Name
Abstract | ||
|---|---|---|
In this work we assess the recently introduced Local Phase Quantization (LPQ) as textural descriptor for the problem of forest species recognition. LPQ is based on quantizing the Fourier transform phase in local neighborhoods and the phase can be shown to be a blur invariant property under certain commonly fulfilled conditions. We show through a series of comprehensive experiments that LPQ surpasses the results achieved by the widely used Local Binary Patterns (LPB) and its variants. Our experiments also show, though, that the results can be further improved by combining both LPB and LPQ. In this sense, several different combination strategies were tried out. Using a SVM classifiers, the combination of LPB and LPQ brought an improvement of about 7 percentage points on a database composed by 2,240 microscopic images extracted from 112 different forest species. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IECON.2012.6388523 | Montreal, QC |
Keywords | Field | DocType |
fourier transforms,forestry,image texture,object recognition,pattern classification,support vector machines,fourier transform phase,lpb,lpq,svm classifiers,blur invariant property,comprehensive experiments,forest species recognition,local binary patterns,local phase quantization,microscopic images,textural descriptor,textural descriptors | Pattern recognition,Phase quantization,Image texture,Support vector machine,Local binary patterns,Fourier transform,Invariant (mathematics),Artificial intelligence,Quantization (signal processing),Mathematics,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
1553-572X E-ISBN : 978-1-4673-2420-5 | 978-1-4673-2420-5 | 8 |
PageRank | References | Authors |
0.60 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. G. Martins | 1 | 58 | 2.65 |
| Luiz S. Oliveira | 2 | 476 | 47.22 |
| Robert Sabourin | 3 | 908 | 61.89 |