This paper presents a novel monitoring architecture addressed to the cloud provider and the cloud consumers. This architecture offers a monitoring platform-as-a-Service to each cloud consumer that allows to customize the monitoring metrics. The cloud provider sees a complete overview of the infrastructure whereas the cloud consumer sees automatically her cloud resources and can define other resources or services to be monitored. This is accomplished by means of an adaptive distributed monitoring architecture automatically deployed in the cloud infrastructure. This architecture has been implemented and released under GPL license to the community as “MonPaaS”, open source software for integrating Nagios and OpenStack. An intensive empirical evaluation of performance and scalability have been done using a real deployment of a cloud computing infrastructure in which more than 3,700 VMs have been executed.

Here we propose an alternative non-explicit way to take into account the relations among wavelet coefficients in natural images for denoising: we use support vector machines (SVM) to learn these relations. Since relations among the coefficients are specific to the signal, SVM regularization removes the noise, which does not share this property. Moreover, due to its non-parametric nature, the method can eventually cope with different noise sources. The results show that: (1) the proposed non-parametric method outperforms conventional methods that assume coefficient independence, and (2) its performance is similar to state-of-the-art parametric methods that do explicitly include these relations. Therefore, the proposed machine learning approach can be seen as a more flexible (model-free) alternative to the explicit description of wavelet coefficient relations in Bayesian approaches.

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.

Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding using an appropriate distortion criterion [3], [4] based on a simple visual cortex model. Training the SVM by using an accurate perception model avoids any a priori assumption and improves the rate-distortion performance of the original approach.

Regularization constraints are necessary in inverse problems such as image restoration, optical flow computation or shape from shading to avoid the singularities in the solution. Conventional regularization techniques are based on some a priori knowledge of the solution: usually, the solution is assumed to be smooth according to simple statistical image or motion models. Using the fact that human visual perception is adapted to the statistics of natural images and sequences, the class of regularization functionals proposed in this work are not based on an image model but on a model of the human visual system. In particular, the current nonlinear model of early human visual processing is used to obtain locally adaptive regularization functionals for image restoration without any a priori assumption on the image or the noise. The results show that these functionals constitute a valid alternative to those based on the local autocorrelation of the image.

In this paper, a multigrid motion compensation video coder based on the current human visual system (HVS) contrast discrimination models is proposed. A novel procedure for the encoding of the prediction errors has been used. This procedure restricts the maximum perceptual distortion in each transform coefficient. This subjective redundancy removal procedure includes the amplitude nonlinearities and some temporal features of human perception. A perceptually weighted control of the adaptive motion estimation algorithm has also been derived from this model. Perceptual feedback in motion estimation ensures a perceptual balance between the motion estimation effort and the redundancy removal process. The results show that this feedback induces a scale-dependent refinement strategy that gives rise to more robust and meaningful motion estimation, which may facilitate higher level sequence interpretation. Perceptually meaningful distortion measures and the reconstructed frames show the subjective improvements of the proposed scheme versus an H.263 scheme with unweighted motion estimation and MPEG-like quantization.

Fovea segmentation in fluorescein angiographies is a fundamental first task in any study of ocular diseases. The importance of fovea detection is due to the fact that the nearer the centre of the fovea a lesion is, the graver this lesion is. The proposed method is based on B-snakes and uses a greedy algorithm to minimise an appropriate energy which accurately leads to a convenient characterisation of the boundary of the foveal zone. The first initialisation step, which consists of finding the most appropriate local minimum along with a procedure to construct an initial contour involving a region growing algorithm, leads to a convenient and robust initialisation of the proposed active contour model.