Detecting Objects

This article is part of a series introducing developers to Computer Vision. Check out other articles in this series.

In my career, object detection and tracking has been one of the hottest topics in Computer Vision. I wish I could dive right into what makes all of it possible, but I learned that object detection and tracking relies on a whole lot of other concepts — most of which we’ve already covered in previous articles. First, let’s first define what these terms mean so that there’s no confusion.


Describing Features

This article is part of a series introducing developers to Computer Vision. Check out other articles in this series.

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Photo by Aniket Deole on Unsplash

What’s a Descriptor, Anyway?

The reality is that extracting edges, corners or blobs from an image by itself isn’t as helpful as we would like. We just have points within an image — how can we start to understand the image a bit deeper? How do we go from having a bunch of points in an image to understanding that there’s an airplane or a person in the image? Feature Descriptors are algorithms that look at additional information around a Feature Point to better understand what it represents. …


Improving Features

This article is part of a series introducing developers to Computer Vision. Check out other articles in this series.

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Photo by Gordon Mak on Unsplash

When I first started learning about Scale Invariance, I assumed we could just run the Harris Corner detector across the different levels in a scale space to find invariant features. And, we can — this specific method is called the Harris-Laplace Corner detector. But as I researched and learned more about Scale Invariant detectors, there was one method that kept coming up over and over again. This method is the Difference of Gaussian (DoG) detector.

I found the Difference of Gaussian (DoG) technique really interesting for a couple of reasons. While the Harris Corner detector focuses on, well, corners, the DoG technique identifies blobs. Blobs are a more amorphous region of pixels that share something in common, such as intensity. I also noticed that the DoG technique is often referenced in papers that deal with Feature Descriptors, which we will learn about later. The last bit worth mentioning is that DoG identifies both edges and blob features in the same operation. …

About

Vinny DaSilva

Technical Product Manager and Developer Relations specializing in AR & VR. Team ThinkReality at Lenovo. Previously at Samsung NEXT, Vuforia

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