Object detection using gabor filters
By Anil K. Jain, Nalini K. Ratha, S. Lakshmanan. 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.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Feature representation for statistical-learning-based object detection: A review (2015, background): “The illumination-invariance of shape features is determined by the applied contour detection in pre-step while that of Gabor [30] and Haar wavelets [38] needs to be improved.”
- A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “The Gabor feature (Jain et al., 1997) is a classical kind of texture features.”
- Modeling and Detection of Geospatial Objects Using Texture Motifs (2006, background): “Jain et al. [16] use Gabor filters to derive features that are then utilized for segmenting objects, such as tanks, cars, and fingerprints , in complex backgrounds.”
- Data clustering: a review (1999, methodology): “This Gabor filter based technique has proven very powerful and has been extended to the automatic segmentation of text in documents [Jain and Bhattacharjee 1992] and segmentation of objects in complex backgrounds [Jain et al. 1997].”
- Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures (2012, methodology): “These methods might be placed into a few broad categories: those that aim to distinguish the foreground regions of images from the background [33, 12, 65, 39], segment images into distinct regions to perform localization [35, 23, 56], track an object over a sequence of images (after its position has”
- SpotGarbage: smartphone app to detect garbage using deep learning (2016, background): “The popular set of image features include histogram of oriented gradients (HOG) [6, 9], scale invariant feature transform (SIFT) [16, 24], Gabor filters [12], Gabor”
- A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images (2009, methodology): “[22] have employed Gabor filters to perform segmentation of objects from complex backgrounds.”
- Defect detection in textured materials using Gabor filters (2000, methodology): “Gabor filters achieve optimal joint localization in spatial and spatial frequency domain [25] and, therefore, have been used extensively for texture analysis [26]–[29] and document analysis [30], [31], and object detection [32].”