In May of 2022, scientists published an image that, despite its visual simplicity, amazed everyone who saw it: the first “real” image of a black hole. But this was not a simple photograph. It was a composite of a series of partial images combined using a sophisticated kind of artificial intelligence-based image processing called sparse modelling. While deep learning has gotten much of the attention in AI-utilizing circles, sparse modeling offers another way for embedded vision to improve your image system, and it has distinct advantages all its own.
Embedded vision refers to the use of a camera and a processing unit “embedded” in a larger system for which it is specifically designed. These smaller embedded systems can use AI in order to automate certain tasks, removing the necessity of human intervention.
In the example of one well-known form of artificial intelligence, deep learning, these algorithms are trained on large data sets in order to understand and sort through all the information that an image contains by comparing it to a large library of other images. This sort of image processing is very powerful, but it does have its drawbacks. One significant drawback is the amount of computational resources that it requires.
The appeal of sparse modeling, another form of ai-based image processing, is that it reduces the computational resources required due to its more targeted nature. Inspired in part by contemporary anatomical work that recognizes that the optic nerve first captures a “sparse” image involving relatively few neurons , sparse modeling uses statistics and information theory in order to realize the benefits of embedded vision while minimizing the costs involved with intensive computer modeling. Using patterns that tend to be at the edges of the objects the algorithm is trained on, sparse modeling reconstructs and understands more for less. Unlike deep learning, which uses large datasets and a lot of raw computing power to predict what it is looking at, sparse modelling uses a minimal dataset to compare only certain important facets of each dataset that it works with. For this reason, it’s more useful in applications where you know exactly what your embedded vision system is going to see, and so it can be specialized for a smaller subset of objects. Because of this targeting, sparse modelling does not need additional hardware such as a GPU; instead, it can be easily integrated into your already existing systems. Thus, the ability to be slotted into smaller systems makes sparse modeling a better fit for applications like embedded vision.
Sparse modeling’s uses ran the gamut from commonplace to esoteric. It is used, for example, in facial recognition software as well as in some self-driving automation systems. More abstractly, sparse modeling has been used in such different contexts as “the classification of natural images” and for the “quantification of artistic style.” Sparse modeling has also found uses in the manufacturing and medical fields due to its flexibility and the ease with which these models can be trained. In the future, sparse modeling for analyzing visual information stands to develop along with our understanding of how the human eye functions. Some scientists are even trying to use sparse modeling to understand planets outside of our solar system! The wide-variety of uses that sparse modeling is and will be used for demonstrates the flexibility and power it offers anyone interested in a flexible yet powerful tool for image analysis.
Combined with embedded vision, sparse modeling can change your business today. Cameras offered by Phase 1 Technology such as the Baumer EX series can be utilized with a sparse modelling algorithm to ensure the success of your particular application. If you want to examine the wider range of cameras that we offer, our full catalogue can be found here. No matter what camera you need, we hope that you are as excited as we are about the prospects that sparse modeling offers your industry!
Do you want to add machine vision to your next automation project? Purchase a quality machine vision camera from the experts at Phase 1 Technology Corp.