Efficient Models and Algorithms for Image Processing for Industrial Applications

Image processing and computer vision are now part of our daily life and allow artificial intelligence systems to see and perceive the world with a visual system similar to the human one. In the quest to improve performance, computer vision algorithms reach remarkable computational complexities. The high computational complexity is mitigated by the availability of hardware capable of supporting these computational demands. However, high-performance hardware cannot always be relied upon when one wants to make the research product usable. In this work, we have focused on the development of computer vision algorithms and methods with low computational complexity but high performance. The first approach is to study the relationship between Fourier-based metrics and Wasserstein distances to propose alternative metrics to the latter, considerably reducing the time required to obtain comparable results. In the second case, instead, we start from an industrial problem and develop a deep learning model for change detection, obtaining state-of-the-art performance but reducing the computational complexity required by at least a third compared to the existing literature

The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems

We investigate properties of some extensions of a class of Fourier-based probability metrics, originally introduced to study convergence to equilibrium for the solution to the spatially homogeneous Boltzmann equation. At difference with the original one, the new Fourier-based metrics are well-defined also for probability distributions with different centers of mass, and for discrete probability measures supported over a regular grid. Among other properties, it is shown that, in the discrete setting, these new Fourier-based metrics are equivalent either to the Euclidean-Wasserstein distance $W_2$, or to the Kantorovich-Wasserstein distance $W_1$, with explicit constants of equivalence. Numerical results then show that in benchmark problems of image processing, Fourier metrics provide a better runtime with respect to Wasserstein ones.Comment: 18 pages, 2 figures, 1 tabl

The Fourier Discrepancy Function

In this paper, we introduce the p-Fourier Discrepancy Functions, a new family of metrics for comparing discrete probability measures, inspired by the χr-metrics. Unlike the χr-metrics, the p-Fourier Discrepancies are well-defined for any pair of measures. We prove that the p-Fourier Discrepancies are convex, twice differentiable, and that their gradient has an explicit formula. Moreover, we study the lower and upper tight bounds for the p-Fourier Discrepancies in terms of the Total Variation distance

Non-volatile resistive switching in nanoscaled elemental tellurium by vapor transport deposition on gold

Two-dimensional (2D) materials are highly promising as resistive switching materials for neuromorphic and in-memory computing owing to their fascinating properties derived from their low thickness. However, most of the reported 2D resistive switching materials struggle with complex growth methods or limited growth area. Tellurium, a novel member of single-element 2D materials, is showing pioneering characteristics such as simplicity in chemistry, structure, and synthesis which make it highly suitable for various applications. This study presents the first memristor design based on nanoscaled elemental tellurium synthesized by vapor transport deposition (VTD) method at a temperature as low as 100 °C in full compliance with a back-end-of-line (BEOL) processing. We demonstrate that the memristive behavior of nanoscaled tellurium can be enhanced by selecting gold as the substrate material which results in a lower set voltage and reduced energy consumption. In addition, the formation of conductive paths which in turn lead to resistive switching behavior on the gold substrate is proven to be driven by the gold-tellurium interface reconfiguration during the VTD process as revealed by energy electron loss spectroscopy analysis of the interface. Our findings reveal the potential of nanoscaled tellurium as a versatile and scalable material for neuromorphic computing systems as well as the influential role of gold as electrode material in enhancing tellurium’s memristive performance