Name
Abstract | ||
|---|---|---|
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach. We outperform the present state-of-the-art, excelling particularly in the high-precision regime, while running an order of magnitude faster and requiring no post-processing. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.01439 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
25 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camaro, C.-O.Dufresne | 1 | 9 | 1.89 |
| Morteza Rezanejad | 2 | 1 | 1.03 |
| Stavros Tsogkas | 3 | 94 | 6.80 |
| Kaleem Siddiqi | 4 | 3259 | 242.07 |
| Sven J. Dickinson | 5 | 2836 | 185.12 |