Conference Proceedings


Noel E. O'Connor
Remi Trichet


Computer Science

computer vision feature detection image processing experimental edge low cost state of the art computer applications

TREAT: Terse Rapid Edge-Anchored Tracklets (2016)

Abstract Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Subject = Computer Science: Image processing
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics

Full list of authors on original publication

Noel E. O'Connor, Remi Trichet

Experts in our system

Noel E. O'Connor
Dublin City University
Total Publications: 449
Remi Trichet
Dublin City University
Total Publications: 4