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Object detection using gabor filters
- Source :
- Pattern Recognition. 30:295-309
- Publication Year :
- 1997
- Publisher :
- Elsevier BV, 1997.
-
Abstract
- This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (“Gabor features”) and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image—it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query.
- Subjects :
- Pixel
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Object (computer science)
Object detection
Artificial Intelligence
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
Signal Processing
Pattern recognition (psychology)
Computer vision
Segmentation
Computer Vision and Pattern Recognition
Artificial intelligence
business
Cluster analysis
Software
Subjects
Details
- ISSN :
- 00313203
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition
- Accession number :
- edsair.doi...........bd0bf0f8de5cbcacb86178f7eaf2f65a