Alsmadi Mutasem (5 results)

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  • Language: English

    Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2012

    3848421674 / 9783848421671

    • Softcover

    Seller: Books Puddle, New York, NY, U.S.A.Books Puddle

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    Condition: New. pp. 180.

  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2012

    3848421674 / 9783848421671

    • Softcover
    • Print on Demand

    Seller: moluna, Greven, Germanymoluna

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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Alsmadi MutasemMutasem Khalil Sari Al Smadi. He received his BS degree in Software engineering in 2006 from Philadelphia University, Jordan. His MSc degree in intelligent system in 2007 from University Utara Malaysia. His PhD degree .

  • Language: English

    Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2012

    3848421674 / 9783848421671

    • Softcover
    • Print on Demand

    Seller: Majestic Books, Hounslow, United KingdomMajestic Books

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    Condition: New

    US$ 119.27

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    Condition: New. Print on Demand pp. 180 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

  • Language: English

    Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2012

    3848421674 / 9783848421671

    • Softcover
    • Print on Demand

    Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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    Condition: New

    US$ 128.45

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    Condition: New. PRINT ON DEMAND pp. 180.

  • Language: English

    Published by LAP Lambert Academic Publishing, 2012

    3848421674 / 9783848421671

    • Softcover
    • Print on Demand

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Condition: New

    US$ 114.55

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    Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work presents a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose feature descriptors and do not analyze their individual impacts in the whole classification task. A problem with classification of fish species is still vital facets due to: arbitrary fish size and orientation; feature variability; environmental changes; poor image quality; segmentation failures; imaging conditions; physical shaping; distortion; noise; overlap, and occlusion of objects in digital images. In addition, the problem in fish classification is to find meaningful features based on the image segmentation and features extraction, and an efficient classifier that produces a better fish images classification accuracy rate. Thus, this research aims to design and develop a novel fish classifier based on an appropriate feature set obtained from image segmentation and features extraction methods, to classify the given fish output into its cluster (poison and non-poison fish), therefore; classifying the clustered poison and non-poison fish into its family.