This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean Opinion Scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes

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Our knowledge on tissue-and disease-specific functions of human genes is rather limited and highly context-specific. Here, we have developed a method for the comparison of mRNA expression levels of most human genes across 9,783 Affymetrix gene expression array experiments representing 43 normal human tissue types, 68 cancer types, and 64 other diseases. This database of gene expression patterns in normal human tissues and pathological conditions covers 113 million datapoints and is available from the GeneSapiens website.

Abstract-A method is decribed which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations.Index Terms-Acoustic signal analysis, music, musical meter analysis, music transcription.

We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain Þltering based on nonlocal block-matching. The considered methods are classiÞed mainly according to two main features: local/nonlocal and pointwise/multipoint. Here nonlocal is an alternative to local, and multipoint is an alternative to pointwise. These alternatives, though obvious simpliÞcations, allow to impose a fruitful and transparent classiÞcation of the basic ideas in the advanced techniques. Within this framework, we introduce a novel single-and multiplemodel transform domain nonlocal approach. The Block Matching and 3-D Filtering (BM3D) algorithm, which is currently one of the best performing denoising algorithms, is treated as a special case of the latter approach.

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The idea of local smoothing and local approximation is so natural that it is not surprising it has appeared in many branches of science. Citing [145] we mention early works in statistics using local polynomials by the Italian meteorologist Schiaparelli (1866) and the Danish actuary Gram (1879) (famous for developing the Gram-Schmidt procedure for the orthogonalization of vectors).In the 1960s and 1970s the idea became the subject of intensive theoretical study and applications, in statistics by Nadaraya [158] The local polynomial approximation as a tool appears in different modifications and under different names, such as moving (sliding, windowed) least square, Savitzky-Golay filter, reproducing kernels, and moment filters. We prefer the term LPA with a reference to publications on nonparametric estimation in mathematical statistics where the advanced development of this technique can be seen.In this chapter the discrete local approximation is presented in a general multivariate form. In the introductory section (Sec. 2.1), we discuss an observation model and multi-index notation for multivariate data, signals, and estimators. Section 2.2 starts with the basic ideas of the LPA presented initially for 2D signals typical for image processing. The window function, the order of the LPA model, and the scaling of the estimates are considered in detail. Further, the LPA is presented for the general multivariate case with estimates for smoothing and differentiation. Estimators for the derivatives of arbitrary orders are derived. Examples of 1D smoothers demonstrate that the scale (window size) parameter selection is of importance.The LPA estimates are given in kernel form in Sec. 2.3. The polynomial smoothness of the kernels and their properties are characterized by the vanishing moment conditions.The LPA can be treated as a design method for linear smoothing and differentiating filters. Links of the LPA with the nonparametric regression concepts are clarified in Sec. 2.4, and the LPA for interpolation is discussed briefly in Sec. 2.5.

Abstract-The problem of unmanned aerial vehicles classification using continuous wave radar is considered in this paper. Classification features are extracted from micro-Doppler signature. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with 9.5 GHz radar. Planes, quadrocopter, helicopters and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides capability of correct classification with a probability of around 95%.