Ando S. Image field categorization and edge/corner detection from gradient covariance.IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(2):179–190.
Article
Google Scholar
Burt PJ, Hong T and Adelson EH. The laplacian pyramid as a compact image code.IEEE Transactions on Communications 1983; 31(4):532–540.
Article
Google Scholar
Connor CE, Egeth HE and Yantis S. Visual attention: bottom-up versus top-down.Current Biology 2004; 14(19):850–852.
Article
Google Scholar
Crowley JL, Riff O and Piater J. Fast computation of characteristic scale using a half octave pyramid. In:Proceedings of International Workshop on Cognitive Vision; 2002; Zurich, Switzerland. Berlin, Germany: Springer-Verlag; 2002. p. 1–8.
Google Scholar
Daugman JG. Complete discrete 2-d gabor transforms by neural networs for image analysis and compression.Proceedings of IEEE Transactions on Acoustics, Speech, and Signal 1988; 36(7):1169–1179.
Article
MATH
Google Scholar
Desimone R and Duncan J. Neural mechanisms of selective visual attention.Annual Reviews Neuroscience 1995; 18(1):193–222.
Article
Google Scholar
Draper BA, Baek K and Boody J. Implementing the expert object recognition pathway. In:Proceedings of International Conference on Computer Vision Systems; 2003; Graz, Austria. Berlin, Germany: Springer-Verlag; 2003. p. 1–11.
Google Scholar
Draper BA and Lionelle A. Evaluation of selective attention under similarity transformations.Computer Vision and Image Understanding 2002; 100(1):152–171.
Article
Google Scholar
Engel PM.INBC: an incremental algorithm for dataflow segmantation based on a probabilistic approach. Porto Alegre: Universidadade Federal do Rio Grande do Sul; 2009. (Technical Report RP-3690)
Google Scholar
Engel S, Zhang X and Wandell B. Colour tuning in human visual cortex measured with functional magnetic resonance imaging.Nature 1997; 388(6637):68–71.
Article
Google Scholar
Frintrop S. VOCUS: a visual attention system for object detection and goal-directed search. [PhD thesis]. Bonn:Universität Bonn; 2006.
Book
Google Scholar
Greenspan S, Belongie S, Goodman R, Perona P, Rakshit S and Anderson CH. Overcomplete steerable pyramid filters and rotation invariance. In:Proceedings of IEEE Computer Vision and Pattern Recognition; 1994; Seattle, WA. Los Alamitos, CA: IEEE Press; 1994. p. 222–228.
Chapter
Google Scholar
Harel J and Koch C. On the optimality of spatial attention for object detection. In:Proceedings of 5 International Workshop on Attention in Cognitive Systems; 2009; Santorini, Grécia. Berlin, Germany: Springer-Verlag; 2009. p. 1–14. (v. 5395).
Chapter
Google Scholar
Heinen MR and Engel PM. Visual selective attention model for robot vision. In:Proceedings of 5 IEEE Latin American Robotics Symposium; 2008; Salvador, Brazil. Los Alamitos, CA: IEEE Press; 2008. p. 1–6.
Google Scholar
Heinen MR and Engel PM. Evaluation of visual attention models under 2d similarity transformations. In:Proceedings of 24 ACM Symposium on Applied Computing; 2009; Honolulu, Hawaii. New York, NY: ACM press; 2009. (Special Track on Intelligent Robotic Systems).
Google Scholar
Indiveri G, Mürer R and Kramer J. Active vision using an analog VLSI model of selective attention.IEEE Transactions on Circuits and Systems 2001; 48(5):492–500. (parte II, Analog and digital signal processing).
Article
Google Scholar
Itti L. Models of bottom-up attention and saliency. San Diego: Elsevier Press; 2005. p. 576–582.
Google Scholar
Itti L and Koch C. Computational modeling of visual attention.Nature Reviews. 2001; 2(3):194–203.
Article
Google Scholar
Itti L, Koch C and Niebur E. A model of saliency-based visual attention for rapid scene nalysis.IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998; 20(11):1254–1259.
Article
Google Scholar
Kentridge R, Heywood C and Davidoff J. Color perception. In: Arbib MA. (Ed.).The handbook of brain theory and neural networks. 2 ed. Cambridge: MIT Press; 2003. p. 230–233.
Google Scholar
Klein RM. Inhibition of return.Trends in Cognitive Sciences. 2000; 4(4):138–147.
Article
Google Scholar
Koch C and Ullman S. Shifts in selective visual attention: toward the underlying neural circuitry.Human Neurobiology 1985; 4(4):219–227.
Google Scholar
Lee KW, Buxton H and Jianfeng F. Cue-guided search: a computational model of selective attention.IEEE Trans. Neural Networks 2005; 16(4):910–924.
Article
Google Scholar
Leventhal AG.The neural basis of visual function. Boca Raton: CRC Press; 1991. (v. 4, Vision and visual dysfunction).
Google Scholar
Lindeberg T. Feature detection with automatic scale selection.International Journal of Computer Vision 1998; 30(2):79–116.
Article
Google Scholar
Liu YH and Wang XJ. Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron.Journal of Computational Neuroscience 2001; 10(1):25–45.
Article
Google Scholar
Lowe DG. Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision 2004; 60(2):91–110.
Article
Google Scholar
Marfil R, Bandera A, Rodríguez JA and Sandoval F. A novel hierarchical framework for object-based visual attention. In:Proceedings of 5 International Workshop on Attention in Cognitive Systems; 2009; Santorini, Grécia. Berlin, Germany: Springer-Verlag; 2009. p. 27–40. (v. 5393).
Chapter
Google Scholar
Marques O, Mayron L, Borba G and Gamba H. An attentiondriven model for similar images with image retrieval applications.EURASIP Journal on Advances in Signal Processing 2007; (1):1–17.
Article
Google Scholar
Mozer MC and Sitton M. Computational modeling of spatial attention. In: Pashler H. (Ed.).Attention. London: Psychology Press, London; 1998. p. 341–395.
Google Scholar
Nagai Y. From bottom-up visual attention to robot action learning. In:Proceedings of 8 IEEE International Conference on Development and Learning; 2009; Shanghai, China. Los Alamitos, CA: IEEE Press.
Google Scholar
Niebur E and Koch C. Control of selective visual attention: modeling the “where” pathway.Neural Information Processing System 1996; 8(1):802–808.
Google Scholar
Orabona F, Metta G, and Sandini G. Object-based visual attention: a model for a behaving robot. In:Proceedings of 3 Attention and Performance in Computational Vision; 2005; San Diego, CA. Los Alamitos, CA: IEEE Press; 2005.
Google Scholar
Ouerhani N, Bur A and Hügli H. Visual attention-based robot self-localization. In:Proceedings of European Conference on Mobile Robots; 2005; Ancona, Italy. Los Alamitos, CA: IEEE Press; 2005. p. 8–13.
Google Scholar
Pashler, H.The Psycology of Attention. Cambridge: MIT Press; 1997.
Google Scholar
Perko R, Wojek C, Schiele B and Leonardis A. Integrating visual context and object detection within a probabilistic framework. In:Proceedings of 5 International Workshop on Attention in Cognitive Systems; 2009; Santorini, Grécia. Berlin, Germany: Springer-Verlag; 2009. p. 54–68. (v. 5395).
Chapter
Google Scholar
Treisman AM. Features and objects: the fourteenth bartlett memorial lecture.The Quarterly Journal of Experimental Psychology 1988; 40(2):201–237.
Google Scholar
Treisman AM and Gelade G. A feature integration theory of attention.Cognitive Psychology 1980; 12(1):97–136.
Article
Google Scholar
Tsotsos JK, Culhane SM, Wai WYK, Lai Y, Davis N, and Nuflo F. Modeling visual attention via selective tuning.Artificial Intelligence 1995; 78(1/2):507–545.
Article
Google Scholar
Vieira-Neto H.Visual novelty detection for autonomous inspection robots. [PhD thesis]. Essex: University of Essex; 2006.
Google Scholar
Vieira-Neto H and Nehmzow U. Visual novelty detection with automatic scale selection.Robotics and Autonomous Systems 2007; 55(9):693–701.
Article
Google Scholar
Wang T, Zheng N and Mei K. A visual brain chip based on selective attention for robot vision application. In:Proceedings of IEEE International Conference on Space Mission Challenges for Information Technology; 2009. Los Alamitos, CA: IEEE Press; 2009. p. 93–97.
Chapter
Google Scholar
Witkin AP. Scale-space filtering. In:Proceedings of International Joint Conference on Artificial Intelligence; 1983; Karlsruhe, Germany. San Fransisco, CA: Morgan Kaufman; 1983. p. 1019–1022.
Google Scholar