Alterar idioma para EnglishAlterar idioma para Japonês
Foto do perfil

Contact Info

Direct Dial +55 21 2391 4835
Mobile +55 21 98985 5382
allan.freites@lickslegal.com
Open PDF file

Allan Freites

Electrical Engineer and Patent Specialist

Allan F. da Silva is an electrical engineer and patent specialist who joined Licks Attorneys’ Rio de Janeiro office in 2026. He provides technical support for patent litigation matters in the electrical engineering and telecommunications industries, with a particular emphasis on video processing and coding technologies. His practice focuses on the technical analysis of patents and their prosecution histories before the BRPTO, EPO, and USPTO. In addition, Mr. Silva assists in building technical arguments for infringement and validity actions, analyzes and rebuts opposing expert opinions, and provides support in matters involving Standard Essential Patents (SEPs) and essentiality determinations.

Mr. Silva received an Engineering degree in Electronic and Computer Engineering from the Federal University of Rio de Janeiro (UFRJ) in 2013, a M.Sc. degree in Electrical Engineering from the Federal University of Rio de Janeiro (COPPE/UFRJ) in 2015, and a D.Sc. at the same institution in 2019. He also earned a Technological Innovation Award from ANP for his participation in the DORIS project to monitor oil platforms.

Between November 2017 and October 2018, Mr. Silva earned a Doctoral Exchange Program grant (SWE) from the Brazilian Council for Scientific and Technological Development (CNPq), where he joined the “Integration: from Material to Systems (IMS)” laboratory at the University of Bordeaux. Since 2022, he is a postdoctoral researcher at Institute of Systems and Robotics in the University of Coimbra (ISR-UC). His research interests include the areas of computer vision as well as video and image processing.

  • Portuguese
  • English
  • French

  • Litigation
  • Patents

  • Indication to CAPES Thesis Award 2020 (2020).

  • Pixel-based change detection in moving-camera videos using twin convolutional features on a data-constrained scenario, IEEE Access, 2025;
  • Frames in signal processing. In: Signal Processing and Machine Learning Theory, Elsevier, 2023;
  • Change detection in moving-camera videos with limited samples using twin-CNN features and learnable morphological operations, Signal Processing: Image Communication, 2023;
  • A morphological approach to the automatic detection of dark fringes of birefringence images obtained in a multipass rheometer, Rheologica Acta, 2020;
  • Fundamentals and techniques for the localization of a sensor and the mapping of an environment using videos. In: Livro de minicursos SBRT, 2019;
  • Moving-camera video surveillance in cluttered environments using deep features. In: IEEE International Conference on Image Processing (ICIP), 2018;
  • Anomaly detection with a moving camera using multiscale video analysis. In: Multidimensional Systems and Signal Processing, 2018;
  • Anomaly detection with a moving camera using spatio-temporal codebooks. In: Multidimensional Systems and Signal Processing, 2017;
  • Anomaly detection in moving-camera video sequences using principal subspace analysis. In: IEEE Transactions on Circuits and Systems I, 2017;
  • Online video-based sequence synchronization for moving camera object detection. In: IEEE International Workshop on Multimedia Signal Processing (MMSP), 2017;
  • Morphological approach to the automatic detection of dark fringes applied to birefringence images. In: IEEE International Conference on Image Processing (ICIP), 2015;
  • An annotated video database for abandoned-object detection in a cluttered environment. In: International Telecommunications Symposium (ITS), 2014.