2,520 research outputs found
Contributions to the analysis and segmentation of remote sensing hyperspectral images
142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data
Multiscale Bone Remodelling with Spatial P Systems
Many biological phenomena are inherently multiscale, i.e. they are
characterized by interactions involving different spatial and temporal scales
simultaneously. Though several approaches have been proposed to provide
"multilayer" models, only Complex Automata, derived from Cellular Automata,
naturally embed spatial information and realize multiscaling with
well-established inter-scale integration schemas. Spatial P systems, a variant
of P systems in which a more geometric concept of space has been added, have
several characteristics in common with Cellular Automata. We propose such a
formalism as a basis to rephrase the Complex Automata multiscaling approach
and, in this perspective, provide a 2-scale Spatial P system describing bone
remodelling. The proposed model not only results to be highly faithful and
expressive in a multiscale scenario, but also highlights the need of a deep and
formal expressiveness study involving Complex Automata, Spatial P systems and
other promising multiscale approaches, such as our shape-based one already
resulted to be highly faithful.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
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
From Models to Simulations
This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s.
Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations
Mortal Computation: A Foundation for Biomimetic Intelligence
This review motivates and synthesizes research efforts in
neuroscience-inspired artificial intelligence and biomimetic computing in terms
of mortal computation. Specifically, we characterize the notion of mortality by
recasting ideas in biophysics, cybernetics, and cognitive science in terms of a
theoretical foundation for sentient behavior. We frame the mortal computation
thesis through the Markov blanket formalism and the circular causality entailed
by inference, learning, and selection. The ensuing framework -- underwritten by
the free energy principle -- could prove useful for guiding the construction of
unconventional connectionist computational systems, neuromorphic intelligence,
and chimeric agents, including sentient organoids, which stand to revolutionize
the long-term future of embodied, enactive artificial intelligence and
cognition research.Comment: Several revisions applied, corrected error in Jarzynski equality
equation (w/ new citaion); references and citations now correctly aligne
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