Licia Capodiferro

Two-Dimensional Approach to Full-Reference Image Quality Assessment Based on Positional Structural Information

A method for full-reference visual quality assessment based on the 2-D combination of two diverse metrics is described. The first metric is a measure of structural information loss based on the Fisher information about the position of the structures in the observed images. The second metric acts as a categorical indicator of the type of distortion that images underwent. These two metrics constitute the inner state of a virtual cognitive model, viewed as a system whose output is the automatic visual quality estimate. The use of a 2-D metric fills the intrinsic incompleteness of methods based on a single metric while providing consistent response across different image impairment factors and blind distortion classification capability with a modest computational overhead. The high accuracy and robustness of the method are demonstrated through cross-validation experiments.

Space-time Zernike moments and pyramid kernel descriptors for action classification

Action recognition in videos is a relevant and challenging task of automatic semantic video analysis. Most successful approaches exploit local space-time descriptors. These descriptors are usually carefully engineered in order to obtain feature invariance to photometric and geometric variations. The main drawback of space-time descriptors is high dimensionality and efficiency. In this paper we propose a novel descriptor based on 3D Zernike moments computed for space-time patches. Moments are by construction not redundant and therefore optimal for compactness. Given the hierarchical structure of our descriptor we propose a novel similarity procedure that exploits this structure comparing features as pyramids. The approach is tested on a public dataset and compared with state-of-the art descriptors.

Multi agent system for cooperative energy management in microgrids

In the last years the microgrid are emerged as the key component able to increase the efficiency, reliability, and sustainability of traditional electrical infrastructures. Micro distribution systems aggregate small, modular renewable power source, distributed storage and local loads as autonomous entities that can exchange power with the traditional electricity if operating in connected mode. A prime task in microgrid operation is the dynamic balance of local supply and power demand due to the intermittent nature of renewable energy resource and the variability of load demand during the day. However the power transfer among each microgrid and the main grid is always associated with a cost due to the loss of power over the distribution line. In this paper, a multi-agent systems (MAS) for the optimal coordination of multiple distributed energy resources is presented. The agents, associated with each microgrid, implement a cooperative strategy to minimise the power loss over the distribution lines and to maximise the economic income by sharing the surplus of the generated power between the microgrids belonging to the same coalition. The simulation results show the effectiveness of the proposed control strategy demonstrating that the MGs payoff increases up to 30% when microgrids cooperate to gain the power balance.

SVM for historical sport video classification

In this work the authors propose a classification method based on Support Vector Machine (SVM) and key frames features extraction to classify historical sport video contents. In the context of the Italian Project, IRMA (Information Retrieval in Multimedia Archives), with the goal to recover and preserve historical videos of proven cultural interest, a data set made up of several hours of videos from the 1960 Olympic games, provided by RAI and Teche RAI, is adopted as testbed. Each video is summarized by its key frames and represented by the features vectors computed in the Laguerre Gauss transformed domain. The high-level video classification starts from these vectors that are the input of the SVM classifier. The experimental results show the effectiveness of the proposed method.

Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence

Abstract. A new technique for texture segmentation is presented. The method is based on the use of Laguerre Gauss (LG) functions, which allow an efficient representation of textures. In particular, the marginal densities of the LG expansion coefficients are approximated by the generalized Gaussian densities, which are completely described by two parameters. The classification and the segmentation steps are performed by using a modified k-means algorithm exploiting the Kullback–Leibler divergence as similarity metric. This clustering method is a more efficient system for texture comparison, thus resulting in a more accurate segmentation. The effectiveness of the proposed method is evaluated by using mosaic image sets created by using the Brodatz dataset, and real images.

Smooth image inpainting by least square oriented edge prediction

This paper introduces a new spatial edge oriented algorithm for automatic digital inpainting. The approach is based on the Laguerre Gauss analysis of the structure information of the regions surrounding the damaged portions of the image, extrapolating in automatic way the gradient of the luminance and color in missing areas this estimation is made of a least square fitting algorithm from simplified edge lines that stood on the boundary of missing region. The reconstruction of the unknown parts is automatically obtained by a variational method that uses the predicted gradient information imposing smoothing constraints on luminance and color level. Experiments on a number of images show the effectiveness of the proposed algorithm in smooth areas, as well as in areas with edges and/or textured.

Textured areas detection and segmentation in circular harmonic functions domain

In this work a novel technique for detecting and segmenting textured areas in natural images is presented. The method is based on the circular harmonic function, and, in particular, on the Laguerre Gauss functions. The detection of the textured areas is performed by analyzing the mean, the mode, and the skewness of the marginal densities of the Laguerre Gauss coefficients. By using these parameters a classification of the patch and of the pixel, is performed. The feature vectors representing the textures are built using the parameters of the Generalized Gaussian Densities that approximate the marginal densities of the Laguerre Gauss functions computed at three different resolutions. The feature vectors are clustered by using the K-means algorithm in which the symmetric Kullback-Leibler distance is adopted. The experimental results, obtained by using a set of natural images, show the effectiveness of the proposed technique.

Impact of edges characterization on image clustering

In this work a novel technique for representing the edges of an image is presented and the impact of this on image clustering is investigated. The characterization is performed in two steps: the “most important” edges are first selected by using both the Laplace operator and the Laguerre Gauss functions, and then the phase distribution of each edge point is estimated. The similarity is measured by using the Euclidean distance. The query-by-example systems usually rank in the first positions objects very similar to the query. If many almost identical copies of the query object are present in the database, all of them are shown. However, some object that are interesting are not ranked in the first positions. To this aim a clustering method is used. This method is based on the low level features, such as edges, texture, and color.

Eye detection based on the polynomial Hermite expansion

In this contribution a procedure of eye detection and localization for biometric applications is presented. It is essentially based on the calculus of two polar moments whose values are employed for detection and localization of pupils. The polar moments are calculated using a bank of mono-dimensional Hermite filters whose outputs can be easily converted to give the local expansion of the analyzed image in the Gauss Laguerre polar separable family of functions.

Two-channel technique for high dynamic range image visualization

Advances in digital imaging technologies make increasingly available high quality images characterized by high dynamic range (HDR) of color components. Usual display devices are unable to visualize the information content of HDR images, especially in presence of light. For this reason special compression techniques for image rendering on conventional displays have been developed. In this work, a new technique based on simultaneous compression of low frequency radiance variations and detail enhancement is presented. In this work, a method working in the transform domain of an edge oriented wavelet transform is presented. Low-frequency components and high-resolution wavelet coefficients are separately manipulated before image reconstruction in order to reduce the overall dynamic range. The effectiveness of the technique is shown by means of significant examples.