Giovanni Corsini

Bio: Giovanni Corsini is an academic researcher from University of Pisa. The author has contributed to research in topics: Hyperspectral imaging & Dopaminergic. The author has an hindex of 38, co-authored 297 publications receiving 4968 citations. Previous affiliations of Giovanni Corsini include National Research Council.

A tutorial overview of anomaly detection in hyperspectral images

Abstract: In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.

Autofocusing of inverse synthetic aperture radar images using contrast optimization

Abstract: A number of autofocusing techniques are proposed for motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects These techniques are based on the optimization of a contrast function, which represents a measure of the degree of focus of the image Three novel image contrast functions are defined and the main steps of the autofocusing algorithm are described A comparison of the behavior of the proposed techniques is performed analyzing some results obtained from simulation of realistic targets

Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images

Abstract: In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is proposed. Noise is described using a parametric model that accounts for the dependence of noise variance on the useful signal. Such model takes into account the photon noise contribution and is therefore suitable for noise characterization in the data acquired by new-generation HS sensors where electronic noise is not dominant. A new algorithm is developed for the estimation of noise parameters which consists of two steps. First, the noise and signal realizations are extracted from the original image by resorting to the multiple-linear-regression-based approach. Then, the model parameters are estimated by using a maximum likelihood approach. The new method does not require the intervention of a human operator and the selection of homogeneous regions in the scene. The performance of the new technique is analyzed on simulated HS data. Results on real data are also presented and discussed. Images acquired with a new-generation HS camera are analyzed to give an experimental evidence of the dependence of random noise on the signal level and to show the results of the estimation algorithm. The algorithm is also applied to a well-known Airborne Visible/Infrared Imaging Spectrometer data set in order to show its effectiveness when noise is dominated by the signal-independent term.

MK-801 prevents 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced parkinsonism in primates.

Abstract: In cynomologus monkeys, systemic administration of MK-801, a noncompetitive antagonist for the N-methyl-D-aspartate receptor, prevented the development of the parkinsonian syndrome induced by the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). MK-801 also attenuated dopamine depletion in the caudate and putamen and protected dopaminergic neurons in the substantia nigra from the degeneration induced by the neurotoxin. Nevertheless, 7 days after MPTP administration in the caudate and putamen of monkeys also receiving MK-801, the levels of toxic 1-methyl-4-phenylpyridinium were even higher than those measured in monkeys receiving MPTP alone. This indicates that the protective action of MK-801 is not related to MPTP metabolism and strongly suggests that, in primates, the excitatory amino acids could play a crucial role in the mechanism of the selective neuronal death induced by MPTP.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Abstract: Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

Aging, energy, and oxidative stress in neurodegenerative diseases.

Abstract: The etiology of neurodegenerative diseases remains enigmatic; however, evidence for defects in energy metabolism, excitotoxicity, and for oxidative damage is increasingly compelling. It is likely that there is a complex interplay between these mechanisms. A defect in energy metabolism may lead to neuronal depolarization, activation of N-methyl-D-aspartate excitatory amino acid receptors, and increases in intracellular calcium, which are buffered by mitochondria. Mitochondria are the major intracellular source of free radicals, and increased mitochondrial calcium concentrations enhance free radical generation. Mitochondrial DNA is particularly susceptible to oxidative stress, and there is evidence of age-dependent damage and deterioration of respiratory enzyme activities with normal aging. This may contribute to the delayed onset and age dependence of neurodegenerative diseases. There is evidence for increased oxidative damage to macromolecules in amyotrophic lateral sclerosis, Huntington's disease, Parkinson's disease, and Alzheimer's disease. Potential therapeutic approaches include glutamate release inhibitors, excitatory amino acid antagonists, strategies to improve mitochondrial function, free radical scavengers, and trophic factors. All of these approaches appear promising in experimental studies and are now being applied to human studies.