On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel approach to the fast computation of Zernike moments
Pattern Recognition
Biomedical image analysis on a cooperative cluster of GPUs and multicores
Proceedings of the 22nd annual international conference on Supercomputing
GPU acceleration of Zernike moments for large-scale images
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
High Performance Computation of Moments for an Accurate Classification of Bone Tissue Images
HPCC '11 Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications
Recognitive Aspects of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shot boundary detection using Zernike moments in multi-GPU multi-CPU architectures
Journal of Parallel and Distributed Computing
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In our previous work, we have provided tools for an efficient characterization of biomedical images using Legendre and Zernike moments, showing their relevance as biomarkers for classifying image tiles coming from bone tissue regeneration studies (Ujaldon, 2009) [24]. As part of our research quest for efficiency, we developed methods for accelerating those computations on GPUs (Martin-Requena and Ujaldon, 2011) [10,9]. This new stage of our work focuses on the efficient data partitioning to optimize the execution on many-cores and clusters of GPUs to attain gains up to three orders of magnitude when compared to the execution on multi-core CPUs of similar age and cost using 1 Mpixel images. We deploy a successive and successful chain of optimizations which exploit symmetries in trigonometric functions and access patterns to image pixels which are effectively combined with massive data parallelism on GPUs to enable (1) real-time processing for our set of input biomedical images, and (2) the use of high-resolution images in clinical practice.