Name
Abstract | ||
|---|---|---|
In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1587/transinf.E95.D.1698 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
frequency spectrum, cepstrum, multiple kernel learning | Computer vision,Mel-frequency cepstrum,Pattern recognition,Radial basis function kernel,Computer science,Multiple kernel learning,Cepstrum,Speech recognition,Frequency spectrum,Artificial intelligence,Kernel method,Kernel (image processing) | Journal |
Volume | Issue | ISSN |
E95D | 6 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||