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Apprendre à programmer : Algorithmes et perception objet

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Global DTV IC Industry Report, 2007-2008 74. : Fuzzy set theoretic approach to computer vision: an overview. In: Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, USA, March 1992, pp. 135–142 (1992) 75. : FIRE operators for image processing. Fuzzy Sets and Systems 103, 265–275 (1999) 76. : Edge detection in noisy images using fuzzy reasoning. IEEE Trans. on Instrumentation and Measurement 47(5), 1102–1105 (1995) 77. : Edge detction by FIRE operators. In: Proc. IEEE Int. Conf. on Fuzzy Systems, Orlando (USA), June 1994, vol.

In: Proc. IEEE Int. Workshop VLSI Design and Video Technology, Suzhou (Chica), May 2005, pp. org 46 1 Basic Concepts 72. : SOC design flow case study: design a video processing pipeline. In: Tutorial of International Conference on ASIC (ASICON), Guilin (China), October 2007, p. 10 (2007) 73. Global DTV IC Industry Report, 2007-2008 74. : Fuzzy set theoretic approach to computer vision: an overview. In: Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, USA, March 1992, pp. 135–142 (1992) 75. : FIRE operators for image processing.

Removal of impulse noise using a FIRE filter. In: Porc. IEEE Int. Conf. on Image Processing, Lausanne (Switzerland), September 1996, vol. 2, pp. 975–978 (1996) 93. : A fuzzy decision directed filter for impulse noise reduction. Fuzzy Sets and Systems 77, 111–116 (1996) References 47 94. : Hybrid neuro-fuzzy filter for impulse noise removal. Pattern Recognition 32, 1843–1855 (1999) 95. : Noise removal from image data using recursive neurofuzzy filters. IEEE Trans. on Instrumentation and Measurement 49(2), 307–314 (2000) 96.

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