Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Nearest neighbour fields accurately and intuitively describe the transformation between two images and have been heavily used in computer vision. Generating such fields, however, is not an easy task due to the induced computational complexity, which quickly grows with the sizes of the images. Modern parallel devices such as graphics processing units depict a viable way of reducing the practical run time of such compute-intensive tasks. In this work, we propose a novel parallel implementation for one of the state-of-the-art methods for the computation of nearest neighbour fields, called p ropagation-assisted k -d trees. The resulting implementation yields valuable computational savings over a corresponding multi-core implementation. Additionally, it is tuned to consume only little additional memory and is, hence, capable of dealing with high-resolution image data, which is vital as image quality standards keep rising
Titel Proceedings of the IEEE International Conference on Big Data (BigData2020)
StatusUdgivet - 2020
Begivenhed2020 IEEE International Conference on Big Data (BigData2020) - Virtual
Varighed: 10 dec. 202013 dec. 2020


Konference2020 IEEE International Conference on Big Data (BigData2020)

ID: 258658900