Image-understanding systems (IUS) include three levels of abstraction as follows: Low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. Many of these requirements are really topics for further research.
The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation.
While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.
Also Read: What Is Computer Vision?
Their significance for the machine vision systems:
A machine vision system (MVS) is a type of technology that enables a computing device to inspect, evaluate and identify still or moving images.
It is a field in computer vision and is quite similar to surveillance cameras, but provides automatic image capturing, evaluation and processing capabilities.
A machine vision system primarily enables a computer to recognize and evaluate images. It is similar to voice recognition technology, but uses images instead.
A machine vision system typically consists of digital cameras and back-end image processing hardware and software. The camera at the front end captures images from the environment or from a focused object and then sends them to the processing system. Depending on the design or need of the MVS, the captured images are either stored or processed accordingly.