Computer vision is a field of machine learning that focuses on deriving meaningful information from digital images, whether it's a video stream of assembly lines, computed tomography images from non-destructive testing or any other visual input.
Computer vision aims to perform the same kinds of tasks that humans perform when analyzing images but at a much larger scale and with the faultless consistency of a machine. In some applications, machine learning solutions will outperform their expert human counterpart on an image-by-image basis.
How can Element Help?
Our data science and machine learning experts offer services and capabilities that are uniquely suited to drive digital transformation within organizations. The basic types of tasks that can be performed in computer vision are categorized as:
- Classification: The attribution of an image to an object or an abstract class. For example, we may wish to identify the image subject as being a particular part, or we may wish to flag the presence of defects from non-destructive tests (NDT).
- Object Detection: The identification of pre-defined items in an image. The rail track in Figure 1 for example has been identified through semantic segmentation. This kind of application could be used to identify obstructions on a railway track for example. Other examples may include identifying defects or material characteristics from image-based non-destructive testing, or detecting machinery that requires maintenance in production lines.
Figure 1: Semantic segmentation identifies the location a railway track
Many other derived tasks such as object tracking and content-based image retrieval have valuable application in industry and together, form a strong toolset to achieve productivity gains, continuous improvement or to generate new service offerings.
Contact our expert Digital Engineering team today to learn how to use machine learning and data science to increase productivity, promote safety or develop new revenue streams within your organization.
With applications in computer vision such as generative models, Bayesian Inference is a method to update model hypotheses following observations on data.
To learn more about how Bayesian Inference underpins many machine learning techniques read our technical article.
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