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Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
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Improved Dynamic Window Approach by Using Lyapunov Stability Criteria |
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2008 |
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Latin American Applied Research |
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38 |
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4 |
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289–298 |
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ADAS @ adas @ BSA2008 |
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1056 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
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2015 |
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IEEE Transactions on Neural Networks and Learning Systems |
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TNNLS |
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26 |
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8 |
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1761 - 1775 |
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Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
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2162-237X |
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ADAS; 600.076 |
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Admin @ si @ DFD2015 |
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2547 |
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Author |
Angel Sappa; M.A. Garcia |
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Incremental Integration of Multiresolution Range Images |
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2007 |
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The imaging science journal. Vol. 55, No. 3 pp. 127–139 |
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ADAS @ adas @ SaG2007d |
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812 |
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Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
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Incremental model learning for spectroscopy-based food analysis |
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2017 |
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Chemometrics and Intelligent Laboratory Systems |
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CILS |
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167 |
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123-131 |
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Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
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In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. |
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ADAS; 600.118 |
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Admin @ si @ DGK2017 |
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3002 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
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Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
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2016 |
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Robotics and Autonomous Systems |
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RAS |
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83 |
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312-325 |
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Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
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When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
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Elsevier B.V. |
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ADAS; 600.086, 600.076 |
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Admin @ si @OSS2016a |
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2806 |
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