A set of so-called cortical images, motivated by the function of simple cells in the primary visual cortex of mammals, is computed from each of two input images and an image pyramid is constructed for each cortical image. The two sets of cortical image pyramids are matched synchronously and an optimal mapping of the one image onto the other image is determined. The method was implemented on the Connection Machine CM-5 of the University of Groningen in the data-parallel programming model and applied to the problem of face recognition.

A computational model of so-called grating cells is proposed. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond strongly to bar gratings of a given orientation and periodicity but very weakly or not at all to single bars. This non-linear behavior is quite different from the spatial frequency filtering behavior exhibited by the other types of orientation selective cells. It is incorporated in the proposed model by using an AND-like non-linearity to combine the responses of simple cells and compute the activities of so-called grating subunits which are subsequently summed up. The parameters of the model are adjusted to reproduce the results measured by neurophysiologists with different visual stimuli. The proposed computational model of a grating cell is used to compute the collective activation of sets of such cells, referred to as cortical images, induced by natural visual stimuli. On the basis of the results of such simulations we speculate about the possible role of grating cells in the visual system and demonstrate the usefulness of grating cell operators for some computer vision tasks, such as automatic face recognition and document processing.

A biologically motivated compute intensive approach to computer vision is developed and applied to the problem of face recognition. The approach is based on the use of two-dimensional Gabor functions that fit the receptive fields of simple cells in the primary visual cortex of mammals. A descriptor set that is robust against translations is extracted by a global reduction operation and used for a search in an image database. The method was applied on a database of 205 face images of 30 persons and a recognition rate of 94% was achieved.

The performance of a number of texture feature operators is evaluated. The features are all based on the local spectrum which is obtained by a bank of Gabor filters. The comparison is made using a quantitative method which is based on Fisher's criterion. It is shown that, in general, the discrimination effectiveness of the features increases with the amount of post-Gabor processing.

The performance of two well-known texture operators (based on Gabor energy and the cooccurrence matrix) is compared with the performance of a new, biologically motivated texture operator, the dot-pattern selective cell operator. The comparison is made using a quantitative method based on the Mahalanobis distance. Together with some classification experiments the comparison shows a clear superiority of the new operator in dot-pattern texture problems.

Texture feature extraction operators, which comprise linear filtering, eventually followed by post-processing, are considered. The filters used are Laws' masks (1980), filters derived from well-known discrete transforms, and Gabor filters. The post-processing step comprises nonlinear point operations and/or local statistics computation. The performance is measured by means of the Mahalanobis distance between clusters of feature vectors derived from different textures. The results show that post-processing improves considerably the performance of filter based texture operators

Texture is an important part of the visual world of animals and humans and their visual systems successfully detect, discriminate, and segment texture. Relatively recently progress was made concerning structures in the brain that are presumably responsible for texture processing. Neurophysiologists reported on the discovery of a new type of orientation selective neuron in areas V1 and V2 of the visual cortex of monkeys which they called grating cells. Such cells respond vigorously to a grating of bars of appropriate orientation, position and periodicity. In contrast to other orientation selective cells, grating cells respond very weakly or not at all to single bars which do not make part of a grating. Elsewhere we proposed a nonlinear model of this type of cell and demonstrated the advantages of grating cells with respect to the separation of texture and form information. In this paper, we use grating cell operators to obtain features and compare these operators in texture analysis tasks with commonly used feature extracting operators such as Gabor-energy and co-occurrence matrix operators. For a quantitative comparison of the discrimination properties of the concerned operators a new method is proposed which is based on the Fisher (1923) linear discriminant and the Fisher criterion. The operators are also qualitatively compared with respect to their ability to separate texture from form information and their suitability for texture segmentation.

The performance of two well-known texture operators (based on Gabor-energy and the cooccurrence matrix) is compared with the performance of a new, biologically motivated texture operator, the grating cell operator, previously proposed by the authors (1995, 1997). The comparison is made using a new quantitative method, based on the Fisher criterion (1923). The results show the clear superiority of the new operator in oriented texture problems.

Computational models of periodic- and aperiodic-pattern selective cells, also called grating and bar cells, respectively, are proposed. Grating cells are found in areas V1 and V2 of the visual cortex of monkeys and respond strongly to bar gratings of a given orientation and periodicity but very weakly or not at all to single bars. This non-linear behaviour, which is quite different from the spatial frequency filtering behaviour exhibited by the other types of orientation-selective neurons such as the simple cells, is incorporated in the proposed computational model by using an AND-type non-linearity to combine the responses of simple cells with symmetric receptive field profiles and opposite polarities. The functional behaviour of bar cells, which are found in the same areas of the visual cortex as grating cells, is less well explored and documented in the literature. In general, these cells respond to single bars and their responses decrease when further bars are added to form a periodic pattern. These properties of bar cells are implemented in a computational model in which the responses of bar cells are computed as thresholded differences of the responses of corresponding complex (or simple) cells and grating cells. Bar and grating cells seem to play complementary roles in resolving the ambiguity with which the responses of simple and complex cells represent oriented visual stimuli, in that bar cells are selective only for form information as present in contours and grating cells only respond to oriented texture information. The proposed model is capable of explaining the results of neurophysiological experiments as well as the psychophysical observation that the perception of texture and the perception of form are complementary processes.

We propose to compute a vector eld, similar to the optical ow computed between successive frames of an image sequence, which maps optimally (in a certain sense) one face image onto another face image. A cost of the mapping is computed and used to quantify the dissimilarity between the two images. The technique is applied to the problem of person identiication by comparing an input face image to all face images prestored in a database. The method was implemented on the Connection Machine CM-5 of the University of Groningen 1 in the data-parallel programming model.

A computationally intensive approach to pattern recognition in images is developed and applied to face recognition. Similarly to previous work, we compute functional inner products of a two-dimensional input signal (image) with a set of two-dimensional Gabor functions which fit the receptive fields of simple cells in the primary visual cortex of mammals. The proposed model includes nonlinearities, such as thresholding, orientation competition and lateral inhibition. The output of the model is a set of cortical images each of which contains only edge lines of a particular orientation in a particular light-to-dark transition direction. In this way the information of the original image is split into different channels. The cortical images are used to compute a lower-dimension space representation for object recognition. The method was implemented on the Connection Machine CM-5(1) and achieved a recognition rate of 97% when applied to a large database of face images.

We propose to compute a vector field, similar to the optical flow computed between successive frames of an image sequence, which maps optimally (in a certain sense) one face image onto another face image. A cost of the mapping is computed and used to quantify the dissimilarity between the two images. The technique is applied to the problem of person identification by comparing an input face image to all face images prestored in a database. The method was implemented on the Connection Machine CM-5 of the University of Groningen1 in the data-parallel programming model.

This work presents explorations in the microstructure of natural vision systems based on large scale computer simulations Similarly to previous work in this area we compute the functional inner products of a two dimensional input signal image with a set of two dimensional Gabor functions which have been shown to t the receptive elds of simple cells in the primary visual cortex of mammals These inner products are then considered as net inputs to the cortical cells and used to compute the cell activations as non linear functions A previously used model is extended with a pixel wise winner takes all competition between di erent Gabor lters which is introduced in order to model lateral inhibition between cortical cells The e ect of lateral inhibition is qualitatively estimated by visualization of computed cortical images and quantitatively evaluated by applying the model to a face recognition problem Recognition rate of was achieved on a database of face images of persons vs achieved with a previously used model

A biologically motivated, computationally intensive approach to computer vision is developed and applied to the problem of automatic face recognition. The approach is based on the use of two-dimensional Gabor functions which model the receptive eld functions of simple cells in the primary visual cortex of mammals. The convolutions of an input image with a set of antisymmetric visual receptive eld functions (imaginary parts of Gabor functions) become the subject of thresholding and orientation competition. The developed cortical lters deliver highly structured information which is used for eecient feature extraction and representation in a lower dimension space. Applied to face recognition, the method gives a recognition rate of 98.5% on a large database of face images.