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ABSTRACT: We extend our previous work on Multilinear Independent Component Analysis (MICA) by introducing a Fast-MICA algorithm that demonstrates the same improvement over classical ICA as the original MICA algorithm [1] while improving the computational speed by two polynomial orders of magnitude. Apart for enabling a faster determination of the multilinear structure of image patch probability density, this new approach opens up, for the first time, the possibility of computing a novel non-stationarity index based on the relative change in mutual information. We demonstrate the performance of our Fast-MICA algorithm together with an illustration of our novel non-stationarity index.
Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
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ABSTRACT: We present information-theoretic underpinnings of a computation theory of low-level visual fixations in natural images. In continuation of our prior work on optimal contrast-based fixations [1], we develop an optimum texture- based fixation selection algorithm based on a recent theory of non-stationarity measurement in natural images [2]. Thereafter we propose a simple coupling of the optimal texture-based and contrast-based fixation features to produce a new algorithm called CONTEXT, which exhibits robust performance for fixation selection in natural images. The performance of the fixation algorithms are evaluated for natural images by comparison to randomized fixation strategies via actual human fixations performed on the images. The fixation patterns obtained outperform randomized, GAFFE-based [3], and Itti [4] fixation strategies in terms of matching human fixation patterns. These results also demonstrate the important role that contrast and textural information play in low-level visual processes in the Human Visual System (HVS).
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on; 11/2008
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ABSTRACT: We refine the classical independent component analysis (ICA) decomposition using a multilinear expansion of the probability density function of the source statistics. In particular, we introduce a specific nonlinear system that allows us to elegantly capture the statistical dependences between the responses of the multilinear ICA (MICA) filters. The resulting multilinear probability density is analytically tractable and does not require Monte Carlo simulations to estimate the model parameters. We demonstrate the MICA model on natural image textures and envision that the new model will prove useful for analyzing nonstationarity natural images using natural scene statistics models.
IEEE Transactions on Image Processing 04/2008; · 3.04 Impact Factor
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ABSTRACT: We refine the classical independent component analysis (ICA) decomposition using a multilinear expansion of the probability density function of the source statistics. In particular, to model the source statistics of natural image textures, we introduce a specific non-linear system that allows us to elegantly capture the statistical dependences between the responses of the multilinear ICA (MICA) filters. The resulting multilinear probability density is analytically tractable and does not require Monte Carlo simulations to estimate the model parameters. We demonstrate the success of the MICA model on natural textures and discuss applications to non-stationarity detection and natural scene statistics (NSS) modeling
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on; 05/2007 · 4.63 Impact Factor
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ABSTRACT: In this paper we address the problem of visual surveillance, which we define as the problem of optimally extracting information from the visual scene with a fixating, foveated imaging system. We are explicitly concerned with eye/camera movement strategies that result in maximizing information extraction from the visual field. Here we demonstrate how a novel characterization of the contrast statistics of natural images can be used for selecting fixation points that minimize the total contrast uncertainty (entropy) of natural images. We demonstrate the performance of the algorithm and compare its performance to ground truth methods. The results show that our algorithm performs favorably in terms of both efficiency and its ability to find salient features in the image.
Image Processing, 2005. ICIP 2005. IEEE International Conference on; 10/2005
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ABSTRACT: We develop theorems that place limits on the point-wise approximation of the responses of filters, both linear shift invariant (LSI) and linear shift variant (LSV), to input signals and images that are LSV in the following sense: they can be expressed as the outputs of systems with LSV impulse responses, where the shift variance is with respect to the filter scale of a single-prototype filter. The approximations take the form of LSI approximations to the responses. We develop tight bounds on the approximation errors expressed in terms of filter durations and derivative (Sobolev) norms. Finally, we find application of the developed theory to defoveation of images, deblurring of shift-variant blurs, and shift-variant edge detection.
IEEE Transactions on Image Processing 02/2005; · 3.04 Impact Factor
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ABSTRACT: We present a novel approach for non-stationarity detection in natural images by exploiting the prior knowledge of the independent component structure of scene statistics. Our proposed non-stationarity index is conceptually simple and is intertwined with the probabilistic structure of the image segment being analyzed. It shows consistently good results when applied to natural scenes and, we expect, will find useful applications in computer vision algorithms in as much as the detection of statistically non-stationary locations in images can be an important preliminary step toward the understanding of scene content and in the guiding of visual fixations.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
