Fish Classification Using Memetic by Alsmadi Mutasem (2 results)

Author: 
Title: 
Refine with Advanced Search

Refine your search

  • Books (2)

  • New (2)

to

Custom price range (US$)

to

  • Language: English

    Published by LAP Lambert Academic Publishing, 2012

    3848421674 / 9783848421671

    • Softcover

    Seller: preigu, Osnabrück, Germanypreigu

    5-star seller
    Contact seller

    Condition: New

    US$ 67.29

    US$ 78.78 shipping 
    Ships from Germany to U.S.A.

    Quantity: 5 available

    Taschenbuch. Condition: Neu. Fish Classification | Fish Classification Using Memetic Algorithms with Back Propagation Classifier | Mutasem Alsmadi (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783848421671 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

  • Language: English

    Published by LAP Lambert Academic Publishing, 2012

    3848421674 / 9783848421671

    • Softcover
    • Print on Demand

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

    5-star seller
    Contact seller

    Condition: New

    US$ 79.77

    US$ 39.39 shipping 
    Ships from Germany to U.S.A.

    Quantity: 2 available

    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.…