Automatic selection and detection of visual landmarks using multiple segmentations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4319 LNCS, Issue , 2006, Pages 601-610

  Langdon, Daniel ^a   Soto, Alvaro ^a   Mery, Domingo ^a  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

AUTOMATIC SELECTION; MULTIPLE SEGMENTATION; REAL-WORLD SCENARIO; SEGMENTATION ALGORITHMS; UNSTRUCTURED ENVIRONMENTS; UNSUPERVISED APPROACHES; VISION BASED; VISUAL LANDMARKS;

IMAGE SEGMENTATION;

EID: 70350268368     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11949534_60     Document Type: Conference Paper

References (14)

1. I. Biederman. Recognition by components: a theory of human image understanding. Psychological Review, 94(2):115-147, 1987.
2. J. Duncan. Integrated mechanisms of selective attention. Current Opinion in Biology, 7:255-261, 1997.
3. S. Harris. A combined corner and edge detector. pages 147-151, 1988.
4. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998.
5. N. Karlsson, L. Goncalves, M. Munich, and P. Pirjanian. The vslam algorithm for navigation in natural environments. Korean Robotics Society Review, 2(1):51-67, 2005.
6. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
7. D. Mery and F. Pedreschi. Segmentation of colour food images using a robust algorithm. Journal of Food engineering, 66:353-360, 2004.
8. X. Munoz. Image segmentation integrating colour, texture and boundary information. PhD thesis, Universitat de Girona, 2002.
9. B. Schiele and J. Crowley. Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation, 9:777-804, 1997.
10. A. Soto. A Probabilistic Approach for the Adaptive Integration of Multiple Visual Cues Using an Agent Framework. PhD thesis, Robotics Institute, Carnegie Mellon University, 2002.
11. Y. Sun and R. Fisher. Object-based visual attention for computer vision. Artificial Intelligence, 146:77-123, 2003.
12. J. K. Tsotsos, S. Culhane, W. Wai, Y. Lai, N. Davis, and F. Nuflo. Modeling visual-attention via selective tuning. Artificial Intelligence, 78(1-2):507-545, 1995.
13. D. Walther, U. Rutishauser, C. Koch, and P. Perona. Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 100:41-63, 2005.
14. D.R. Walther, D. Edgington and C. Koch. Detection and tracking of objects in underwater video. pages 544-549, 2004.