494 research outputs found
Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs
The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image
classification. By efficient schemes, we mean schemes that produce good classification results in
terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing
infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The
n-dimensional images include images with two and three dimensions, such as images coming from
the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand
hyperspectral images acquired in remote sensing.
In image analysis, classification is a regularly used method for information retrieval in areas such as
medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as
the hyperspectral images have been widely available in recent years owing to the reduction in the
size and cost of the sensors, the number of applications at lab scale, such as food quality control, art
forgery detection, disease diagnosis and forensics has also increased. Although there are many
spectral-spatial classification schemes, most are computationally inefficient in terms of execution
time. In addition, the need for efficient computation on low-cost computing infrastructures is
increasing in line with the incorporation of technology into everyday applications.
In this thesis we have proposed two spectral-spatial classification schemes: one based on
segmentation and other based on wavelets and mathematical morphology. These schemes were
designed with the aim of producing good classification results and they perform better than other
schemes found in the literature based on segmentation and mathematical morphology in terms of
accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU
computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on
GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters
were analyzed and different data partitioning and thread block arrangements were studied to exploit
the GPU resources. The results show that the GPU is an adequate computing platform for on-board
processing of hyperspectral information
Efficient multitemporal change detection techniques for hyperspectral images on GPU
Hyperspectral images contain hundreds of reflectance values for each pixel.
Detecting regions of change in multiple hyperspectral images of the same
scene taken at different times is of widespread interest for a large number of
applications. For remote sensing, in particular, a very common application is
land-cover analysis. The high dimensionality of the hyperspectral images
makes the development of computationally efficient processing schemes
critical. This thesis focuses on the development of change detection
approaches at object level, based on supervised direct multidate
classification, for hyperspectral datasets. The proposed approaches improve
the accuracy of current state of the art algorithms and their projection onto
Graphics Processing Units (GPUs) allows their execution in real-time
scenarios
Massively parallel landscape-evolution modelling using general purpose graphical processing units
As our expectations of what computer systems can do and our ability to capture data improves, the desire to perform ever more computationally intensive tasks increases. Often these tasks, comprising vast numbers of repeated computations, are highly interdependent on each other – a closely coupled problem. The process of Landscape-Evolution Modelling is an example of such a problem. In order to produce realistic models it is necessary to process landscapes containing millions of data points over time periods extending up to millions of years. This leads to non-tractable execution times, often in the order of years. Researchers therefore seek multiple orders of magnitude reduction in the execution time of these models. The massively parallel programming environment offered through General Purpose Graphical Processing Units offers the potential for multiple orders of magnitude speedup in code execution times. In this paper we demonstrate how the time dominant parts of a Landscape-Evolution Model can be recoded for a massively parallel architecture providing two orders of magnitude reduction in execution time
Medical image segmentation using GPU-accelerated variational level set methods
Medical imaging techniques such as CT, MRI and x-ray imaging are a crucial component of modern diagnostics and treatment. As a result, many automated methods involving digital image processing have been developed for the medical field. Image segmentation is the process of finding the boundaries of one or more objects or regions of interest in an image. This thesis focuses on accelerating image segmentation for the localization of cancerous lung nodules in two-dimensional radiographs. This process is used during radiation treatment, to minimize radiation exposure to healthy tissue. The variational level set method is used to segment out the lung nodules. This method represents an evolving segmentation boundary as the zero level set of a function on a two-dimensional grid. The calculus of variations is employed to minimize a set of energy equations and find the nodule\u27s boundary. Although this approach is flexible, it comes at significant computational cost, and is not able to run in real time on a general purpose workstation. Modern graphics processing units offer a high performance platform for accelerating the variational level set method, which, in its simplest sense, consists of a large number of parallel computations over a grid. NVIDIA\u27s CUDA framework for general purpose computation on GPUs was used in conjunction with three different NVIDIA GPUs to reduce processing time by 11x--20x. This speedup was sufficient to allow real-time segmentation at moderate cost
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
In this paper, we address the problem of efficient execution of a computation
pattern, referred to here as the irregular wavefront propagation pattern
(IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in
several image processing operations. In the IWPP, data elements in the
wavefront propagate waves to their neighboring elements on a grid if a
propagation condition is satisfied. Elements receiving the propagated waves
become part of the wavefront. This pattern results in irregular data accesses
and computations. We develop and evaluate strategies for efficient computation
and propagation of wavefronts using a multi-level queue structure. This queue
structure improves the utilization of fast memories in a GPU and reduces
synchronization overheads. We also develop a tile-based parallelization
strategy to support execution on multiple CPUs and GPUs. We evaluate our
approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs
and 2 multicore CPUs) using the IWPP implementations of two widely used image
processing operations: morphological reconstruction and euclidean distance
transform. Our results show significant performance improvements on GPUs. The
use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x
with respect to single core CPU executions for morphological reconstruction and
euclidean distance transform, respectively.Comment: 37 pages, 16 figure
- …