Comparison of Local Feature Extraction Paradigms Applied to Visual SLAM

Autores/as

  • Víctor R. López López Instituto Tecnológico de Tijuana, Tree-Lab, Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Tijuana, Baja California, México
  • Leonardo Trujillo Instituto Tecnológico de Tijuana, Tree-Lab, Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Tijuana.
  • Pierrick Legrand Universite´ of Bordeaux, CQFD Team, INRIA Bordeaux, IMB, Talence, France
  • Victor H. Díaz Ramírez Instituto Politécnico Nacional, CITEDI
  • Gustavo Olague CICESE, EvoVisión Group, Applied Physics Division, Ensenada, Baja California

DOI:

https://doi.org/10.13053/cys-20-4-2500

Palabras clave:

Local features, genetic programming, composite correlation filter, SLAM.

Resumen

The detection and description of locally salient regions is one of the most widely used low-level processes in modern computer vision systems. The general approach relies on the detection of stable and invariant image features that can be uniquely characterized using compact descriptors. Many detection anddescription algorithms have been proposed, most ofthem derived using different assumptions or problem models. This work presents a comparison of different approaches towards the feature extraction problem, namely: (1) standard computer vision techniques;(2) automatic synthesis techniques based on geneticprogramming (GP); and (3) a new local descriptorbased on composite correlation filtering, proposed for the first time in this paper. The considered methods are evaluated on a difficult real world problem, vision-based simultaneous localization and mapping (SLAM). Using three experimental scenarios, results indicate that the GP-based methods and the correlation filtering techniques out perform widely used computer vision algorithms such as the Harris and Shi-Tomasi detectors and the Speeded Up Robust Features descriptor.

Biografía del autor/a

Víctor R. López López, Instituto Tecnológico de Tijuana, Tree-Lab, Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Tijuana, Baja California, México

Received a degree in Electronic Engineering (2012) and a Masters in Sciences of Engineering (2015) from the Instituto Tecnológico  de Tijuana, México. He is currently studying a PhD in Sciences of Engineering in the Instituto Tecnológico de Tijuana, México, where his research interests are Genetic Programming (GP), Computer Vision (CV) and Human-Computer interface (HCI). He is also a current member of the TREE-LAB research group.

Leonardo Trujillo, Instituto Tecnológico de Tijuana, Tree-Lab, Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Tijuana.

Received a degree in Electronic Engineering (2002) and a Masters in Computer Science (2004) from the Instituto Tecnológico de Tijuana, México. He also received a doctorate in Computer Science from the CICESE research center, in Ensenada México (2008), developing Genetic Programming (GP) applications for Computer Vision problems, focusing on feature extraction and image description. He is currently professor at Instituto Tecnológico de Tijuana, México (ITT), where he is currently President of the Master Program, head of the Cybernetics research group and is the head researcher of the TREE-LAB research group.

Pierrick Legrand, Universite´ of Bordeaux, CQFD Team, INRIA Bordeaux, IMB, Talence, France

Received his PhD in applied mathematics (multifractal analysis, signal processing) from Ecole centrale de Nantes and from Nantes university in December 2004. During his thesis, under the supervision of Jacques Levy-Vehel, he developed interpolation and denoising methods based on Hölderian regularity and  wavelets, proving that these methods reach an optimal rate of convergence. Moreover, during his thesis he was in charge of the development of FracLab, a free Matlab toolbox. In 2005 he received a post-doctoral position shared between the Evovision group at CICESE research center (Ensenada, México) and INRIA COMPLEX Team (Rocquencourt, France). In 2006, a second post-doctoral position allowed Pierrick Legrand to develop the first genetic algorithm running on a pocket PC. On September 2006, he became associate professor at the university of Bordeaux (UFR Sciences and Modelisation) and researcher at the IMB (Institut de Mathématiques de Bordeaux, UMR CNRS 5251), both of which are positions he currently holds. In 2008, Pierrick Legrand, started to develop and apply signal processing technics and evolutionary computation methods to the analysis of EEG signals. Since 2010 he is also a researcher at INRIA, the same year he was awarded two best-paper awards from the leading conference in evolutionary computation, GECCO 2010.

Victor H. Díaz Ramírez, Instituto Politécnico Nacional, CITEDI

Obtained his MS degree in electronics engineering from Instituto Tecnológico de Mexicali in 2003 and his PhD in computer science from Centro de Investigación  Científica y de Educación Superior de Ensenada (CICESE), México, in 2007. He is now a professor at Instituto Politécnico Nacional, México. His research interests include signal and image processing, pattern recognition, and opto-digital correlators.

Gustavo Olague, CICESE, EvoVisión Group, Applied Physics Division, Ensenada, Baja California

Received his Ph.D. in Computer Vision, Graphics and Robotics from INPG (Institut Polytechnique de Grenoble) and INRIA (Institut National de Recherche en Informatique et Automatique) in France. He is a Professor in the Dept. of Computer Science at CICESE (Centro de Investigación Científica y de Educación Superior de Ensenada) in México, and the Director of its EvoVisión Research Team. He is also an Adjoint Professor of Engineering at UACH (Universidad Autónoma de Chihuahua). He has authored over 100 conference proceedings papers and journal articles, he coedited two special issues in Pattern Recognition Letters and Evolutionary Computation, and he served as cochair of the Real-World Applications track at the main international evolutionary computing conference, GECCO (ACM SIGEVO Genetic and Evolutionary Computation Conference). Prof. Olague has received numerous distinctions, among them the Talbert Abrams Award presented by the American Society for Photogrammetry and Remote Sensing (ASPRS) for authorship and recording of current and historical engineering and scientific developments in photogrammetry; Best Paper awards at major conferences such as GECCO, EvoIASP (EuropeanWorkshop on Evolutionary Computation in Image Analysis, Signal Processing and attern Recognition) and EvoHOT (European Workshop on Evolutionary Hardware Optimization); and twice the Bronze Medal at the Humies (GECCO award for Human-Competitive results produced by genetic and evolutionary computation). His main research interests are evolutionary computing computer vision. He is author of the book Evolutionary Computer Vision published by Springer

Descargas

Publicado

2016-12-18

Número

Vol. 20 Núm. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Sección

Articles of the Thematic Issue

Licencia

Transfiero exclusivamente a la revista “Computación y Sistemas”, editada por el Centro de Investigación en Computación (CIC), los Derechos de Autor del artículo antes mencionado, asimismo acepto que no serán transferidos a ninguna otra publicación, en cualquier formato, idioma, medio existente (incluyendo los electrónicos y multimedios) o por desarrollar.

Certifico que el artículo, no ha sido divulgado previamente o sometido simultáneamente a otra publicación y que no contiene materiales cuya publicación violaría los Derechos de Autor u otros derechos de propiedad de cualquier persona, empresa o institución. Certifico además que tengo autorización de la institución o empresa donde trabajo o estudio para publicar este Trabajo.

El autor, representante acepta la responsabilidad por la publicación del Trabajo en nombre de todos y cada uno de los autores.

Esta Transferencia está sujeta a las siguientes reservas:

  • Los autores conservan todos los derechos de propiedad (tales como derechos de patente) de este Trabajo, con excepción de los derechos de publicación transferidos al CIC, mediante este documento.
  • Los autores conservan el derecho de publicar el Trabajo total o parcialmente en cualquier libro del que ellos sean autores o editores y hacer uso personal de este trabajo en conferencias, cursos, páginas web personal, etc.