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The breadth of different image processing approaches adopted over the years in attempting to implement hyperspectral imaging for food quality monitoring was surveyed.
Image processing approaches including complex mathematical operations dissecting the conglomerates by finding local minimal and maximal staining intensities in an image ("watershed algorithms") could be considered, but generally are prone to severe errors due to unavoidable staining variability.
In many aspects it deviates from classical image processing approaches.
Reliability of vision systems may be enhanced by a good integration of prior knowledge and the cooperation between various image processing approaches.
A comprehensive review of proposed T-F image processing approaches shows that they can be grouped in six classes (corresponding to the numbering of the next subsections).
Thus, we will study image processing approaches to improve the segmentation and increase the amount of relevant features of the samples.
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In this paper, a vision-based structural displacement measurement system integrated with a digital image processing approach is developed.
This paper presents an image processing approach to automatically extract features from vibration signal, based on visual words representation.
Characterized by 3D CAD-based image mask filters, color-based noise removal, and area-based progress calculation, the image processing approach provides as-built schedule information.
An image processing approach is then proposed to measure the time evolution of the feature variable (particle contacts and temperature variance) of the two processes.
This should result in a more advanced design for automatic abnormality diagnosis and localization systems based on a complete multichannel T-F image processing approach.
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