128 research outputs found
3D + t Morphological Processing: Applications to Embryogenesis Image Analysis
We propose to directly process 3D + t image sequences with mathematical morphology operators, using a new classi?cation of the 3D+t structuring elements. Several methods (?ltering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebra?sh embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebra?sh early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it bene?ts from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms
Image Processing Using FPGAs
This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs
A multiobjective metaheuristic approach for morphological filters on many-core architectures
Mathematical Morphology (MM) is a set-theoretic technique for the analysis of geometrical structures. It provides a powerful tool for image processing, but is hampered by significant computational requirements. These requirements can be substantially reduced by decomposing complex operators into sequences of simpler operators, at the cost of degradation of the quality of the results. This decomposition also directly translates to streaming task graphs, a programming model that maps well to the kind of systolic architectures typically associated with many-core systems. There is however a trade-off between mappings that implement high-quality filters and mappings that offer high performance in many-core systems. The approach presented in this paper exploits a multi-objective evolutionary algorithm as a design-time tool to investigate trade-offs between the quality of the MM decomposition and computational performance. The evolutionary process performs an analysis of filter quality vs computational performance and generates a set of task graphs and mappings that represent different trade-offs between the two objectives. It then outputs a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. The performance of the tool is benchmarked on a morphological filter for the detection of features in a high-resolution PCB image
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
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
Monitoring the Microstructural Evolution of Solid Oxide Fuel Cell Anodes
As the global energy landscape evolves in the face of climate change, the uptake of intermittent renewables is gaining ground. Electrochemical devices for energy generation and storage are therefore becoming increasingly prevalent. Solid oxide fuel cells represent an energy conversion device with high efficiency and fuel versatility but continue to suffer from cost and durability issues, hindering commercial viability. Establishing electrode microstructure-property relationships provides insight into both initial and long term performance and thus far, full comprehension of degradation phenomena has remained elusive. Building on 2D stereological approaches, recent advances in tomographic techniques such as focused-ion beam-scanning electron microscopy and X-ray nano computed tomography have allowed for 3D investigations of electrode microstructures. However, the former methodology is inherently destructive and with the latter, reliable contrast for typical SOFC electrode materials has not been easily accessible. In this thesis, an examination of these tomographic modalities is conducted, with focused-ion beam scanning electron microscropy slice and view applied to both virgin and aged Ni-YSZ anodes to virtually reconstruct their microstructures. A laser-preparation technique for the fabrication of geometrically optimised samples for X ray nano-computed tomography is developed, and facilitates access to a larger sampled volume, thus providing more representative characterisation of the entire anode. Prepared samples are exposed to ex-situ annealing in a lab-based furnace wherein 900 °C is identified as the appropriate temperature for monitoring appreciable microstructual evolution within the first 12 hours of annealing. An in-situ¬ laser heating set-up at a synchrotron beamline illustrates the very early-stage microstructural reorganisation inherent to high-temperature operation. Significant attention is directed throughout towards the extraction of reliable metrics, sampling a representative volume element and capturing evolution by digital volume correlation techniques. The expectation is that the developed methodology will provide insight into the necessary fabrication and operational parameters for maximising solid oxide fuel cell performance and durability
Advanced Applications of Rapid Prototyping Technology in Modern Engineering
Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Analysis of Some Textured Images by Transputer
Texture, as a visual perception, can be easily seen by eye and often described without much difficulty. However, textural recognition and measurement by machine is a very different issue and has only recently been developed. In this thesis, a whole set of new algorithms have been developed to analyse textured images with particular reference to the requirements of soil microstructural applications. The new technology of parallel processing is used to implement and improve the complicated computations
Vision Sensors and Edge Detection
Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing
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