The Compelling Challenges of Engineering Computer Vision

Of all the interesting obstacles slowing down the advancement of artificial intelligence, computer vision may be the most compelling. This is due to the multifaceted challenge of programming a machine with enough inductive reasoning to extrapolate information from observations and come up with plausible and accurate conclusions. Of course, this is the end goal of artificial intelligence research – endowing a computer with the power and ability to think, at least within reason. When it comes to translating flexible human thought processes into more structured machines, there are a handful of problems that slow down the computer’s mastery.

Quick Thinking: Classifying Sights at a Glance

While we move around the world and throughout our daily routines, we see an uncountable number of images that our brain parses through and then separates into different classifications. To us, this process of simplification and differentiation is done without conscious thought, but the trick of classifying images is a little more difficult for a computer. We have years of practice amid hundreds of contexts to fall back on, while a computer must decode all this input from scratch. Engineers and researchers are solving this problem by feeding computers a cavalcade of images to fine-tune their identification abilities, then programming the machines to scan images one set of pixels at a time until an accurate classification is made. The process is speeding up by the day thanks to creative engineers.

Knowing Where to Look: Object Detection

When we need to look for specific objects or types of objects, years of context and experience help us narrow down our search. Computers do not have these helpful shortcuts, so training a computer to detect certain things is a difficult process. Clever researchers and programmers have begun narrowing things down, however, by telling computers to look at region clumps – areas in images that are denser and more clumped together than others – thus indicating a space where objects are gathered. When a computer can narrow its search this way, the object detection speeds up, but it still requires a lot of power and time. Much testing and research is required to hone this process, so it is a promising area for machine learning engineers.

Inferring Predictions: Tracking Specific Objects

It is hard enough for an aware human to track one person or object through their visual field, and it is even harder for computers to do so. The aforementioned problems of classification and detection come into play as the computer must first identify what the operator wishes it to track, then it has to parse through multitudes of image layers to keep its sights trained on that one object. Researchers are using multi-layered image captures to help computers stick to their targets, and in recent times, heat tracking has helped computers stay accurate. There is still a lot of work to be done, and this is promising for aspiring AI researchers.

Tracking Multiple Objects at Once: Image Segmentation

Finally, there is the trouble of identifying disparate parts of images and following them through their respective paths. This problem will require reams of research. It is clear that honing computer vision will take work, and this means the future for machine learning engineers is open and bright.

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