Fish Classification: Fish Classification Using Memetic Algorithms with Back Propagation Classifier

 
9783848421671: Fish Classification: Fish Classification Using Memetic Algorithms with Back Propagation Classifier
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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.

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About the Author:

Mutasem 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 in intelligent system in 2011 from University National Malaysia, Malaysia. He has published number of papers related to these areas.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Fish Classification Using Memetic Algorithms with Back Propagation Classifier | 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. | Format: Paperback | Language/Sprache: english | 180 pp. Seller Inventory # K9783848421671

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Book Description LAP Lambert Academic Publishing Mrz 2012, 2012. Taschenbuch. Condition: Neu. Neuware - 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. 180 pp. Englisch. Seller Inventory # 9783848421671

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Book Description LAP Lambert Academic Publishing Mrz 2012, 2012. Taschenbuch. Condition: Neu. Neuware - 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. 180 pp. Englisch. Seller Inventory # 9783848421671

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Book Description LAP Lambert Academic Publishing Mrz 2012, 2012. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - 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. 180 pp. Englisch. Seller Inventory # 9783848421671

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Mutasem Alsmadi, Khairuddin Omar, Ibrahim Almarashdeh
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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Condition: New. Aufl.. Language: English . Brand New Book. 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. Seller Inventory # KNV9783848421671

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Book Description LAP Lambert Academic Publishing. Paperback. Condition: New. 180 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783848421671

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