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Claudine Badue
Researcher at Universidade Federal do Espírito Santo
Publications - 98
Citations - 2009
Claudine Badue is an academic researcher from Universidade Federal do Espírito Santo. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 17, co-authored 90 publications receiving 1124 citations. Previous affiliations of Claudine Badue include Universidade Federal de Minas Gerais.
Papers
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Journal ArticleDOI
Self-driving cars: A survey
Claudine Badue,Rânik Guidolini,Raphael V. Carneiro,Pedro Azevedo,Vinicius B. Cardoso,Avelino Forechi,Luan F. R. Jesus,Rodrigo F. Berriel,Thiago M. Paixão,Filipe Mutz,Lucas de Paula Veronese,Thiago Oliveira-Santos,Alberto F. De Souza +12 more
TL;DR: A detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA), is presented.
Proceedings ArticleDOI
Distributed query processing using partitioned inverted files
TL;DR: Experimental results on retrieval eficiency show that, within the framework, the global index partitioning outpe~orms the local index partitions in the distributed system.
Proceedings ArticleDOI
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
Jacson Rodrigues Correia-Silva,Rodrigo F. Berriel,Claudine Badue,Alberto F. De Souza,Thiago Oliveira-Santos +4 more
TL;DR: This paper investigates if a target blackbox CNN can be copied by persuading it to confess its knowledge through random non-labeled data, and shows that it is possible to create a copycat CNN by simply querying a target network as black-box withrandom non- labeled data.
Posted Content
PolyLaneNet: Lane Estimation via Deep Polynomial Regression
Lucas Tabelini,Rodrigo F. Berriel,Thiago M. Paixão,Claudine Badue,Alberto F. De Souza,Thiago Oliveira-Santos +5 more
TL;DR: A novel method for lane detection is presented that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression, which is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset.
Posted Content
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Lucas Tabelini,Rodrigo F. Berriel,Thiago M. Paixão,Claudine Badue,Alberto F. De Souza,Thiago Oliveira-Santos +5 more
TL;DR: This work proposes an anchor-based deep lane detection model, which, akin to other generic deep object detectors, uses the anchors for the feature pooling step, and shows that the method outperforms the current state-of-the-art methods showing both higher efficacy and efficiency.