Images synthesized using depth-image-based-rendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important low-order structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-the-art metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.

Free-viewpoint video, as the development direction of the next-generation video technologies, uses the depth-image-based rendering (DIBR) technique for the synthesis of video sequences at viewpoints, where real captured videos are missing. As reference videos at multiple viewpoints are not available, a blind reliable real-time quality metric of the synthesized video is needed. Although no-reference quality metrics dedicated to synthesized views successfully evaluate synthesized images, they are not that effective when evaluating synthesized video due to additional temporal flicker distortion typical only for video. In this paper, a new fast no-reference quality metric of synthesized video with synthesis distortions is proposed. It is guided by the fact that the DIBR-synthesized images are characterized by increased high frequency content. The metric is designed under the assumption that the perceived quality of DIBR-synthesized video can be estimated by quantifying the selected areas in the high-high wavelet subband. The threshold is used to select the most important distortion sensitive regions. The proposed No-Reference Morphological Wavelet with Threshold (NR_MWT) metric is computationally extremely efficient, comparable to PSNR, as the morphological wavelet transformation uses very short filters and only integer arithmetic. It is completely blind, without using machine learning techniques. Tested on the publicly available dataset of synthesized video sequences characterized by synthesis distortions, the metric achieves better performances and higher computational efficiency than the state-of-the-art metrics dedicated to DIBR-synthesized images and videos.

In this paper a no-reference image quality assessment (IQA) metric for DIBR-synthesized images is proposed. Sparsity based features of morphologically decomposed image subbands are used to estimate distortion level in images. A General regression neural network is utilized to calculate quality score. The performance is evaluated using publicly available IRCCyN/IVC DIBR image database. Experimental results show that proposed metric accords with human subjective judgment.

In this paper, two image quality metrics based on morphological multiscale decompositions have been applied for the evaluation of free viewpoint video sequences. These sequences are synthesized using decompressed depth maps in the Depth-Image-Based Rendering synthesis process. Since the synthesis introduces edge distortion in the synthesized image/video, morphological filters are used for their ability to maintain important geometric information (i.e., edges) across different resolution levels. The edges displacement in different resolution scales is evaluated by means of the Mean Square Error. The adopted metrics show higher correlation with human judgment than state-of-the-art image quality measures used in this context.

Financial option prices have experienced excessive volatility in response to the recent economic and financial crisis. During the crisis periods, financial markets are, in general, subject to an abrupt regime shift which imposes a significant challenge to option pricing models. In this context, swiftly evolving markets and institutions require valuation models that are capable of recognizing and adapting to such changes. Both parametric and non-parametric pricing models have shown poor forecast ability for options traded in late 1987 and 2008. Surprisingly, the pricing inaccuracy was more pronounced for non-parametric models than for parametric models. To address this problem, we propose a novel hybrid methodology - modular neural network-fuzzy learning vector quantization (MNN-FLVQ) model - that uses the Kohonen unsupervised learning and fuzzy clustering algorithms to classify the S&P 500 stock market index options, and thereby detect a regime shift. In our empirical application, the results for the 2008 financial crisis demonstrate that the MNN-FLVQ model is superior to the competing methods in regards to option pricing during regime shifts.

Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion affects both human attention and coding quality, this relationship has only recently started gaining attention among the research community, when video quality assessment (VQA) is concerned. In this paper, the effect of calculating several objective measure features, related to video coding artifacts, separately for salient motion and other regions of the frames of the sequence is examined. In addition, we propose a new scheme for quality assessment of coded video streams, which takes into account salient motion. Standardized procedure has been used to calculate the Mean Opinion Score (MOS), based on experiments conducted with a group of non-expert observers viewing standard definition (SD) sequences. MOS measurements were taken for nine different SD sequences, coded using MPEG-2 at five different bit-rates. Eighteen different published approaches related to measuring the amount of coding artifacts objectively on a single-frame basis were implemented. Additional features describing the intensity of salient motion in the frames, as well as the intensity of coding artifacts in the salient motion regions were proposed. Automatic feature selection was performed to determine the subset of features most correlated to video quality. The results show that salient-motion-related features enhance prediction and indicate that the presence of blocking effect artifacts and blurring in the salient regions and variance and intensity of temporal changes in non-salient regions influence the perceived video quality.

This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint).

A new approach, designed for clustering of arbitrary distributed patterns, is presented. This study is concerned with the use of a self-organizing neural network as a frame for data clustering. The nearest network nodes in feature space are treated as prototypes, assigned to the corresponding cluster. The rules for dead-node shifting and simple adjustment of coordinates of the active nodes are introduced. The performance of the proposed self-organizing neural network is examined on the benchmark synthetic and the real-world problem.

The paper describes a neuro-fuzzy identification approach, which uses numerical data as a starting point. The proposed method generates a Takagi-Sugeno fuzzy model, characterized with transparency, high accuracy and a small number of rules. The process of self-organizing of the identification model consists of three phases: clustering of the input-output space using a self-organized neural network; determination of the parameters of the consequent part of a rule from over-determined batch least-squares formulation of the problem, using singular value decomposition algorithm; and on-line adaptation of these parameters using recursive least-squares method. The verification of the proposed identification approach is provided using four different problems: two benchmark identification problems, speed estimation for a dc motor drive, and estimation of the temperature in a tunnel furnace for clay baking.

This paper presents the results of the research concerning possibilities of applying artificial neural networks for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the feedforward neural networks so applied was considered. The emphasis was put on neural network's function as state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of neural networks were analyzed.