Classification of rotifers with machine vision by shape moment invariants

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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Development of machine vision based system for classification of Guava fruits on the basis of CIE1931 chromaticity coordinates

2015 2nd International Symposium on Physics and Technology of Sensors (ISPTS) ◽

10.1109/ispts.2015.7220107 ◽

2015 ◽

Cited By ~ 2

Author(s):

Ashok Kanade ◽

Arvind Shaligram

Keyword(s):

Machine Vision ◽

Chromaticity Coordinates

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Detection and Classification of Metal Workpiece Surface Defects Based on Machine Vision

10.1109/aeeca52519.2021.9574344 ◽

2021 ◽

Author(s):

Yibei Huang ◽

Tao Yu ◽

Kai Wan ◽

Jie Yuan

Keyword(s):

Machine Vision ◽

Surface Defects ◽

Workpiece Surface

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In-Line Sorting of Processed Fruit Using Computer Vision

Image Processing ◽

10.4018/978-1-4666-3994-2.ch044 ◽

2013 ◽

pp. 874-895

Author(s):

J. Blasco ◽

N. Aleixos ◽

S. Cubero ◽

F. Albert ◽

D. Lorente ◽

...

Keyword(s):

Machine Vision ◽

Fruits And Vegetables ◽

Complete Solution ◽

Punica Granatum ◽

Automatic Inspection ◽

Minimally Processed ◽

Total Absence ◽

Similar Appearance ◽

Punica Granatum L

Nowadays, there is a growing demand for quality fruits and vegetables that are simple to prepare and consume, like minimally processed fruits. These products have to accomplish some particular characteristics to make them more attractive to the consumers, like a similar appearance and the total absence of external defects. Although recent advances in machine vision have allowed for the automatic inspection of fresh fruit and vegetables, there are no commercially available equipments for sorting of minority processed fruits, like arils of pomegranate (Punica granatum L) or segments of Satsuma mandarin (Citrus unshiu) ready to eat. This work describes a complete solution based on machine vision for the automatic inspection and classification of these fruits based on their estimated quality. The classification is based on morphological and colour features estimated from images taken in-line, and their analysis using statistical methods in order to grade the fruit into commercial categories.

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