There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

/pdf/no-reference-image-quality-assessment-in-the-spatial-domain-2mnlvf25ig.pdf

No-Reference Image Quality Assessment in the Spatial Domain

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

/pdf/making-a-completely-blind-image-quality-analyzer-4rsald8tn3.pdf

Making a “Completely Blind” Image Quality Analyzer

On robust estimation of the location parameter

Cette communication traite de la caracterisation temporelle et frequentielle des fuseaux de sommeil (spindles), fondee sur un modele temporel sinusoidal stochastique. L'originalite du travail reside a la fois dans l'exploitation des methodes d'analyse spectrale haute resolution pour extraire les frequences actives du signal et l'utilisation du lissage de Kalman pour obtenir les parametres du modele sinusoidal temporel. Nous proposons en particulier de mener une analyse des evolutions temporelles des amplitudes des composantes actives du spindle a partir d'une approche de type EM (Expectation Maximization), pour une caracterisation fine temps frequence.

Caractérisation et analyse des fuseaux de sommeil par analyse spectrale haute résolution et filtrage de Kalman

Volume visualization using 2-D slices is a technique used in several scientific fields such as medicine, geology or any other industrial application using 3D data. The expert has the possibility to explore the data by displaying a set of slices extracted from the volume. The objective is to detect some specific three-dimensional structure and point it in 2-D, directly on slices with a device. This type of interaction realized during the animation of slices is named dynamic pointing. To formalize this task we define in this article a new paradigm: the dynamic steering task (DST). We first relate it with the other tasks studied in Human-Computer Interaction. This study helps us to better understand the reason of the difficulty of this task and why users produce so many positioning errors. We formulate the hypothesis that the errors generated in a DST are coming from the impossibility for the experts to anticipate the structural variations of the structures to be pointed. In order to solve this problem we propose a new technique of visualization, which facilitates anticipation, called the projective slice. The effectiveness of this tool and the veracity of our assertions are determined experimentally.

Coupe projective pour une tâche trajectorielle dynamique

Generalization of the shortest path approach for superpixel segmentation of omnidirectional images

This paper argues that a class of Riemannian metrics, called warped metrics, plays a fundamental role in statistical problems involving location-scale models. The paper reports three new results : i) the Rao-Fisher metric of any location-scale model is a warped metric, provided that this model satisfies a natural invariance condition, ii) the analytic expression of the sectional curvature of this metric, iii) the exact analytic solution of the geodesic equation of this metric. The paper applies these new results to several examples of interest, where it shows that warped metrics turn location-scale models into complete Riemannian manifolds of negative sectional curvature. This is a very suitable situation for developing algorithms which solve problems of classification and on-line estimation. Thus, by revealing the connection between warped metrics and location-scale models, the present paper paves the way to the introduction of new efficient statistical algorithms.

Warped metrics for location-scale models

This paper focuses on the estimation problem of typical parameters of the A/D converters. As basic assumption, the data output consists of a sum of real sinusoids. In this framework, we investigate the interest of the parametric spectral methods in the ADC testing. We give the detail of the three major estimation steps in order to retrieve all signal output parameters. Finally, we present the major advantage of this estimation scheme: the good results obtained in spite of the noncoherent frequency feature used in the test.

Yannick Berthoumieu

Papers

Caractérisation et analyse des fuseaux de sommeil par analyse spectrale haute résolution et filtrage de Kalman

Coupe projective pour une tâche trajectorielle dynamique

Generalization of the shortest path approach for superpixel segmentation of omnidirectional images

Warped metrics for location-scale models

Parametric spectral analysis tools for the characterization of A/D converters