Blur Invariants for Image Recognition

Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not require any prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. It is shown that all blur invariants published earlier are just particular cases of this approach. Experimental comparison to concurrent approaches shows the advantages of the proposed theory.Comment: 15 page

Measurements, Models, Systems and Design

531 s.

Distortion estimates for adaptive lifting transforms with noise

Multimedia analysis, enhancement and coding methods often resort to adaptive transforms that exploit local characteristics of the input source. Following the signal decomposition stage, the produced transform coefficients and the adaptive transform parameters can be subject to quantization and/or data corruption (e.g. due to transmission or storage limitations). As a result, mismatches between the analysis- and synthesis-side transform coefficients and adaptive parameters may occur, severely impacting the reconstructed signal and therefore affecting the quality of the subsequent analysis, processing and display task. Hence, a thorough understanding of the quality degradation ensuing from such mismatches is essential for multimedia applications that rely on adaptive signal decompositions. This paper focuses on lifting-based adaptive transforms that represent a broad class of adaptive decompositions. By viewing the mismatches in the transform coefficients and the adaptive parameters as perturbations in the synthesis system, we derive analytic expressions for the expected reconstruction distortion. Our theoretical results are experimentally assessed using 1D adaptive decompositions and motion-adaptive temporal decompositions of video signals

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated with the signal of interest. The acquired data is then wirelessly transmitted to a central reconstruction point that aims at estimating the desired signal within a prescribed accuracy. Motivated by the obvious processing limitations inherent to such distributed infrastructures, we seek to find efficient compression schemes that account for limited available power and communication bandwidth. In this paper, we propose a transform-based approach to this problem where each sensor provides the central reconstruction point with a low-dimensional approximation of its local observation by means of a suitable linear transform. Under the mean-squared error criterion, we derive the optimal solution to apply at one sensor assuming all else being fixed. This naturally leads to an iterative algorithm whose optimality properties are exemplified using a simple though illustrative correlation model. The stationarity issue is also investigated. Under restrictive assumptions, we then provide an asymptotic distortion analysis, as the size of the observed vectors becomes large. Our derivation relies on a variation of the Toeplitz distribution theorem which allows to provide a reverse "water-filling" perspective to the problem of optimal dimensionality reduction. We illustrate, with a first-order Gauss-Markov model, how our findings allow to compute analytical closed-form distortion formulas that provide an accurate estimation of the reconstruction error obtained in the finite dimensional regime

Efficient compression of motion compensated residuals

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Wavelet-Based Registration of Medical Images.

Registration is the process of spatially aligning two objects and is normally a preprocessing step in most object recognition algorithms. Registration of images and recognition of signatures of objects in images is important for clinical and diagnostic purposes in medicine. Recognizing structure, potential targets for defense purposes and changes in the terrain, from aerial surveillance images and SAR images is the focus of extensive research and development today. Automatic Target Recognition is becoming increasingly important as the defense systems and armament technology move to use smarter munitions. Registration of images is a preprocessing step in any kind of machine vision for robots, object recognition in general, etc. Registration is also important for tuning instruments dealing with images. Most of the available methods of registration today are operator assisted. The state of registration today is more art than science and there are no standards for measuring or validating registration procedures. This dissertation provides a viable method to automatically register images of rigid bodies. It provides a method to register CT and MRI images of the brain. It uses wavelets to determine sharp edges. Wavelets are oscillatory functions with compact support. The Wavelet Modulus Maxima singularides. It also provides a mechanism to characterize the singularities in the images using Lipschitz exponents. This research provides a procedure to register images which is computationally efficient. The algorithms and techniques are general enough to be applicable to other application domains. The discussion in this dissertation includes an introduction to wavelets and time frequency analysis, results on MRI data, a discussion on the limitations, and certain requirements for the procedure to work. This dissertation also tracks the movement of edges across scales when a wavelet algorithm is used and provides a formula for this edge movement. As part of this research a registration classification schematic was developed

Elasto-magnetic waves in metamaterials: physics and modelling

The present work is framed in the wide topic of metamaterials. New elements portray this investigation with respect to what already existing in the current worldwide scenario. Indeed, the attention is focused on elastic metamaterials, with the idea to control wave propagation within the structure in terms of wave targeting, wave stopping and wave absorption. This is a novelty, since the concept of metamaterials is usually related to electromagnetic applications, for which all the uncommon effects, as for instance the invisible cloaking, are mainly related to the negative refraction index. A simple one-dimensional structure is analysed. More than the short-range elastic constitutive relationship, a nonlocal new long-range elastic material is here considered. This deeply affects and changes the topology of the system, leading to unexpected propagation phenomena. A mathematical model, based on the nonlocal elasticity theory of Eringen, is considered. The long-range interaction term appears as an integral function that induces a nonlinear characteristic to the conventional equation of motion. Special types of forces are chosen to model the long-range term, as they not only accurately model the elastic forces, but also lend themselves to analysis by Fourier transform. Closed form analytical solutions are achieved and it is the first time that such type of problem is so exhaustively examined. Indeed, long-range interactions have already been investigated, first by V.E. Tarasov, then by Zingales. However, Tarasov developed an analysis on the purely static response of structures, whilst this work discusses its dynamic behaviour, and Zingales performed only numerical solutions, which prevent a thorough understanding, and is mainly interested in modal analysis. The results are discussed in terms of modal analysis, dispersion relationship and propagation. It can be seen how the introduction of unconventional connections affects the typical behaviour of the structure and new phenomena, as hypersonic and superluminal propagation and negative group velocity arise. The analysis has been extended to a twin system, composed by two identical waveguides, with no structural coupling, but mutually coupled only through the long-range characteristic. An experimental campaign concludes the work. A twin waveguide system has been realized by 3D printing; several magnet holders and metallic strips acting as springs are used so to reproduce the mathematical model. The magnetic coupling recreates the long-range interaction. Different types of excitations have been applied to the primary waveguide, so to retrace first the complex frequency response and secondly the dispersion relationship. First results, even though rough, exhibit a damped response on the main waveguide, and a more complex response in the secondary waveguide, in agreement with what analytically observed

Heating Effects Through Harmonic Distortion on Electric Cables in the Built Environment

Under ideal circumstances, electric power supply voltage and current waveforms should be sinusoidal. However, this is very seldom the case in the built environment, due to the proliferation of non-linear loads. Examples of non-linear loads are those containing switched mode power supplies, reactors and electronic rectifiers/inverters. Common devices such as personal computers, fluorescent lighting, electric motors, variable speed drives, transformers and reactors and virtually all other electronic equipment are examples of non-linear loads. Non-linear loads are the norm in the built environment rather than the exception. Such loads produce complex current and voltage waves and simple spectral analysis of these complex waves shows that they can be represented by a wave at the fundamental power frequency plus other wave forms at integer and non-integer multiples of this frequency. These harmonics produce an overall effect called \u27Harmonic Distortion\u27 which can give rise to overheating in plant, equipment and the power cables supplying them, leading to reduced efficiency, operational life and sometimes failure. Over the last few decades, harmonic distortion in power supplies has increased significantly due to the increasing use of electronic components in industry and elsewhere. Buildings such as modern office blocks, commercial premises, factories, hospitals, etc.,contain equipment that generates harmonic loads as described above. Each item of equipment produces a unique harmonic signature and therefore a harmonic distortion which can be predicted if the equipment in use can be determined in advance. This thesis seeks to identify the harmonic signatures of different types of equipment commonly used and to predict the thermal loading effects on distribution cables caused by the skin and proximity effects of harmonic currents