Results 1  10
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44
Perceptual image distortion
 In Proceedings of SPIE
, 1994
"... In this paper, we present a perceptual distortion measure that predicts image integrity far better than meansquared error. This perceptual distortion measure is based on a model of human visual processing that ts empirical measurements of the psychophysics of spatial pattern detection. The model of ..."
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Cited by 129 (0 self)
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In this paper, we present a perceptual distortion measure that predicts image integrity far better than meansquared error. This perceptual distortion measure is based on a model of human visual processing that ts empirical measurements of the psychophysics of spatial pattern detection. The model of human visual processing proposed involves two major components: a steerable pyramid transform and contrast normalization. We also illustrate the usefulness of the model in predicting perceptual distortion in real images. 1.
Extraction of Perceptually Salient Contours by Striate Cortical Networks
, 1998
"... We present a corticalbased model for computing the perceptual salience of contours embedded in noisy images. It has been suggested (Gilbert, 1992; Field, Hayes & Hess, 1993) that horizontal intracortical connections in primary visual cortex may modulate contrast detection thresholds and preattent ..."
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Cited by 40 (4 self)
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We present a corticalbased model for computing the perceptual salience of contours embedded in noisy images. It has been suggested (Gilbert, 1992; Field, Hayes & Hess, 1993) that horizontal intracortical connections in primary visual cortex may modulate contrast detection thresholds and preattentive "popout ". In our model, horizontal connections mediate contextdependent facilitatory and inhibitory interactions among oriented cells. Strongly facilitated cells undergo temporal synchronization; and perceptual salience is determined by the level of synchronized activity. The model accounts for a range of reported psychophysical and physiological effects of contour salience (Polat & Sagi, 1993, 1994; Kapadia, Ito, Gilbert & Westheimer, 1995; Field et al., 1993; Kovács, Polat & Norcia, 1996; Pettet, McKee & Grzywacz, 1996). In particular, the model proposes that intrinsic properties of synchronization account for the increased salience of smooth, closed contours (Kovács & Julesz, 1993, ...
Efficient SpatialDomain Implementation Of A Multiscale Image Representation Based On Gabor Functions
, 1998
"... Gabor schemes of multiscale image representation are useful in many computer vision applications. However, the classic Gabor expansion is computationally expensive due to the lack of orthogonality of Gabor functions. Some alternative schemes, based on the application of a bank of Gabor filters, have ..."
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Cited by 35 (3 self)
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Gabor schemes of multiscale image representation are useful in many computer vision applications. However, the classic Gabor expansion is computationally expensive due to the lack of orthogonality of Gabor functions. Some alternative schemes, based on the application of a bank of Gabor filters, have important advantages such as computational efficiency and robustness, at the cost of redundancy and lack of completeness. In a previous work we proposed a quasicomplete Gabor transform, suitable for fast implementations in either space or frequency domains. Reconstruction was achieved by simply adding together the even Gabor channels. In this work, we develop an optimized spatialdomain implementation, using onedimensional, 11tap filter masks, that is faster and more flexible than Fourier implementations. The reconstruction method is improved by applying fixed and independent weights to the Gabor channels before adding them. Finally, we analyze and implement, in the spatial domain, two ways to incorporate a highpass residual, which permits a visually complete representation of the image.
Scalespace: A framework for handling image structures at multiple scales
, 1996
"... This article gives a tutorial overview of essential components of scalespace theory  a framework for multiscale signal representation, which has been developed by the computer vision community to analyse and interpret realworld images by automatic methods. 1 The need for multiscale representa ..."
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Cited by 31 (0 self)
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This article gives a tutorial overview of essential components of scalespace theory  a framework for multiscale signal representation, which has been developed by the computer vision community to analyse and interpret realworld images by automatic methods. 1 The need for multiscale representation of image data An inherent property of realworld objects is that they only exist as meaningful entities over In: Proc. CERN School of Computing, Egmond aan Zee, The Netherlands, 821 September, 1996. certain ranges of scale. A simple example is the concept of a branch of a tree, which makes sense only at a scale from, say, a few centimeters to at most a few meters, It is meaningless to discuss the tree concept at the nanometer or kilometer level. At those scales, it is more relevant to talk about the molecules that form the leaves of the tree, and the forest in which the tree grows, respectively. This fact, that objects in the world appear in different ways depending on the scale of ...
Robust fusion of irregularly sampled data using adaptive normalized convolution
 EURASIP Journal on Applied Signal Processing
, 2006
"... We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to ..."
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Cited by 28 (5 self)
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We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signaltonoise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of superresolution image reconstruction. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved. 1.
The Gaussian Derivative model for spatialtemporal vision
 I. Cortical Model. Spatial Vision
, 2001
"... Abstract—Receptive � elds of simple cells in the primate visual cortex were well � t in the space and time domains by the Gaussian Derivative (GD) model for spatiotemporal vision. All 23 � elds in the data sample could be � t by one equation, varying only a single shape number and nine geometric tr ..."
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Cited by 18 (0 self)
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Abstract—Receptive � elds of simple cells in the primate visual cortex were well � t in the space and time domains by the Gaussian Derivative (GD) model for spatiotemporal vision. All 23 � elds in the data sample could be � t by one equation, varying only a single shape number and nine geometric transformation parameters. A differenceofoffsetGaussians (DOOG) mechanism for the GD model also � t the data well. Other models tested did not � t the data as well as or as succinctly, or failed to converge on a unique solution, indicatingoverparameterization.An ef � cient computationalalgorithm was found for the GD model which produced robust estimates of the direction and speed of moving objects in real scenes. 1.
From information scaling of natural images to regimes of statistical models
 Quarterly of Applied Math
, 2008
"... 1 Computer vision can be considered a highly specialized data collection and data analysis problem. We need to understand the special properties of image data in order to construct statistical models for representing the wide variety of image patterns. One special property of vision that distinguish ..."
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Cited by 17 (7 self)
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1 Computer vision can be considered a highly specialized data collection and data analysis problem. We need to understand the special properties of image data in order to construct statistical models for representing the wide variety of image patterns. One special property of vision that distinguishes itself from other sensory data such as speech data is that distance or scale plays a profound role in image data. More specifically, visual objects and patterns can appear at a wide range of distances or scales, and the same visual pattern appearing at different distances or scales produces different image data with different statistical properties, thus entails different regimes of statistical models. In particular, we show that the entropy rate of the image data changes over the viewing distance (as well as the camera resolution). Moreover, the inferential uncertainty changes with viewing distance too. We call these changes information scaling. From this perspective, we examine both empirically and theoretically two prominent and yet largely isolated research themes in image modeling literature, namely, wavelet sparse coding and Markov random fields. Our results indicate that the two models are appropriate on two different entropy regimes: sparse coding targets the
Visual Feature Learning
, 2001
"... Humans learn robust and efficient strategies for visual tasks through interaction with their environment. In contrast, most current computer vision systems have no such learning capabilities. Motivated by insights from psychology and neurobiology, I combine machine learning and computer vision techn ..."
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Cited by 14 (3 self)
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Humans learn robust and efficient strategies for visual tasks through interaction with their environment. In contrast, most current computer vision systems have no such learning capabilities. Motivated by insights from psychology and neurobiology, I combine machine learning and computer vision techniques to develop algorithms for visual learning in openended tasks. Learning is incremental and makes only weak assumptions about the task environment. I begin
Probabilistic Estimation of Optical Flow in Multiple BandPass Directional Channels
, 2000
"... Bandpass directional filters are not normally used as prefilters for optical flow estimation because their orientation selectivity tends to increase the aperture problem. Despite this fact, here we obtain multiple estimates of the velocity by applying the classic gradient constraint to the outp ..."
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Cited by 12 (0 self)
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Bandpass directional filters are not normally used as prefilters for optical flow estimation because their orientation selectivity tends to increase the aperture problem. Despite this fact, here we obtain multiple estimates of the velocity by applying the classic gradient constraint to the output of each filter of a bank of 6 directional second order Gaussian derivatives at 3 spatial resolutions. We obtain estimates of the velocity and of its associate covariance matrix, which define a full probability density function (PDF) for the Gaussian case. We use this probabilistic representation to combine the resulting multiple velocity estimates, by first segmenting them in coherent motion processes, and then combining the estimates inside each coherent group assuming independence. Segmentation maintains the ability to represent multiple motions and helps to reject outliers so that the final estimates are robust, while combination helps to reduce the initial aperture problem. Res...
Linear SpatioTemporal ScaleSpace
 In Proc. ScaleSpace’97, Springer LNCS 1252
, 1997
"... This article presents a scalespace theory for spatiotemporal data. Starting from the main assumptions that (i) the scalespace should be generated by convolution with a semigroup of filter kernels and that (ii) local extrema must not be enhanced when the scale parameter increases, a complete taxo ..."
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Cited by 10 (5 self)
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This article presents a scalespace theory for spatiotemporal data. Starting from the main assumptions that (i) the scalespace should be generated by convolution with a semigroup of filter kernels and that (ii) local extrema must not be enhanced when the scale parameter increases, a complete taxonomy is given of the linear scalespace concepts that satisfy these conditions on spatial, temporal and spatiotemporal domains, including the cases with continuous as well as discrete data.