Name
Abstract | ||
|---|---|---|
This paper proposes a new acne detection approach using a Markov random field (MRF) model and chromophore descriptors extracted by bilateral decomposition. Compared to most existing acne segmentation methods, the proposed algorithm enables to cope with large-dynamic-range intensity usually existing in conventional RGB acne images captured under uncontrolled environment. Algorithm performance has been tested on acne images of human face from a free public database. Experimental results show that acne segmentation derived from this new approach highly agrees to human visual inspection. Moreover, inflammatory response and hyperpigmentation scar can be well discriminated. It is expected that a computer-assisted diagnostic system for acne severity evaluation will be constructed as a consequence of the present work. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | image segmentation, acne detection, chromophore descriptors, Markov random fields (MRFs) |
Field | DocType | Citations |
Computer vision,Scale-space segmentation,Diagnostic system,Segmentation,Markov random field,Computer science,Image segmentation,RGB color model,Artificial intelligence,Acne | Conference | 1 |
PageRank | References | Authors |
0.41 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Liu | 1 | 1 | 0.41 |
| Josiane Zerubia | 2 | 2032 | 232.91 |