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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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
Original languageEnglish
Title of host publication Proceedings of the IEEE International Conference on Big Data (BigData2020)
PublisherIEEE
Publication date2020
Edition10
Pages5172-5181
DOIs
Publication statusPublished - 2020
Event2020 IEEE International Conference on Big Data (BigData2020) - Virtual
Duration: 10 Dec 202013 Dec 2020

Conference

Conference2020 IEEE International Conference on Big Data (BigData2020)
ByVirtual
Periode10/12/202013/12/2020

ID: 258658900