Guido M. Cortelazzo

A noise-robust frequency domain technique for estimating planar roto-translations

This work presents a new method for estimating planar roto-translations that operates in the frequency domain and as such, is not based on features. Since the proposed technique uses all the image information, it is very robust against noise, it can be very accurate; estimation errors on the rotational angle varies from a few hundredths to a few tenths of a degree, depending on the noise level. Experimental evidence of this performance is presented, and the mathematical reasons behind these characteristics are explained in depth. Another remarkable feature of the algorithm consists in that it works in Cartesian coordinates, bypassing the need to transform from the Cartesian to the polar domain, which, typically, is a numerically delicate and computationally onerous task. The proposed technique can become an effective tool for unsupervised estimation of roto-translations by means of implementations based on FFT algorithms.

Simultaneous design in both magnitude and group-delay of IIR and FIR filters based on multiple criterion optimization

This work considers the simultaneous design in both magnitude and group-delay of digital transfer functions on the basis of multiple criterion optimization. Both causal IIR filters and nonlinear phase FIR filters are studied. Examples of the optimal tradeoff filters, both FIR filters and IIR filters, are presented and their characteristics are analyzed.

Time-of-Flight Cameras and Microsoft Kinect(TM)

Time-of-Flight Cameras and Microsoft Kinect closely examines the technology and general characteristics of time-of-flight range cameras, and outlines the best methods for maximizing the data captured by these devices. This book also analyzes the calibration issues that some end-users may face when using these type of cameras for research, and suggests methods for improving the real-time 3D reconstruction of dynamic and static scenes. Time-of-Flight Cameras and Microsoft Kinect is intended for researchers and advanced-level students as a reference guide for time-of-flight cameras.Practitioners working in a related field will also find the book valuable.

Automatic 3D modeling of textured cultural heritage objects

A widespread use of three-dimensional (3D) models in cultural heritage application requires low cost equipment and technically simple modeling procedures. In this context, methods for automatic 3D modeling of textured objects can play a central role. Such methods need fully automatic techniques for 3D view registration and for the removal of texture artifacts. The paper proposes an image processing based procedure that is very robust and simple. It does not require special equipment or skill in order to make textured 3D models. These proposals, originally conceived to address the cost issues of cultural heritage modeling, can be profitably exploited also in other modeling applications.

3D registration by textured spin-images

This work is motivated by the desire of exploiting for 3D registration purposes the photometric information current range cameras typically associate to range data. Automatic pairwise 3D registration procedures are two steps procedures with the first step performing an automatic crude estimate of the rigid motion parameters and the second step refining them by the ICP algorithm or some of its variations. Methods for efficiently implementing the first crude automatic estimate are still an open research area. Spin-images are a 3D matching technique very effective in this task. Since spin-images solely exploit geometry information it appears natural to extend their original definition to include texture information. Such an operation can clearly be made in many ways. This work introduces one particular extension of spin-images, called textured spin-images, and demonstrates its performance for 3D registration. It will be seen that textured spin-images enjoy remarkable properties since they can give rigid motion estimates more robust, more precise, more resilient to noise than standard spin-images at a lower computational cost.

Fusion of Geometry and Color Information for Scene Segmentation

Scene segmentation is a well-known problem in computer vision traditionally tackled by exploiting only the color information from a single scene view. Recent hardware and software developments allow to estimate in real-time scene geometry and open the way for new scene segmentation approaches based on the fusion of both color and depth data. This paper follows this rationale and proposes a novel segmentation scheme where multidimensional vectors are used to jointly represent color and depth data and normalized cuts spectral clustering is applied to them in order to segment the scene. The critical issue of how to balance the two sources of information is solved by an automatic procedure based on an unsupervised metric for the segmentation quality. An extension of the proposed approach based on the exploitation of both images in stereo vision systems is also proposed. Different acquisition setups, like time-of-flight cameras, the Microsoft Kinect device and stereo vision systems have been used for the experimental validation. A comparison of the effectiveness of the different depth imaging systems for segmentation purposes is also presented. Experimental results show how the proposed algorithm outperforms scene segmentation algorithms based on geometry or color data alone and also other approaches that exploit both clues.

A System for 3D Modeling Frescoed Historical Buildings with Multispectral Texture Information

This work proposes a system for the automatic construction of multi-spectral three-dimensional (3D) models of architecture. Besides the specific application, which concerns the interactive visualization and the restoration of historical buildings, the interest of the proposed system lies in the instrumental gap it fills in the multi-spectral nature of the textures, in general needed for rendering with faithful colors, and in the automatism of the 3D model construction. The paper presents a robust procedure for matching 3D points of architecture scenes and a new multiresolution method for texture generation. The proposed system is an effective tool for producing 3D content amenable to a great number of usages.

Multistage sampling structure conversion of video signals

The work extends multistage implementation of sampling structure conversion to the multidimensional (M-D) case. The issues arising in this task are usefully addressed on the basis of lattice theory. Numerical data supporting the advantages of multistage sampling conversion are presented, and the case of format conversion from the 4/3 to the 16/9 aspect ratio is examined as a study case. The main indication of the present work is that multistage implementation, in the case of systems for sampling structure conversion of video signals, may improve the system characteristics and visual rendition. >

Tools for designing chaotic systems for secure random number generation

It is well known that the output of chaotic systems can be completely predicted from the exert knowledge of the initial conditions. However, their extreme sensitivity to initial conditions lends itself to being exploited for generation of random numbers. This work explores this possibility and gives a simple circuit arrangement, together with the tools necessary to assess the random characteristics of its output. The fact that the statistical characteristics of a chaotic system can be determined through spectral analysis of an evolution operator is shown. Numerical techniques for practical estimation of this operator are presented. Special attention is paid to robustness both with respect to numerical approximation and circuit tolerances. Error bounds of practical significance are given. One example of the proposed method and results is given. The results presented are valid for generic sampled chaotic systems and can also be used for applications other than random number generation, e.g., chaotic communications.

Statistical characteristics of granular camera noise

Granular camera noise is per se objectionable in high-quality TV and it is a source of bothersome visual artifacts with digitally coded video sequences. The first and second order statistical characteristics of this noise are investigated in this work. The results quantify intuitively expected notions, such as the relative unimportance of chrominance noise with respect to the luminance noise and the spatially colored nature of the noise. Noise reduction techniques can use, as general guidance rules, the indications of the presented analysis. >