Comparison of texture features based on Gabor filters

Grigorescu, S.E.; Petkov, Nicolai; Kruizinga, P.

doi:10.1109/tip.2002.804262

Cited by 628 publications

(280 citation statements)

References 58 publications

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“…The spatial summation properties of simple cells were modeled by linear filters followed by half-wave rectification (Movshon et al 1978b;Andrews and Pollen 1979;Glezer et al 1980;Kulikowski and Bishop 1981) and Gabor functions proved to be particularly well suited for this purpose (Marcelja 1980;Daugman 1985;Jones and Palmer 1987). Complex cells needed more intricate modeling, which included linear filtering, half-wave rectification and subsequent local spatial summation, or quadrature pair summation of linear filter responses (Movshon et al 1978a;Spitzer and Hochstein 1985;Morrone and Burr 1988;Petkov and Kruizinga 1997;Kruizinga and Petkov 1999;Grigorescu et al 2002, Grigorescu et al 2003. These computational models contributed to understanding of the functions of simple and complex cells and gave the basis for biologically motivated edge detection algorithms in image processing and computer vision (see Fig.…”

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Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Petkov

Subramanian

2007

Biol Cybern

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We study the orientation and speed tuning properties of spatiotemporal three-dimensional (3D) Gabor and motion energy filters as models of time-dependent receptive fields of simple and complex cells in the primary visual cortex (V1). We augment the motion energy operator with surround suppression to model the inhibitory effect of stimuli outside the classical receptive field. We show that spatiotemporal integration and surround suppression lead to substantial noise reduction. We propose an effective and straightforward motion detection computation that uses the population code of a set of motion energy filters tuned to different velocities. We also show that surround inhibition leads to suppression of texture and thus improves the visibility of object contours and facilitates figure/ground segregation and the detection and recognition of objects.

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Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Petkov

Subramanian

2007

Biol Cybern

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“…(Gradshtein & Ryzhik, 1994) -A 32-bin histogram of a 1×400 vector produced by Chebyshev transform of the image with order of N=20. (Gabor, 1946), where the kernel is in the form of a convolution with a Gaussian harmonic function (Gregorescu, Petkov & Kruizinga, 2002), and 7 different frequencies are used (1,2…,7), providing 7 image descriptor values.…”

Section: Classification Methodsmentioning

confidence: 99%

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Shamir

2008

Int J Comput Vis

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Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods.Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.

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“…The raw responses of Gabor filter bank can be used as features for classification but usually some preprocessing is performed to acquire most representative features e.g. Gabor energy and moments of Gabor filter bank responses [17,5]. Similar to the approach given in [17] for vehicle detection, in this paper, magnitude response of each Gabor filter in the bank is collected from each sub-window and is represented by three moments: the mean , i j , the standard deviation , i j and the skewness , i j k (where i corresponds to the ith filter in the bank and j to the jth sub-window).…”

Section: Feature Extractionmentioning

confidence: 99%

Mass detection in digital mammograms using gabor filter bank

Hussain¹,

Khan²,

Muhammad³

et al. 2012

IET Conference on Image Processing (IPR 2012)

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Digital Mammograms are currently the most effective imaging modality for early detection of breast cancer but the number of false negatives and false positives is high. Mass is one type of breast lesion and the detection of masses is highly challenged problem. Almost all methods that have been proposed so far suffer from high number of false positives and false negatives. In this paper, a method for detecting true masses is presented, especially, for the reduction of false positives and false negatives. The key idea of the proposal is the use of Gabor filter banks for extracting the most representative and discriminative local spatial textural properties of masses that are present in mammograms at different orientations and scales. The system is evaluated on 512 (256 normal+256 true mass) regions of interests (ROIs) extracted from digital mammograms of DDSM database. We performed experiments with Gabor filter banks having different numbers of orientations and scales to find the best parameter setting. Using a powerful feature selection technique and support vector machines (SVM) with 10-fold cross validation, we report to achieve Az = 0.995±0.011, the area under ROC. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods, which are based on texture description, and the difference is statistically significant.

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Comparison of texture features based on Gabor filters

Cited by 628 publications

References 58 publications

Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Mass detection in digital mammograms using gabor filter bank

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