This white paper will detail how machine vision—the automated computerized process of acquiring and analyzing digital images primarily for ensuring quality, tracking, and guiding production—benefits from deep learning as the latter is making the former more accessible and capable.
Digital cameras with color image sensors are now commonplace. The same is true for the computing power and device interfaces necessary to handle the additional data from color images.
Automated license or number plate recognition (ALPR/ANPR) is one of the most challenging applications for optical character recognition (OCR) because of the variable conditions encountered and the expected effectiveness.
The emergence of the CoaXPress (CXP) standard gives developers of imaging and vision applications a new camera interface with which to work. Determining if the standard is the right choice for your next project requires careful consideration of both CXP’s features and your application requirements.
Developing a machine vision application for the first time need not be a headache. If you follow a thorough, three‐stage process to develop, test and deploy the project, the results should provide an essential tool in product inspection and valuable insight to enhance overall product quality.
In the world of machine vision and automated optical inspection systems, smart cameras receive a lot of attention. In fact, they are often presented as the favored—if not only solution.
Commercial machine vision software is currently classified along two lines: the conventional vision library and the vision-specific integrated development environment (IDE). Determining which software is right for your vision project depends upon a variety of factors: ease-of-use, productivity, flexibility, performance, completeness, and maintenance.