Bookmark and Share

"Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques," a Presentation from Imagination 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:


Paul Brasnett, Senior Research Manager for Vision and AI in the PowerVR Division at Imagination Technologies, presents the "Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques" tutorial at the May 2018 Embedded Vision Summit.

There has been a very significant shift in the computer vision industry over the past few years, from traditional vision algorithms to deep neural network algorithms. Many companies with experience and investment in classical vision algorithms want to utilize DNNs without discarding their existing investments. For these companies, can classical vision algorithms provide insights and techniques to assist in the development of DNN-based approaches?

In this talk, Brasnett looks at the similarities between classical and deep vision. He also looks at how a classical vision algorithm can be expressed and adapted to become a trainable DNN. This strategy can provide a low-risk path for developers transitioning from traditional vision algorithms to DNN-based approaches.