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.

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

The PASCAL Visual Object Classes Challenge

Fringe projection techniques: Whither we are?

Unsupervised real-time anomaly detection for streaming data

Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30-40% of faults of induction motors are stator faults. This work presents a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology. It is aimed at providing a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors. A list of 183 research publications on the subject is appended for quick reference.

A review of stator fault monitoring techniques of induction motors

The evolution of energy demand and production can be faced with a Grid that becomes Smart: a network that is capable of integrating heterogeneous players in both generation and demand, allowing each of them to access electricity market and being able to react in real-time and autonomously to changing conditions on the grid. Such paradigm may allow to provide advanced services (i.e. Demand Response) increasing network efficiency and reliability. Necessary requisite for the implementation of a Smart Grid is having a metering architecture that can provide timely and reliable information regarding the state of the grid. The paper addresses the topic of an Advaced Metering Infrastructure (AMI), focusing on the first block, the Smart Meter. A meter prototype for the electric energy measurement, based on ARM architecture, is presented; the technical solutions, both in hardware and software, are described in detail; finally the results of tests carried out for its metrological characterization are reported.

Smart meters and demand response: A double challenge

The paper proposes a new approach to a decision making process based on face recognition algorithms. The proposed approach is based on the use of the uncertainty on the information used in the classification process to provide suitable level of confidence to the system output. As a case study, it is applied to a specific face recognition classification algorithm, namely the one based on Linear Discrimination Analysis, but it can be easily extended to other classification algorithms even dealing with other applications.

Face based recognition algorithms: The use of uncertainty in the classification

Volumetric Carotid Plaque Measurements Based on Ultrasound Images: A Preliminary Approach

Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few. In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty. In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.

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Metrological characterization of 3D biometric face recognition systems in actual operating conditions

The standard ISO 9612 describes an engineering method for measuring workers' exposure to noise in a work environment and for estimating the mean noise exposure level taking into account levels of uncertainty. In the paper, the authors propose a novel sampling strategy to automatically select the measurement episodes that are representative of a working activity. The bootstrap approach is proposed as a means to compute the measurement uncertainty of the A-weighted equivalent continuous sound pressure level associated with the work related task of interest. The proposal has been verified with real data acquired during a measurement campaign carried out from exposure to the acoustic noise of a milling machine.

Consolatina Liguori

Papers

Smart meters and demand response: A double challenge

Face based recognition algorithms: The use of uncertainty in the classification

Volumetric Carotid Plaque Measurements Based on Ultrasound Images: A Preliminary Approach

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

Task-based measurements for estimating the workers' exposure to acoustic noise