High-Performance Deformable Image Registration Algorithms for Manycore Processors

Shackleford, James; Kandasamy, Nagarajan; Sharp, Gregory

ISBN 10: 0124077412 / ISBN 13: 9780124077416

Editore: Morgan Kaufmann, 2013

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Codice articolo 19756713

Dati bibliografici

• Titolo: High-Performance Deformable Image Registration Algorithms for Manycore Processors
• Editore: Morgan Kaufmann
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 0124077412
• ISBN 13: 9780124077416
• Rilegatura: Brossura
• Condizione: As New

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Riassunto

High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.

Dalla quarta di copertina

High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the demons registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame, and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing; and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.