Name
Abstract | ||
|---|---|---|
Edge detection remains a hot topic due to its importance as a low level operation for high level operations in computer vision and the fact that there is no edge detector that is optimal for all kinds of images. In this paper, a new edge detector is proposed. The algorithm relies on the concept of edge detection as an imbalanced binary classification problem. In particular, each pixel is characterized by a gradients feature vector and classified as edge or non-edge pixel by means of logistic regression and hysteresis. This algorithm outperforms other state-of-the-art edge detectors both from the visual and quantitative points of view. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66824-6_5 | ADVANCES IN FUZZY LOGIC AND TECHNOLOGY 2017, VOL 2 |
Keywords | Field | DocType |
Edge detection,Imbalanced classification,SMOTE,Logistic regression,Gradient | Feature vector,Pattern recognition,Binary classification,Edge detection,Computer science,Edge detector,Pixel,Artificial intelligence,Detector,Logistic regression | Conference |
Volume | ISSN | Citations |
642 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raquel Fernandez-Peralta | 1 | 0 | 0.34 |
| Sebastià Massanet | 2 | 438 | 34.95 |
| Arnau Mir | 3 | 59 | 14.40 |