Gianni Vernazza

Image stabilization algorithms for video-surveillance applications

An image stabilization algorithm is presented that is specifically oriented toward video-surveillance applications. The proposed approach is based on a novel motion-compensation method that is an adaptation of a well-known image-stabilization algorithm for visualization in video-surveillance applications. In particular, the illustrated methods take into account the specificity of typical video-surveillance applications, where objects moving in a scene often cover a large part of an image thus causing the failure of classic image-stabilization techniques. Evaluation methods for image stabilization algorithms are discussed.

Advanced Video-Based Surveillance Systems

From the Publisher: Advanced Video-Based Surveillance Systems presents second generation surveillance systems that automatically process large sets of signals for performance monitoring tasks. Included is coverage of different architecture designs, customization of surveillance architecture for end-users, advances in the processing of imaging sequences, security systems, sensors, and remote monitoring projects. Examples are provided of surveillance applications in highway traffic control, subway stations, wireless communications, and other areas. This work will be of interest to researchers in image processing, computer vision, digital signal processing, and telecommunications.

Detection of land-cover transitions by combining multidate classifiers

This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard strategies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian weighted average). Experiments, carried out on a multitemporal remote-sensing data set, confirm the effectiveness of the proposed system.

Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised Classification of Multitemporal Remote-Sensing Images

Abstract In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood classifier and a radial basis function neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system.

Unsupervised Change Detection From Multichannel SAR Images

Multichannel synthetic aperture radar (SAR) data present a good potential for environmental monitoring and disaster management, owing both to their insensitivity to atmospheric and sun-illumination conditions, and to the improved discrimination capability they may provide as compared to single-channel SAR. However, this requires accurate and possibly automatic techniques to generate change maps from multichannel SAR images acquired from the same geographic area at different times. In this letter, an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with a variant of the expectation-maximization algorithm and with Markov random fields. The method is validated by experiments on SIR-C/XSAR data

Nonlinear image labeling for multivalued segmentation

We describe a framework for multivalued segmentation and demonstrate that some of the problems affecting common region-based algorithms can be overcome by integrating statistical and topological methods in a nonlinear fashion. We address the sensitivity to parameter setting, the difficulty with handling global contextual information, and the dependence of results on analysis order and on initial conditions. We develop our method within a theoretical framework and resort to the definition of image segmentation as an estimation problem. We show that, thanks to an adaptive image scanning mechanism, there is no need of iterations to propagate a global context efficiently. The keyword multivalued refers to a result property, which spans over a set of solutions. The advantage is twofold: first, there is no necessity for setting a priori input thresholds; secondly, we are able to cope successfully with the problem of uncertainties in the signal model. To this end, we adopt a modified version of fuzzy connectedness, which proves particularly useful to account for densitometric and topological information simultaneously. The algorithm was tested on several synthetic and real images. The peculiarities of the method are assessed both qualitatively and quantitatively.

A distributed intelligence methodology for railway traffic control

A distributed approach to railway traffic control is described. The approach overcomes the upper bounds imposed on the size of controlled areas by the requirement for real-time processing when centralized methodologies are applied. The control problem is modeled in terms of resource allocation tasks, and the concept of priority is generalized to rule local control decisions. The analysis of a global networks behavior, as derived from the integration of local microdecisions, prefigures a depletion effect which will protect the system from traffic jam collapses. Simulation runs are reported to show the control systems overall operation. >

Model generation and model matching of real images by a fuzzy approach

Abstract A method based on fuzzy sets for model representation and matching of real images in a knowledge-based system is presented. The detailed descriptions of the systems models, data structures, and matching mechanism, as well as the introduction to a method for the generation of symbolic models, are the main topics of the present paper. Models and data structures are based on the use of fuzzy restrictions. The process of model generation starts from a set of training images whose features are analysed to find discriminant descriptions of single objects and their mutual relationships. As an example of application, the efficiency of this approach has been tested using medical tomographic images acquired by the magnetic resonance technique. Results demonstrate the applicability of one ideal model to various real scenes of the same type. The systems performance and the errors incurred are evaluated. The robustness of the model and of the method has been proved both by processing images affected by noise and by changing segmentation threshold values in the preprocessing step.

ASSIST: automatic system for surface inspection and sorting of tiles

The ceramic tiles manufacturing process has now been completely automated with the exception of the final stage of production concerned with visual inspection. In this paper we describe an integrated system developed for the detection of defects on colour ceramic tiles and for the colour grading of defect-free tiles. The results suggest that the performance is adequate to provide a basis for a viable commercial visual inspection system.

Enhanced GPSR Routing in Multi-Hop Vehicular Communications through Movement Awareness

Providing reliable and efficient routing in presence of relative movement motivates the introduction of movement awareness to improve performance of existing position-based routing schemes in vehicular ad-hoc networks. The proposed algorithm represents a modification of well-known GPSR which exploits information about movement in order to improve the next forwarding node decision. Performance evaluation of the proposed protocol underlines a promising and robust basis for designing a routing strategy suitable for the automotive scenario.