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The theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms are explored in order to exploit hyperspectral imaging for application to food quality evaluations.
Strategies and essential image processing routines necessary for making the appropriate decision during detection, classification, identification, quantification and/or prediction processes are presented.
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Segmentation is essential to most image processing and pattern recognition algorithms as well as applications in related areas.
For Architecture designers, the essential purpose of using image processing software like Photoshop is to help better deliver their design ideas through drawings.
Hence, image processing is essential to correct such defects and improve the quality of the image.
Image segmentation, which is the extraction of an object from the background in an image, is one of the essential techniques in areas of image processing and computer vision [1, 2].
Low-level image processing is an essential first step in any machine vision application.
Essential reading for academics & industrial researchers working in the area of computer vision, image processing, and medical imaging, it will also be useful background reading for advanced undergraduate & postgraduate students.
To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis.
Such a database is essential for validation of both existing and novel image processing procedures and can only be created by a consortium.
In the field of digital image processing, contrast enhancement plays an essential role in rendering an image clearly recognizable for different imaging applications [1], including computed tomography (CT).
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