Machine vision

Summary

Machine vision (MV) is the technology and methods used to provide -based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated of the image and extraction of the required information.

Official source

https://en.wikipedia.org/wiki/Machine_vision

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Recent developments have enabled the practical realization of optical elements in which the polarization of light may vary spatially. We present an extension of Fourier optics-matrix Fourier optics-for understanding these devices and apply it to the design and realization of metasurface gratings implementing arbitrary, parallel polarization analysis. We show how these gratings enable a compact, full-Stokes polarization camera without standard polarization optics. Our single-shot polarization camera requires no moving parts, specially patterned pixels, or conventional polarization optics and may enable the widespread adoption of polarization imaging in machine vision, remote sensing, and other areas.

2019

Applying image processing technology and machine vision in industry have had significant development in recent decade. Tile and ceramic industry was not excluded form this matter. By using image processing techniques in production line of this industry, it is possible to detect surface defections such as edge defections, cracks and coloring defections. Surface defection is the most common type of defection in tile and ceramic industry. In this article a new and simple approach for detecting surface defections using Fuzzy thersholding and morphological operand and Radon transforms as well as boundary analysis is introduced. The available algorithm in the articles has good ability in determining number and place of defections and displaying them. Compared with results of other methods, the used algorithm in this article has better results on actual data base, in presence of camera noise and environmental lighting effects.

2008

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This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pretrained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task.

2016