"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.
This book demonstrates:
How to redesign widely used image registration algorithms so as
to best expose the underlying parallelism available in these
algorithmsHow to pose and implement the parallel versions of the
algorithms within the single instruction, multiple data (SIMD)
model supported by GPUsProgramming "tricks" that can help readers
develop other image processing algorithms, including registration
algorithms for the GPU
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