Search results for " Informatica"
showing 10 items of 978 documents
Balancing and clustering of words: a combinatorial analysis of the Burrows & Wheeler Transform
2010
The Burrows-Wheeler Transform (denoted by BWT) is a well founded mathematical transformation on sequences introduced in 1994, widely used in the context of Data Compression and recently studied also from a combinatorial point of view. The transformation does not itself compress the data, but it produces a permutation bwt(w) of an input string w that is easier to compress than the original one, with some fast locally-adaptive algorithms, such as Move-to-Front in combination with Huffman or arithmetic coding. It is well-known that in most real texts, characters with the same or similar contexts tend to be the same. So, the BWT tends to group together characters which occur adjacent to similar…
A vision-based fully automated approach to robust image cropping detection
2020
Abstract The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content th…
Metrics, clustering and simulations to evaluate seismic signals
2014
This thesis presents an overview on seismic signals analysis and its related activities to clustering. The real applications require the use of metrics, algorithms and data to test hypothesis or to infer them. Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced by earthquakes and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of …
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
2020
Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good…
Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation
2021
OBJECTIVE: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardior…
A new heterogeneous and reconfigurable architecture for image analysis
1993
In the paper a new architecture for image analysis: HERMIA (Heterogeneous and Reconfigurable Machine for Image Analysis) is presented. It has bt:en developed at the University of Palermo, inside the Progetto Finalizzato of the ltalian Council of Researches (CNR): Sistemi informatici e Calcolo Parallelo. The architecture of the HERMIA-machine is reconfigurable, moreover the integration of heterogeneous module, oriented to the solution of specific problems, allows to salve complex problems by search of optimal strategies. Signa! processing units allows the user to handle and integrate multi-sensors signals (from video, scanner, music recorder). Here the generai architecture, the hardware impl…
Fuzziness, Cognition and Cybernetics: a historical perspective
2015
In the present paper, we connect some old reflections about the relationships existing between the theory of fuzzy sets and cybernetics with modern, contemporary analyses of the crucial (better: unavoidable) role that fuzziness plays in the attempts at scientifically describing aspects of information sciences. The connection, which has a basic conceptual origin, has been triggered also by the recent 50th anniversary of Norbert Wiener’s death which has been instrumental in looking again at some crucial aspects of the birth of information sciences in the midst of the last century. Fuzzy sets are an essential part of this revolution and share all the innovations as well as the difficulties of …
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps
2016
In this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database.