Eduardo A. B. da Silva

TL;DR: This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms and proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.

TL;DR: In this paper, the authors cover all the major topics in digital signal processing (DSP) design and analysis, supported by MATLAB examples and other modeling techniques, and explain clearly and concisely why and how to use DSP systems; how to approximate a desired transfer function characteristic using polynomials and ratios of polynomial coefficients; why an appropriate mapping of a transfer function onto a suitable structure is important for practical applications.

TL;DR: This work provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages, and provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets.

TL;DR: This paper proposes a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features, and designs a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure.

TL;DR: The main compression techniques devised for electric signal waveforms are reviewed providing an overview of the achievements obtained in the past decades and some smart grid scenarios emphasizing open research issues regarding compression of electric signalWaveforms are envisioned.