J.G.Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Amer., 2:1160--1169, 1985.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... functions) can achieve such a lower bound they are very useful in many spectral analysis tasks such as image representation (e.g. 20] and the spatio temporal analysis of motions in image sequences (e.g. 1, 15] Besides, Gabor filters were shown to approximate biological models of vision (e.g. [7, 19, 16]) In the spatio temporal models for motion estimation [1, 2] the energy spectrum of a constant translational motion can be characterized as an oriented plane passing through the origin in the spectral domain. Sampling the spectrum with a set of Gabor filters at different frequencies and ....

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by twodimensional visual cortical filters. Journal of the Optical Society of America, 2(7):1160--1169, 1985.

....exactly, as in the case of the exact orientation steerability, we will no more be able to localize this component in the spatial domain. Therefore, we need many Dirac impulses to increase the localization capability in space. This trade o can be optimized by applying functions with Gaussian shape [7]. Therefore, the approximate steerability has better properties with respect to the uncertainty principle. To summarize, our main concern is orientational resolution with low complexity. To achieve this goal, we directly built our lter in the spatial domain. The price we pay is that we do not ....

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical lters. Journal of the Optical Society of America, 2(7):1160-1169, 1985.

....of neurons and connections decrease and the connections become more adaptive in higher layers. The major elements of the system are: Primary features: The primary features should detect local, generic shape related information from the image. A self similar family of Gabor lters (see, e.g. [29]) is used for this task, since the Gabor lters have optimal combined resolution in spatial and frequency domains. Self organized features: To form complex features the Gabor lter outputs are clustered to natural, possibly non convex clusters by multilayer self organizing map. Classier: Only the ....

J. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2d visual cortical lters. J. Opt. Soc. Am. (A), 2(7):11601169, 1985.

....resolution, while these basis lters usually have wide supports which accentuate the computational burden. The trade o between spatial and spectral localization can be optimized only by using functions with Gaussian shape, since they achieve the lower bound in the uncertainty principle [4]. Based on this motivation, we present in this paper a new 3D steerable lter using angular Gaussian functions to achieve high orientation resolution. Before the steerability was introduced, Big un et al. connected the orientation analysis with symmetry detection using the principal axis analysis ....

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical lters. Journal of the Optical Society of America, 2(7):1160-1169, 1985.

....as in the case of the exact orientation steerability, we will no 8 more be able to localize this component in the spatial domain. Therefore, we need many Dirac impulses to increase the localization capability in space. This trade off can be optimized by applying functions with Gaussian shape [8]. Therefore, the approximate steerability has better properties with respect to the uncertainty principle. To summarize, our main concern is orientational resolution with low complexity. To achieve this goal, we directly built our filter in the spatial domain. The price we pay is that we do not ....

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, 2(7):1160--1169, 1985.

....This mechanism suggests that the visual system decomposes the retinal image into a set of sub bands. This can be realized by filtering the image with a bank of linear filters followed by some nonlinear procedures. The filtering theory developed along this direction includes the Gabor filters [6] and wavelet pyramids [16] The filtering methods have demonstrated excellent performance in texture classification and segmentation [14] A thorough review of texture segmentation using Gabor filter banks can be found in [1] Significant features in the image correspond to high density regions ....

J, Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by tow-dimensional visual cortical filters, Journal of Optical Society of America, vol. A, 1160-1169, 1985.

....needed. The knot class definitions are largely based on appearance of certain details, such as a dark gap between the knot and the sound wood, cracks in the knot, overall shape of the knot, etc. To code the shape related information into features, a self similar family of Gabor filters (see e.g. [3]) is used to transform the information from the gray scale image to more consistent form for the self organizing feature clustering. However, in many studies (see e.g. 10] color distribution of the knot images is shown to be a powerful discriminator for the classes. In the system described here, ....

....of the recognition system, showing both the shape related and color related feature paths. 2.2. Building the primary features with Gabor filters The 2D Gabor filters are orientation and frequency sensitive band pass filters which are optimally localized in both spatial and frequency domains [3], making them suitable for extracting the orientation dependent frequency contents, i.e. edge like features, from as small an area as possible. The forms of the Gabor kernels are spatial sinusoids localized by a Gaussian window. They operate directly on the digital image in spatial domain. The ....

J. Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2D visual cortical filters, J. Opt. Soc. Am. (A), Vol. 2, No. 7, 1985, pp. 1160-1169.

....the classifier is trained with labeled samples on top of the self organized features. The basic features used in the system are 2D Gabor filters (see e.g. 3] that are orientation and frequency sensitive band pass filters which are optimally localized in both spatial and frequency domains [2], making them suitable for extracting the orientation dependent frequency contents, i.e. edge like features, from as small an area as possible. In Fig.3 the principle of the feature construction system is outlined. For detailed description see [6] and [7] Gabor Map Feature MAP MAP Shape ....

J. Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2D visual cortical filters, J. Opt. Soc. Am. (A), Vol. 2, No. 7, 1985, pp. 1160-1169.

....of visual angle. on wavenumber k 0 = k 0 cos 0 ; k 0 sin 0 ) G(k 0 ; 0 ) e Gamma Gamma log 2 (k=k 0 ) Deltak Delta 2 e Gamma Gamma Gamma 0 Delta Delta 2 ; 2) where Deltak = 0:5, Delta = 8. The filters, shown in Figure 3, are chosen to model the visual channels [8]. Each channel of the distorted image is compared to the same channel from the original image and a masking model applied [5] The masking model used here allows only within channel masking and uses masking weights computed as the inverse the normalised detection threshold: C T = 1 CM C T0 ....

J.Daugman. Uncetainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensionals visual cortical filters. Journal of the Optical Society of America, A2(7):1160--1169, July 1985.

....needed. The knot class definitions are largely based on appearance of certain details, such as a dark gap between the knot and the sound wood, cracks in the knot, overall shape of the knot, etc. To code the shape related information into features, a self similar family of Gabor filters (see e.g. [4]) is used to transform the information from the gray scale image to more consistent form for the self organizing feature clustering. However, in many studies (see e.g. 15] color distribution of the knot images is shown to be a powerful discriminator for the classes. In the system described here, ....

....5: A schematic of the classification system combining shape based and color based information. 3.3. Building the primary features with Gabor filters The 2D Gabor filters are orientation and frequency sensitive band pass filters which are optimally localized in both spatial and frequency domains [4], making them suitable for extracting the orientation dependent frequency contents, i.e. edge like features, from as small an area as possible. The forms of the Gabor kernels are spatial sinusoids localized by a Gaussian window. They operate directly on the digital image in spatial domain. The ....

J. Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2D visual cortical filters, J. Opt. Soc. Am. (A), Vol. 2, No. 7, 1985, pp. 1160-1169.

.... the primary features with Gabor transformation Gabor filters were introduced to image processing by Granlund [9] and analyzed by Daugman [5] The 2D Gabor filters are orientation and frequency sensitive band pass filters which are optimally localized in both spatial and frequency domains [4], making them suitable for extracting the orientation dependent frequency contents, i.e. edge like features, from as small an area as possible. The forms of the Gabor kernels are spatial sinusoids localized by a Gaussian window. They operate directly on the digital image in spatial domain. The ....

J. Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2D visual cortical filters, J. Opt. Soc. Am. (A), Vol. 2, No. 7, 1985, pp. 1160-1169.

....in feature space, an invertible transform is required. While there are several viable options, including orthogonal wavelet transforms, Gabor wavelets were chosen for their desirable properties, as follows. Gabor functions achieve the theoretical minimum spacefrequency bandwidth product [13] [14], 18] i.e. spatial resolution is maximized for a given bandwidth. 1057 7149 99 10.00 1999 IEEE 256 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 8, NO. 2, FEBRUARY 1999 . Gabor functions are used as (nonorthogonal) basis functions for exact signal representation. A narrowband Gabor ....

, "Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters," J. Opt. Soc. Amer., vol. 2, pp. 1160--1169, 1985.

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J.G.Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Amer., 2:1160--1169, 1985.

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J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, 2(7):1160--1169, 1985.

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J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical lters. Journal of the Optical Society of America, 2(7):1160-1169, 1985.

No context found.

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by twodimensional visual cortical filters. Journal of the Optical Society of America, 2(7):1160--1169, 1985.

No context found.

J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical lters. Journal of the Optical Society of America, 2(7):1160-1169, 1985.

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J. Daugman. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, A 4:221--231, 1985.

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J. Daugman, Uncertainty relation for resolution in space, spatial frequency and orientation optimized by tow-dimensional visual cortical filters, J. Optical Society of America A, 11601169, 1985.

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