Name
Title | ||
|---|---|---|
Supervised Segmentation Based on Texture Signatures Extracted in the Frequency Domain |
Abstract | ||
|---|---|---|
Texture identification can be a key component in Content Based Image Recognition systems. Although formal definitions of texture vary in the literature, it is commonly accepted that textures are naturally extracted and recognized as such by the human visual system, and that this analysis is performed in the frequency domain. The method presented here employs a discrete Fourier transform in the polar space to extract features, which are then classified with a vector quantizer for supervised segmentation of images into texture regions. Experiments are conducted on a standard database of test problems that show this method compares favorably with the state-of-the-art and improves over previously proposed frequency-based methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72847-4_13 | IbPRIA |
Keywords | Field | DocType |
supervised segmentation,texture region,discrete fourier,frequency domain,frequency-based method,formal definition,image recognition system,polar space,texture identification,human visual system,key component,discrete fourier transform,image recognition | Frequency domain,Computer vision,Pattern recognition,Human visual system model,Segmentation,Computer science,Image texture,Local binary patterns,Vector quantization,Artificial intelligence,Discrete Fourier transform,Quantization (signal processing) | Conference |
Volume | ISSN | Citations |
4477 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonella Di Lillo | 1 | 7 | 2.27 |
| Giovanni Motta | 2 | 88 | 8.98 |
| James A. Storer | 3 | 931 | 156.06 |