? Discusses novel technology for recognizing objects and extracting quantitative information from digital images in order to provide objective, rapid, non-contact and non-destructive quality evaluation.
? International authors with both academic and professional credentials address in detail one aspect of the relevant technology per chapter making this ideal for textbook use
? Divided into three parts, it begins with an outline of the fundamentals of the technology, followed by full coverage of the application in the most researched areas of meats and other foods, fruits, vegetables and grains.
The first book in this rapidly expanding area, Computer Vision Technology for Food Quality Evaluation thoroughly discusses the latest advances in image processing and analysis. Computer vision has attracted much research and development attention in recent years and, as a result, significant scientific and technological advances have been made in quality inspection, classification and evaluation of a wide range of food and agricultural products. This unique work provides engineers and technologists working in research, development, and operations in the food industry with critical, comprehensive and readily accessible information on the art and science of computer vision technology. Undergraduate and postgraduate students and researchers in universities and research institutions will also find this an essential reference source.* Discusses novel technology for recognizing objects and extracting quantitative information from digital images in order to provide objective, rapid, non-contact and non-destructive quality evaluation. * International authors with both academic and professional credentials address in detail one aspect of the relevant technology per chapter making this ideal for textbook use* Divided into three parts, it begins with an outline of the fundamentals of the technology, followed by full coverage of the application in the most researched areas of meats and other foods, fruits, vegetables and grains.
Front Cover 1
Computer Vision Technology for Food Quality Evaluation 4
Copyright Page 5
Contents 6
About the Editor 12
Contributors 14
Preface 16
Part I: Fundamentals of Computer Vision Technology 18
Chapter 1. Image Acquisition Systems 20
1 Introduction 20
2 The electromagnetic spectrum 21
3 Image acquisition systems 23
4 Conclusions 48
Nomenclature 48
References 49
Chapter 2. Image Segmentation Techniques 54
1 Introduction 54
2 Pre-processing techniques 55
3 Segmentation techniques 58
4 Conclusions 69
Nomenclature 70
References 71
Chapter 3. Object Measurement Methods 74
1 Introduction 74
2 Size 75
3 Shape 76
4 Color 80
5 Texture 84
6 Combined measurements 89
7 Conclusions 89
Nomenclature 90
Appendix 91
References 94
Chapter 4. Object Classification Methods 98
1 Introduction 98
2 Artificial neural network 99
3 Statistical classification 103
4 Fuzzy logic 108
5 Decision tree 111
6 Support vector machine 113
7 Conclusions 119
Nomenclature 119
References 121
Part II: Quality Evaluation of Meat, Poultry, and Seafood 126
Chapter 5. Quality Evaluation of Meat Cuts 128
1 Introduction 128
2 Quality evaluation of beef 129
3 Quality evaluation of pork 142
4 Quality evaluation of lamb 146
5 Future perspectives 148
6 Conclusions 149
References 149
Chapter 6. Quality Measurement of Cooked Meats 156
1 Introduction 156
2 Shrinkage 157
3 Pores and porosity 162
4 Color 166
5 Image texture 167
6 Conclusions 170
Nomenclature 170
References 171
Chapter 7. Quality Inspection of Poultry Carcasses 174
1 Introduction 174
2 Poultry quality inspection 175
3 Color imaging for quality inspection 176
4 Spectral imaging 180
5 Poultry image classifications 188
6 Conclusions 199
References 200
Chapter 8. Quality Evaluation of Seafood 206
1 Introduction 206
2 Visual quality of seafood 206
3 Conclusions 223
References 224
Part III: Quality Evaluation of Fruit and Vegetables 228
Chapter 9. Quality Evaluation of Apples 230
1 Introduction 230
2 Material 232
3 Shape grading 239
4 Color grading 239
5 Evaluation of surface defects 241
6 Calyx and stalk-end recognition 247
7 Defect recognition and fruit classification 248
8 Conclusions 255
Nomenclature 256
References 256
Chapter 10. Quality Evaluation of Citrus Fruits 260
1 Introduction 260
2 Image analysis in the visible spectrum 264
3 Quality inspection in the non-visible spectrum 272
4 Internal quality inspection 273
5 Inspection of clementine and satsuma segments 275
6 Conclusions 277
References 277
Chapter 11. Quality Evaluation of Strawberries 282
1 Introduction 282
2 Grading of size, shape, and ripeness 284
3 Detection of bruises and fecal contamination 290
4 Estimation of firmness and soluble-solids content 296
5 Estimation of anthocyanin distribution 300
6 Further challenges 301
7 Conclusions 302
References 302
Chapter 12. Classification and Quality Evaluation of Table Olives 306
1 Introduction 306
2 Classification of table olives 307
3 Application of machine vision 311
4 Industrial applications 318
5 Conclusions 318
Acknowledgments 319
References 319
Chapter 13. Grading of Potatoes 322
1 Introduction 322
2 Surface defects 323
3 Potato classification 324
4 Applications 325
5 Conclusions 332
Acknowledgments 333
References 333
Chapter 14. Quality Evaluation of Fruit by Hyperspectral Imaging 336
1 Introduction 336
2 Techniques for measuring optical properties 337
3 The hyperspectral imaging system 341
4 Applications 350
5 Conclusions 361
Acknowledgments 361
Nomenclature 361
References 362
Part IV: Quality Evaluation of Grains 366
Chapter 15. Quality Evaluation of Wheat 368
1 Introduction 368
2 Machine vision 370
3 Soft X-ray imaging 379
4 Near-infrared spectroscopy and hyperspectral imaging 381
5 Thermal imaging 386
6 Potential practical applications of machine vision technology 387
Acknowledgments 388
References 388
Chapter 16. Quality Evaluation of Rice 394
1 Introduction 394
2 Quality of rice 394
3 Quality evaluation of raw rice 396
4 Quality evaluation of cooked rice 407
5 Conclusions 414
References 414
Chapter 17. Quality Evaluation of Corn/Maize 418
1 Introduction 418
2 Corn 425
3 Machine vision determination of corn quality 427
4 Conclusions 436
References 436
Part V: Quality Evaluation of Other Foods 442
Chapter 18. Quality Evaluation of Pizzas 444
1 Introduction 444
2 Pizza base production 445
3 Pizza sauce spread 450
4 Application of pizza toppings 455
5 Conclusions 461
Nomenclature 461
References 462
Chapter 19. Quality Evaluation of Cheese 464
1 Introduction 464
2 Cheese quality characteristics 464
3 Cheese defects 479
4 Microstructure evaluation 482
5 Conclusions 491
References 491
Chapter 20. Quality Evaluation of Bakery Products 498
1 Introduction 498
2 Quality characteristics of bakery products 501
3 Computer vision inspection of bakery products 512
4 Conclusions 534
Nomenclature 534
References 536
Chapter 21. Image Analysis of Oriental Noodles 540
1 Introduction 540
2 Imaging in noodle quality assessment 546
3 Measuring the impact of external grading factors 553
4 Developments and further applications 556
5 Conclusions 559
References 559
Chapter 22. Quality Evaluation and Control of Potato Chips and French Fries 562
1 Introduction 562
2 Computer vision 563
3 Applications 567
4 Conclusions 580
Acknowledgments 580
References 580
Index 584
A 584
B 585
C 585
D 587
E 588
F 588
G 588
H 589
I 589
J 590
K 590
L 590
M 590
N 591
O 592
P 592
Q 593
R 593
S 594
T 595
U 596
V 597
W 597
X 597
Z 597
Series 598
Image Segmentation Techniques
Chaoxin Zheng and Da-Wen Sun, Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Dublin 2, Ireland
1 Introduction
Owing to the imperfections of image acquisition systems, the images acquired are subject to various defects that will affect the subsequent processing. Although these defects can sometimes be corrected by adjusting the acquisition hardware, for example by increasing the number of images captured for the same scene and adopting higher quality instruments, such hardware-based solutions are time-consuming and costly. Therefore it is preferable to correct the images, after they have been acquired and digitized, by using computer programs, which are fast and relatively low-cost. For example, to remove noise, smooth filters (including linear and median filters) can be applied; to enhance contrast in low-contrast images, the image histograms can be scaled or equalized. Such corrections of defects in images are generally called “image pre-processing.”
After pre-processing, the images are segmented. Segmentation of food images, which refers to the automatic recognition of food products in images, is of course required after image acquisition, because food quality evaluation is completely and automatically conducted by computer programs, without any human participation in computer vision techniques. Although image segmentation is ill-defined, it can generally be described as separating images into various regions in which the pixels have similar image characteristics. Since segmentation is an important task, in that the entire subsequent interpretation tasks (i.e. object measurement and object classification) rely strongly on the segmentation results, tremendous efforts are being made to develop an optimal segmentation technique, although such a technique is not yet available. Nevertheless, a large number of segmentation techniques have been developed. Of these, thresholding-based, region-based, gradient-based, and classification-based segmentation are the four most popular techniques in the food industry, yet none of these can perform with both high accuracy and efficiency across the wide range of different food products. Consequently, other techniques combining several of the above are also being developed, with a compromise on accuracy and efficiency. Even so, they are not adaptable enough for use on the full diversity of food products.
This chapter reviews the image pre-processing techniques and the image segmentation techniques that are adoptable or have already been adopted in the food industry. The feasibility of the various techniques is also discussed. This review can serve as a foundation for applying the segmentation techniques available, and for the development of new segmentation techniques in computer vision systems.
2 Pre-processing techniques
2.1 Noise removal
Images captured using various means are all subject to different types of noise, such as the read-out noise while reading information from cameras, the wiring noise while transferring video signals from cameras to computers, and the electronic noise while digitizing video signals. All these lead to degradation of the quality of the images when they are subsequently processed. In Figure 2.1, two images of the same scene have been taken at an interval of less than 2 seconds, using the same image acquisition system, and the differences are illustrated to demonstrate the noise produced during image acquisition. It is clearly important that noise is removed after images have been digitized and stored in computers, and the most efficient and feasible approach for image noise removal is to “average” the image by itself.
Figure 2.1 Illustration of noise present in images: (a) two-color peanut images (in RGB space) taken at an interval of less than 2 seconds; (b) their difference in the red component; (c) their difference in the green component; (d) their difference in the blue component. Contrast was enhanced in images (b), (c), and (d).
2.1.1 Linear filter
The simplest method of averaging an image by itself is the linear filter, by which the intensity values of pixels in the image are averaged using the intensity values of their neighboring pixels within a small region. The filter processing can be described by the following equation:
(2.1)
where f(x, y) is the intensity value of pixel (x, y), while M is the size of the filter and w represents the weighting of the filter. The weighting and size of the filter can be adjusted to remove different types of noise. For instance, increasing the weighting of the central pixel means that the central pixel dominates the averaging. Increasing the size of the filter results in a smoother image with less noise, but the detail of the image is reduced.
2.1.2 Median filter
Another popular filter that is widely used is the median filter. The intensity values of pixels in a small region within the size of the filter are examined, and the median intensity value is selected for the central pixel. Removing noise using the median filter does not reduce the difference in brightness of images, since the intensity values of the filtered image are taken from the original image. Furthermore, the median filter does not shift the edges of images, as may occur with a linear filter (Russ, 1999). These two primary advantages have led to great use of the median filter in the food industry (Du and Sun, 2004, 2006a; Faucitano et al., 2005).
2.2 Contrast enhancing
Sometimes images captured are of low contrast – in other words, the intensity values of the images are within a small range of intensity levels, and thus pixels with different intensity values are not well distinguished from each other. An image in which the intensity values range from 100 to 109 is shown in Figure 2.2a. However, it is impossible to sense the difference of intensity values between pixels. The process of contrast-enhancing is designed to increase the difference in intensity values among pixels so that they can be effortlessly distinguished by human or computer vision. Most of the contrast-enhancing utilizes the image histogram, which is a plot showing the occurrence of intensity values in images (Jain, 1989).
Figure 2.2 Illustrations of (a) low-contrast image, and (b) high contrast after histogram scaling.
2.2.1 Histogram scaling
In histogram scaling, the original histogram is transferred from one scale to another – mostly from a smaller scale to larger one. Accordingly, the difference between two neighboring intensity values is increased. For instance, Figure 2.2b is the transformed image of Figure 2.2a, whose histogram has been reallocated from [100, 109] to the scale of [0, 200] linearly so that the difference between neighboring intensity values of the original image is increased from 1 to 20 – which can easily be observed. The transform function used for histogram scaling can be linear or non-linear, and one-to-one or multiple-to-one.
2.2.2 Histogram equalization
Most of the transform functions for histogram scaling are limited to proposed cases. Therefore, it is important to develop a flexible and hopefully optimal function that can be employed for different types of images. Taking this into consideration, histogram equalization has been developed, in which a much more uniform histogram is generated from the original histogram by spreading out the number of pixels at the histogram peaks and selectively compressing those at the histogram valleys (Gauch, 1992). Histogram equalization can be simply described by equation (2.2):
(2.2)
where H denotes the original histogram, and l and L are the minimum and maximum intensity values, respectively. The parameter i is the ith intensity value in the histogram; j and j′ stand for the intensity value in the original histogram, and its corresponding intensity value in the equalized histogram, respectively. Sometimes the contrast needs to be constrained to a limited range for the purpose of retaining visual information of objects in images, especially those with homogeneous intensity values. Therefore, the contrast-limited adaptive histogram equalization method was developed and has been applied to adjust pork images by facilitating the segmentation of pores (Du and Sun, 2006a). In this method, the contrast of the images is enhanced by first dividing each image into non-overlapping small regions, and then enhancing the contrast in each small region.
3 Segmentation techniques
3.1 Thresholding-based segmentation
In thresholding-based segmentation the image histogram is partitioned into two classes using a single value, called bi-level thresholding (Figure 2.3), or into multiple classes using multiple values, called multilevel thresholding, based on the characteristics of the histogram. In bi-level thresholding, pixels with intensity values less than the threshold are set as background (object) while others are set as object (background). In multiple-level thresholding, pixels with intensity values between two successive thresholds are assigned as a class. However, in tri-level thresholding, only two classes are normally defined – i.e....
| Erscheint lt. Verlag | 28.4.2011 |
|---|---|
| Sprache | englisch |
| Themenwelt | Sachbuch/Ratgeber ► Gesundheit / Leben / Psychologie ► Ernährung / Diät / Fasten |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Informatik ► Weitere Themen ► CAD-Programme | |
| Sozialwissenschaften ► Politik / Verwaltung | |
| Technik ► Lebensmitteltechnologie | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 0-08-055624-8 / 0080556248 |
| ISBN-13 | 978-0-08-055624-6 / 9780080556246 |
| Haben Sie eine Frage zum Produkt? |
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