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ASARI: a new adaptive oversegmentation method

Mathieu, Bérengère and Crouzil, Alain and Puel, Jean-Baptiste ASARI: a new adaptive oversegmentation method. (2017) In: Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017), 20 June 2017 - 23 June 2017 (Faro, Portugal).

(Document in English)

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Official URL: https://doi.org/10.1007/978-3-319-58838-4_22


Using superpixels instead of pixels has become a popular pre-processing step in computer vision. However, there are few adaptive methods able to automatically find the best comprise between boundary adherence and superpixel number. Moreover, no algorithm producing color and texture homogeneous superpixels keeps competitive execution time. In this article we suggest a new graph-based region merging method, called Adaptive Superpixel Algorithm with Rich Information (ASARI) to solve these two difficulties. We will show that ASARI achieves results similar to the state-of-the-art methods on the existing benchmarks and outperforms these methods when dealing with big images.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in Volume 10255 of Lecture Notes in Computer Science ISSN : 0302-9743 ISBN: 978-3-319-58837-7 The original PDF is available at: https://link.springer.com/chapter/10.1007/978-3-319-58838-4_22
HAL Id:hal-01913673
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Ecole Nationale Supérieure de Formation de l'Enseignement Agricole - ENSFEA (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
ANR-11-LABX-0040-CIMI within the program ANR-11-IDEX-0002-02
Deposited On:24 Sep 2018 14:25

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