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Dive into the research topics where Roberta Piroddi is active.

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Featured researches published by Roberta Piroddi.


Advances in Imaging and Electron Physics | 2004

Analysis of Irregularly Sampled Data: A Review

Roberta Piroddi; Maria Petrou

Publisher Summary This chapter is reviews the work directed by the scientific community into the efficient solution of the irregular sampling problem and discusses the principal application areas that motivated the interest into irregular sampling. It also presents a survey of the available noniterative techniques, with some details about the underlying theory and interpretation and discusses the iterative methods that are reviewed. An interesting mathematical framework for the analysis of irregular data—namely, the normalized convolution—which takes into account the certainty of the data is also presented, along with a comparative evaluation of the various methods. In addition, a survey of the latest developments in this area of research is presented. Areas such as medical image processing or remote sensing would benefit from a unified framework of analysis.


Computer Vision and Image Understanding | 2006

Texture recognition from sparsely and irregularly sampled data

Maria Petrou; Roberta Piroddi; A. Talebpour

In this paper, we present methodology for recognising textures from irregularly sampled data. We use features constructed from the trace transform, which represents images with functional values along tracing lines rather than brightness values at sampling points. Once texture classification may be performed using line, as opposed to point representations, there is no problem about using irregularly sampled data. The analysis is performed using tracing lines identified by the Hough transform. The results obtained are compared with the results obtained by performing texture classification using samples on the conventional regular grid.


The Computer Journal | 2010

Networks of Concepts and Ideas

Maria Petrou; Marco Elio Tabacchi; Roberta Piroddi

We present the results of an experiment designed to investigate the way information is organized and stored in the human brain. In particular, we are using controlled stimuli to reverse engineer the networks of ideas and concepts in order to answer the following questions. (1) Are the networks of ideas and concepts in the human brain invoked by verbal and visual stimuli distinct from each other? The answer appears to be no for the network of ideas and inconclusive for the network of concepts. (2) What is the topology of these networks? Our experimental results show that both are small-world networks, with the network of ideas being random and the network of concepts scale-free.


Remote Sensing | 2004

Irregularly sampled scenes

Maria Petrou; Roberta Piroddi; Sunil Chandra

In this paper, we present a review of some commonly used methods for signal interpolation and/or estimation, from a set of randomly chosen samples. Most of these methods were originally devised for 1D signals. First we extend these methods to 2D and then perform a comparative study. Our experimental results show good interpolation/reconstruction performances of some methods for sampling ratios as small as 5% of the original number of pixels.


british machine vision conference | 2002

Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach

Roberta Piroddi; Theodore Vlachos

In this papera noveltwo-stage architecturefor object-basedsegmentation of moving sequences is proposed using multiple features such as motion, intensity and texture. The first stage locates perceptually meaningful objects using a hierarchyof single-feature segmentationprocesses. The second stage refines the boundaries of located objects using a combination of features according to a set of appropriate rules. Experimental results show that the proposedapproachyields intuitively correctas well as accurate segmentationsof moving sequences, which compare favourably with established state-of-the art techniques in the literature.


IEEE Signal Processing Letters | 2006

A simple framework for spatio-temporal video segmentation and delayering using dense motion fields

Roberta Piroddi; Theodore Vlachos

To represent object movement, the conventional approach is to embed the motion information into parametric and/or statistical motion models. However, the inherent complexity of this description is ill suited for application requiring rapid and automatic, albeit significant, responses. Surveillance applications belong to this class and are emerging as the most active field of research in computer vision. For this purpose, we observe that the differential invariants obtained by dense optic flow are capable of accurately describing complex object motion without requiring setting up and initializing models. In this letter, we demonstrate the novel use of such motion descriptors for spatio-temporal object segmentation and delayering of sequences. Our results show the ability of this approach to describe simply and accurately differently moving objects and to be incorporated in segmentation processes that deliver a hierarchical description of object, producing evident improvements to the segmented objects while being computationally efficient


iberian conference on pattern recognition and image analysis | 2005

Texture interpolation using ordinary kriging

Sunil Chandra; Maria Petrou; Roberta Piroddi

We present a survey of the application of ordinary Kriging to texture interpolation using a variety of models that have been proposed to model the variogram of the image. The novelty of our approach is in the fully automated process of fitting the models to the data over a finite range of values.


international conference on digital signal processing | 2002

Object-based segmentation of moving sequences using multiple features

Roberta Piroddi; Theodore Vlachos

In this paper a novel two-stage architecture for object-based segmentation of moving sequences is proposed using multiple features such as motion, intensity and texture. The first stage locates perceptually meaningful objects using a hierarchy of single-feature segmentation processes. The second stage refines the boundaries of located objects using a suitable combination of features and a set of appropriate rules. Experimental results show that the proposed approach yields intuitively correct as well as accurate segmentations of moving sequences.


EURASIP Journal on Advances in Signal Processing | 2006

A method for single-stimulus quality assessment of segmented video

Roberta Piroddi; Theodore Vlachos

We present a unified method for single-stimulus quality assessment of segmented video. This method takes into consideration colour and motion features of a moving sequence and monitors their changes across segment boundaries. Features are estimated using a local neighbourhood which preserves the topological integrity of segment boundaries. Furthermore the proposed method addresses the problem of unreliable and/or unavailable feature estimates by applying normalized differential convolution (NDC). Our experimental results suggest that the proposed method outperforms competing methods in terms of sensitivity as well as noise immunity for a variety of standard test sequences.


IEEE Signal Processing Letters | 2008

Gradient-Adaptive Normalized Convolution

Vasileios Argyriou; Theodore Vlachos; Roberta Piroddi

Signal estimation for sparsely and irregularly sampled signals can be carried out using either noniterative methods, or iterative methods or methods that deal with irregular samples and their uncertainty, through normalized convolution. The latter is a general method for filtering incomplete or uncertain data and is based on the separation of both data and operator into a signal part and a certainty part. It has been proven that normalized convolution yields a local description which is optimal both in an algebraic and a least-squares sense. In this letter, we employ the normalized convolution concept to formulate a novel reconstruction method for irregularly sampled signals, utilizing an anisotropic, rotated applicability filter. Our experimental results demonstrate performance gains in a least-squares sense, retaining edge and contour information, especially in sparsely sampled areas on the image plane.

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Maria Petrou

Imperial College London

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