2015/12/29 by Grzegorz Kurzejamski, Kurzejamski, Grzegorz, J. Zawistowski +4
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote-Sensing Image Classification #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1512.08648
23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) 2015 Short Paper. Computer Science Research Notes CSRN 2502, ISSN 2464-4617, ISBN 978-80-86943-66-4, 2015
arxiv created 2015/12/29 · openalex publication_date 2015/12/29 · arxiv updated 2015/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a framework designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. The framework uses a single feedback loop and a pattern resizing mechanism to demonstrate the top effectiveness of the state-of-the-art local features. A high detection rate with a low false detection chance can be achieved with use of only one pattern per object and no manual parameters adjustments. The method incorporates well known local features and a basic matching process to create a reliable voting space. Further steps comprise of metric transformations, graphical vote space representation, two-phase vote aggregation process and a cascade of verifying filters.