Image morphological processing

Mathematical Morphology with applications in image processing and analysis has been becoming increasingly important in today\u27s technology. Mathematical Morphological operations, which are based on set theory, can extract object features by suitably shaped structuring elements. Mathematical Morphological filters are combinations of morphological operations that transform an image into a quantitative description of its geometrical structure based on structuring elements. Important applications of morphological operations are shape description, shape recognition, nonlinear filtering, industrial parts inspection, and medical image processing. In this dissertation, basic morphological operations, properties and fuzzy morphology are reviewed. Existing techniques for solving corner and edge detection are presented. A new approach to solve corner detection using regulated mathematical morphology is presented and is shown that it is more efficient in binary images than the existing mathematical morphology based asymmetric closing for corner detection. A new class of morphological operations called sweep mathematical morphological operations is developed. The theoretical framework for representation, computation and analysis of sweep morphology is presented. The basic sweep morphological operations, sweep dilation and sweep erosion, are defined and their properties are studied. It is shown that considering only the boundaries and performing operations on the boundaries can substantially reduce the computation. Various applications of this new class of morphological operations are discussed, including the blending of swept surfaces with deformations, image enhancement, edge linking and shortest path planning for rotating objects. Sweep mathematical morphology is an efficient tool for geometric modeling and representation. The sweep dilation/erosion provides a natural representation of sweep motion in the manufacturing processes. A set of grammatical rules that govern the generation of objects belonging to the same group are defined. Earley\u27s parser serves in the screening process to determine whether a pattern is a part of the language. Finally, summary and future research of this dissertation are provided

Wearable Wireless Devices

No abstract available

Analysis of the complexity of algorithms for finding the coefficients of the mathematical model of lowintensity electroretinosignal

The complexity of methods of parametric identification is analyzed and their comparison is carried out at definition of coefficients of mathematical model of response of a retina of an eye at decrease in intensity of test light irritation. The algorithm of parametric identification of the mathematical model of retinal response based on direct complete search has a significant time complexity, which prevents rapid readjustment of the expert system in the case ofremote, automated processing of low-intensity retinal response to diagnose the functional state of the body. Therefore, it is necessary to study the complexity of search algorithms and apply other approaches to solving problem of optimization or parameter identification. In this case, the criterion K of the optimality of the selection of coefficients will be the proximity of the simulated retinal response to the reference, pre-developed response

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 129, June 1974

This special bibliography lists 280 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1974

Spatial and temporal background modelling of non-stationary visual scenes

PhDThe prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally

Autonomous Sensing Nodes for IoT Applications

The present doctoral thesis fits into the energy harvesting framework, presenting the development of low-power nodes compliant with the energy autonomy requirement, and sharing common technologies and architectures, but based on different energy sources and sensing mechanisms. The adopted approach is aimed at evaluating multiple aspects of the system in its entirety (i.e., the energy harvesting mechanism, the choice of the harvester, the study of the sensing process, the selection of the electronic devices for processing, acquisition and measurement, the electronic design, the microcontroller unit (MCU) programming techniques), accounting for very challenging constraints as the low amounts of harvested power (i.e., [μW, mW] range), the careful management of the available energy, the coexistence of sensing and radio transmitting features with ultra-low power requirements. Commercial sensors are mainly used to meet the cost-effectiveness and the large-scale reproducibility requirements, however also customized sensors for a specific application (soil moisture measurement), together with appropriate characterization and reading circuits, are also presented. Two different strategies have been pursued which led to the development of two types of sensor nodes, which are referred to as 'sensor tags' and 'self-sufficient sensor nodes'. The first term refers to completely passive sensor nodes without an on-board battery as storage element and which operate only in the presence of the energy source, provisioning energy from it. In this thesis, an RFID (Radio Frequency Identification) sensor tag for soil moisture monitoring powered by the impinging electromagnetic field is presented. The second term identifies sensor nodes equipped with a battery rechargeable through energy scavenging and working as a secondary reserve in case of absence of the primary energy source. In this thesis, quasi-real-time multi-purpose monitoring LoRaWAN nodes harvesting energy from thermoelectricity, diffused solar light, indoor white light, and artificial colored light are presented