Bookmark and Share

"Implementing Image Pyramids Efficiently in Software," a Presentation from Polymorphic Technologies

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Michael Stewart, Proprietor of Polymorphic Technologies, presents the "Implementing Image Pyramids Efficiently in Software," tutorial at the May 2018 Embedded Vision Summit.

An image pyramid is a series of images, derived from a single original image, wherein each successive image is at a lower resolution than its predecessors. Image pyramids are widely used in computer vision, for example to enable detection of features at different scales.

After a brief introduction to image pyramids and their uses in vision applications, Stewart explores techniques for efficiently implementing image pyramids on various processor architectures, including CPUs and GPUs. He illustrates the use of fixed- and floating-point arithmetic, vectorization and parallelization to speed up image pyramid implementations. He also examines how memory caching approaches impact the performance of image pyramid code and discusses considerations for applications requiring real-time response or minimum latency.