Name
Abstract | ||
|---|---|---|
We tackle the problem of detecting sources of combustion in high definition multispectral medium wavelength infrared (MWIR) (3-5 μm) images. We present a novel approach to this problem consisting of processing the images block-wise using a new technique that we call supervised principal component analysis (SPCA) to get the components of these blocks. This outperforms state-of-the-art methods with a significant reduction in the complexity of the whole scheme. As a classifier, we propose the use of a support vector machine (SVM) comparing the results from both its novelty-detection and binary non-linear versions. High performance is achieved from a small set of components. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/AVSS.2003.1217911 | AVSS |
Keywords | Field | DocType |
object detection,human face,infrared images,greedy search algorithm,vision system,face candidate,stereo cue,svm classifiers,likely image region,principal component analysis,feature extraction,support vector machine,image classification,computational complexity,combustion,support vector machines,infrared,pixel,multispectral images | Computer vision,Object detection,Pattern recognition,Computer science,Support vector machine,Multispectral image,Feature extraction,Artificial intelligence,Contextual image classification,Classifier (linguistics),Principal component analysis,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
0-7695-1971-7 | 1 | 0.44 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Santiago-Mozos | 1 | 39 | 4.04 |
| José M. Leiva-Murillo | 2 | 44 | 2.68 |
| Fernando Pérez-Cruz | 3 | 749 | 61.24 |
| A. Artés-Rodríguez | 4 | 5 | 1.23 |