer Vision Technology in the Food and Beverage IndustriesSuurem pilt
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Contributors mostly from the science and engineering side, but also the food and agriculture side, describe some of the ways computer vision could be and is being used in processing operations and quality control in the food and beverage business. The topics include hyperspectral and multispectral imaging, image processing techniques, computer vision for automatic sorting, detecting and removing foreign bodies, the image analysis of food microstructure, real-time ultrasound imaging methods for the quality control of meat, fish, evaluating grain quality, and developing a multispectral imaging system for evaluating the quality of cereal gains and grain products. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

The use of computer vision systems to control manufacturing processes and product quality has become increasingly important in food processing. This book reviews image acquisition and processing technologies and their applications in particular sectors of the food industry.

Part one provides an introduction to computer vision in the food and beverage industries, discussing computer vision and infrared techniques for image acquisition, hyperspectral and multispectral imaging, tomographic techniques and image processing. The middle group of chapters considers computer vision technologies for automatic sorting, foreign body detection and removal, automated cutting and image analysis of food microstructure, while the conclusion focuses on the current and future applications of computer vision in specific areas of the food and beverage industries, such as quality control of meats, gain quality evaluation and the evaluation and control of fruit, vegetable and nut quality.
Contributor contact details xi
About the editor xv
Woodhead Publishing Series in Food Science, Technology and Nutrition xviii
Part I An introduction to computer vision in the food and beverage industries
1(130)
1 Computer vision and infrared techniques for image acquisition in the food and beverage industries
3(24)
M. Z. Abdullah
1.1 Introduction
3(2)
1.2 The electromagnetic spectrum
5(2)
1.3 Image acquisition systems
7(17)
1.4 Conclusions
24(1)
1.5 References
24(2)
1.6 Appendix: nomenclature and abbreviations
26(1)
2 Hyperspectral and multispectral imaging in the food and beverage industries
27(37)
J. Qin
2.1 Introduction
27(1)
2.2 Spectral image acquisition methods
28(2)
2.3 Construction of spectral imaging systems
30(11)
2.4 Calibration of spectral imaging systems
41(6)
2.5 Spectral images and analysis techniques
47(7)
2.6 Applications for food and beverage products
54(4)
2.7 Conclusions
58(1)
2.8 Further information
58(1)
2.9 References
59(5)
3 Tomographic techniques for computer vision in the food and beverage industries
64(33)
M. Z. Abdullah
3.1 Introduction
64(1)
3.2 Nuclear tomography
65(4)
3.3 Electrical impedance
69(17)
3.4 Image reconstruction
86(3)
3.5 Applications
89(4)
3.6 Conclusions
93(1)
3.7 References
93(2)
3.8 Appendix: nomenclature and abbreviations
95(2)
4 Image processing techniques for computer vision in the food and beverage industries
97(34)
N. A. Valous
D.-W. Sun
4.1 Introduction
97(2)
4.2 Digital image analysis techniques
99(14)
4.3 Classification
113(6)
4.4 Relevance, impact and trends for the food and beverage industry
119(3)
4.5 Conclusions
122(1)
4.6 References
123(8)
Part II Computer vision applications in food and beverage processing operations/technologies
131(122)
5 Computer vision in food processing: an overview
133(17)
R. Lind
A. Murhed
5.1 Introduction to computer vision
133(3)
5.2 Technology selection
136(3)
5.3 Selection of image analysis methods
139(4)
5.4 Application examples
143(5)
5.5 Conclusion
148(1)
5.6 References
148(2)
6 Computer vision for automatic sorting in the food industry
150(31)
E. R. Davies
6.1 Introduction
150(2)
6.2 Basic techniques and their application
152(8)
6.3 Advanced techniques and their application
160(12)
6.4 Alternative image modalities
172(1)
6.5 Special real-time hardware for food sorting
173(2)
6.6 Recent advances in computer vision for food sorting
175(1)
6.7 Future trends
176(1)
6.8 Conclusion
177(1)
6.9 Sources of further information and advice
177(1)
6.10 Acknowledgements
178(1)
6.11 References
178(3)
7 Computer vision for foreign body detection and removal in the food industry
181(25)
N. Toyofuku
R. P. Haff
7.1 Introduction
181(2)
7.2 Optical inspection
183(5)
7.3 Fundamentals of X-ray inspection
188(7)
7.4 X-ray inspection of food products
195(5)
7.5 Conclusions
200(1)
7.6 References
200(6)
8 Automated cutting in the food industry using computer vision
206(27)
W. D. R. Daley
O. Arif
8.1 Introduction
206(2)
8.2 Machine vision and computer vision
208(3)
8.3 Feature selection, extraction and analysis
211(1)
8.4 Machine learning algorithms
212(1)
8.5 Application examples: sensing for automated cutting and handling
213(15)
8.6 Future trends
228(2)
8.7 Conclusions
230(1)
8.8 Acknowledgments
231(1)
8.9 References
231(2)
9 Image analysis of food microstructure
233(20)
J. C. Russ
9.1 Introduction
233(1)
9.2 Quality control applications of digital imaging
234(3)
9.3 Characterizing the internal structure
237(1)
9.4 Volume, surface and length
238(5)
9.5 Number and spatial distribution
243(6)
9.6 Surfaces and fractal dimensions
249(1)
9.7 Conclusions
250(1)
9.8 References
251(2)
Part III Current and future applications of computer vision for quality control and processing of particular products
253(230)
10 Computer vision in the fresh and processed meat industries
255(22)
P. Jackman
D.-W. Sun
10.1 Introduction
255(1)
10.2 Meat image features
256(4)
10.3 Application and implementation
260(9)
10.4 Application and implementation for lamb, pork and other processed meats
269(2)
10.5 Future trends
271(1)
10.6 Conclusions
271(1)
10.7 References
272(5)
11 Real-time ultrasound (RTU) imaging methods for quality control of meats
277(53)
S. R. Silva
V. P. Cadavez
11.1 Introduction
277(1)
11.2 Historical background on ultrasound use for carcass composition and meat traits evaluation
278(4)
11.3 Basic ultrasound imaging principles
282(3)
11.4 Applications of real-time ultrasound (RTU) to predict carcass composition and meat traits in large animals
285(8)
11.5 Applications of RTU to predict carcass composition and meat traits in small animals and fish
293(10)
11.6 Using real-time ultrasonography to predict intramuscular fat (IMF) in vivo
303(7)
11.7 Optimization of production system and market carcass characteristics
310(3)
11.8 The future for RTU imaging in the meat industry
313(1)
11.9 Conclusion
314(1)
11.10 References
315(15)
12 Computer vision in the poultry industry
330(22)
K. Chao
B. Park
M. S. Kim
12.1 Introduction
330(1)
12.2 Poultry processing applications
331(2)
12.3 Development of spectral imaging for poultry inspection
333(3)
12.4 Case studies for online line-scan poultry safety inspection
336(14)
12.5 Future trends
350(1)
12.6 Conclusions
350(1)
12.7 References
351(1)
13 Computer vision in the fish industry
352(27)
J. R. Mathiassen
E. Misimi
S. O. Ostvik
I. G. Aursand
13.1 Introduction
352(1)
13.2 The need for computer vision in the fish industry
353(1)
13.3 Automated sorting and grading
354(6)
13.4 Automated processing
360(7)
13.5 Process understanding and optimization
367(6)
13.6 Challenges in applying computer vision in the fish industry
373(1)
13.7 Future trends
374(1)
13.8 Further information
375(1)
13.9 Conclusions
376(1)
13.10 References
376(3)
14 Fruit, vegetable and nut quality evaluation and control using computer vision
379(21)
J. Blasco
N. Aleixos
S. Cubero
D. Lorente
14.1 Introduction
379(2)
14.2 Basics of machine vision systems for fruit, vegetable and nut quality evaluation and control
381(5)
14.3 Applications of computer vision in the inspection of external features
386(2)
14.4 Real-time automatic inspection systems
388(4)
14.5 Future trends
392(2)
14.6 Conclusions
394(1)
14.7 Sources of further information
395(1)
14.8 Acknowledgements
396(1)
14.9 References
396(4)
15 Grain quality evaluation by computer vision
400(22)
D. S. Jayas
C. B. Singh
15.1 Introduction
400(2)
15.2 Colour imaging
402(4)
15.3 Hyperspectral imaging
406(5)
15.4 X-ray imaging
411(4)
15.5 Thermal imaging
415(3)
15.6 Conclusions
418(1)
15.7 Acknowledgements
418(1)
15.8 References
419(3)
16 Computer vision in the bakery industry
422(29)
C.-J. Du
Q. Cheng
D.-W. Sun
16.1 Introduction
422(1)
16.2 Computer vision applications for analysing bread
423(9)
16.3 Computer vision applications for analysing muffins
432(4)
16.4 Computer vision applications for analysing biscuits
436(3)
16.5 Computer vision applications for analysing pizza bases
439(5)
16.6 Computer vision applications for analysing other bakery products
444(1)
16.7 Future trends and further information
445(1)
16.8 Conclusions
446(1)
16.9 References
447(4)
17 Development of multispectral imaging systems for quality evaluation of cereal grains and grain products
451(32)
M. A. Shahin
D. W. Hatcher
S. J. Symons
17.1 Introduction
452(4)
17.2 Hyperspectral imaging
456(3)
17.3 Detection of mildew damage in wheat
459(2)
17.4 Detection of fusarium damage in wheat
461(4)
17.5 Sprout damage in wheat
465(4)
17.6 Determination of green immature kernels in cereal grains
469(2)
17.7 Effect of mildew on the quality of end-products
471(2)
17.8 Development of multispectral imaging systems
473(4)
17.9 Conclusions
477(1)
17.10 Acknowledgements
478(1)
17.11 References
478(5)
Index 483
Professor Da-Wen Sun is a world authority in food engineering research and education. He is a member of the Royal Irish Academy, the highest academic honour in Ireland, and is also a member of Academia Europaea (The Academy of Europe). His main research activities include cooling, drying, and refrigeration processes and systems; quality and safety of food products; bioprocess simulation and optimization; and computer vision technology. His many scholarly works have become standard reference materials for researchers in such areas as computer vision, computational fluid dynamics modelling and vacuum cooling.
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