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Hybrid Metaheuristics for Image Analysis - HARDCOVER
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Condition:
“THE BOOK IS NEW - THE COVER HAS VERY MINOR WEAR - HARDCOVER”
Like new
A book that looks new but has been read. Cover has no visible wear, and the dust jacket (if applicable) is included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections.
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Postage:
Free USPS Media MailTM.
Located in: Hinton, Iowa, United States
Delivery:
Estimated between Thu, 14 Aug and Mon, 18 Aug to 94104
Returns:
30-day returns. Buyer pays for return postage. If you use an eBay postage label, it will be deducted from your refund amount.
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eBay item number:285010062963
Item specifics
- Condition
- Like new
- Seller notes
- “THE BOOK IS NEW - THE COVER HAS VERY MINOR WEAR - HARDCOVER”
- Book Title
- Hybrid Metaheuristics For Image Analysis
- Custom Bundle
- No
- Item Height
- 0.6in.
- ISBN
- 9783319776248
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
331977624X
ISBN-13
9783319776248
eBay Product ID (ePID)
20038750266
Product Key Features
Number of Pages
Xii, 256 Pages
Language
English
Publication Name
Hybrid Metaheuristics for Image Analysis
Publication Year
2018
Subject
Engineering (General), Intelligence (Ai) & Semantics, Neural Networks, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Computers, Technology & Engineering
Format
Hardcover
Dimensions
Item Weight
20.1 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms.- A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation.- Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers.- Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm.- Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization.- Image Segmentation Using Metaheuristic-Based DeformableModels.- Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection.- Image Thresholding Based on Fuzzy Particle Swarm Optimization.- Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype.
Synopsis
This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology., Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms.- A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation.- Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers.- Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm.- Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization.- Image Segmentation Using Metaheuristic-Based DeformableModels.- Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection.- Image Thresholding Based on Fuzzy Particle Swarm Optimization.- Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype.
LC Classification Number
Q334-342
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