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Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning
The measurement samples are taken from the process and further analyses separately.
Published in Chapter:
Hyperspectral/Multispectral Imaging Methods for Quality Control
Dhanushka Chamara Liyanage (Tallinn University of Technology, Estonia), Mart Tamre (Tallinn University of Technology, Estonia), and Robert Hudjakov (Tallinn University of Technology, Estonia)
DOI: 10.4018/978-1-7998-8686-0.ch017
Abstract
Product quality assurance is a vital component in any manufacturing process. With the advancement of machine vision, the product quality inspection has been vastly improved. This couldn't be achieved with human inspection otherwise when it comes to consistency, accuracy, and the speed. The advance sensor technologies and image processing algorithms are ensuring the product and process quality in various industries including pharmaceutical manufacturing, food production, agriculture, and waste sorting. In contrast to the RGB imaging technology, multispectral and hyperspectral imaging technologies carry more information about the objects under inspection. With the help of both spectral and spatial information, it is possible to discriminate the quality indices of various products with higher accuracy than RGB imaging methods. This chapter discusses the state-of-the-art product quality inspection applications using hyperspectral imaging and multispectral imaging using modern machine learning and other statistical algorithms.
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Exploring New Handwriting Parameters for Writer Identification
Acquisition of biometric parameter from the residues left by the human being actions. In handwriting analysis these residues are: the gray level and the wide of the strokes including the form of the stroke.
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