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Renato J. Cintra
Researcher at Federal University of Pernambuco
Publications - 157
Citations - 2739
Renato J. Cintra is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Discrete cosine transform & Image compression. The author has an hindex of 25, co-authored 150 publications receiving 2284 citations. Previous affiliations of Renato J. Cintra include University of Akron & University of Lyon.
Papers
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Journal ArticleDOI
Bias Correction and Modified Profile Likelihood under the Wishart Complex Distribution
TL;DR: Improved methods for the maximum likelihood (ML) estimation of the equivalent number of looks L are proposed, and the second-order bias expression proposed by Cox and Snell for the ML estimator of this parameter is presented.
Posted Content
On a Density for Sets of Integers
TL;DR: In this paper, a relationship between the Riemann zeta function and a density on integer sets is explored, and several properties of the examined density are derived, such as the properties of a given density on an integer set on a set.
Proceedings ArticleDOI
Digital architecture for real-time CNN-based face detection for video processing
TL;DR: A hardware computing architecture for face detection that classifies an image as a face or non-face and takes the form of a deep convolutional neural network which can classify if a search window inside a picture contains a human face or not.
Journal ArticleDOI
Improved Point Estimation for the Rayleigh Regression Model
TL;DR: Bias-adjusted estimators tailored for the Rayleigh regression model based on: 1) Cox and Snell's method; 2) Firth's scheme; and 3) the parametric bootstrap method yield nearly unbiased estimates and accurate modeling results.
Proceedings ArticleDOI
Autoregressive model for multi-pass SAR change detection based on image stacks
Bruna Gregory Palm,Dimas I. Alves,Viet T. Vu,Mats I. Pettersson,Fábio M. Bayer,Renato J. Cintra,Renato Machado,Patrik Dammert,Hans Hellsten +8 more
TL;DR: Applying AR model for each pixel position in the image stack obtained an estimated image of the ground scene which can be used as a reference image for CDA and reveals that ground scene estimates by the AR models is accurate and can be use for change detection applications.