Techniques de bas niveau en traitement d'images pour la télédétection des milieux non homogènes

La télédétection vise à acquérir l'information sur des cibles en étudiant leur réponse aux ondes électromagnétiques. Et partout nous rencontrons des milieux non homogènes et des composites. Connaître comment ces milieux non homogènes répondent a la sonde de télédétection est de la plus grande importance pour la praticabilité même de la télédétection. Le comportement macroscopique d'un composite peut séxprimer en fonction des caractéristiques macroscopiques de ses constituants, mais d'une manière complexe incluant la géométrie de leur arrangement. Si nous pouvons obtenir le tenseur diélectrique efficace d'un composé, nous pouvons modéliser sa réponse au champ électromagnétique, et donc sa réponse comme cible de télédétection. La nécessité pour inclure la géométrie détaillée du système d'une façon efficace dans des méthodes numériques, ainsi qu'une équivalence entre les images numériques et les modèles de treillis des composites, suggère le recours aux techniques de bas niveau de traitement d'images numériques. Le cadre de cette thèse est le traitement numérique d'un problème général de télédétection fondée sur le problème électromagnétique d'homogénéisation dans des microstructures. Dans ce contexte, deux techniques de traitement d'images de bas niveau sont présentées, à savoir, une nouvelle méthode pour l'étiquetage des composantes connexes, présentant des améliorations significatives par rapport aux méthodes existantes, et une méthode de codage des configurations locales avec plusieurs caractéristiques la rendant appropriée pour des applications variées. Leurs avantages sont discutés, et des exemples d'application sont fournis au-delà du domaine spécifique étant à leur origine, comme la vision artificielle, le codage d'image, ou encore la synthèse d'image.The aim of remote sensing is obtaining information about targets by studying their response to electromagnetic waves. And everywhere we found non homogeneous media. Knowing how these non homogeneous media respond to the remote sensing probe is of great importance for the very feasibility of remote sensing. The macroscopic behaviour of a composite can be expressed as a function of the macroscopic characteristics of its constituents, but usually in a complex way which includes the geometry of their arrangement. If we are able to obtain the effective permittivity tensor of any given composite, we can model its macroscale response to the electromagnetic field, and therefore its response as a remote sensing target. The necessity of including the detailed geometry of the system in an efficient way in the numerical methods, together with an equivalence between grid models and digital images, suggest the recourse to low level image processing techniques. The framework of this thesis is the numerical treatment of a general problem in remote sensing based on the electromagnetic problem of homogenization of microstructures. In this context, two low level image processing techniques are presented, a new method for the labelling of connected components, with significant advantages over the classical methods, and a local configuration encoding scheme with characteristics which render it useful for different applications. Their advantages and applicability are discussed, together with some examples of application in fields out of the scope of the specific problem which originated them, namely computer vision, image coding, and image synthesis

Machine vision and the OMV

The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described

Patch-based semantic labelling of images.

PhDThe work presented in this thesis is focused at associating a semantics to the content of an image, linking the content to high level semantic categories. The process can take place at two levels: either at image level, towards image categorisation, or at pixel level, in se- mantic segmentation or semantic labelling. To this end, an analysis framework is proposed, and the different steps of part (or patch) extraction, description and probabilistic modelling are detailed. Parts of different nature are used, and one of the contributions is a method to complement information associated to them. Context for parts has to be considered at different scales. Short range pixel dependences are accounted by associating pixels to larger patches. A Conditional Random Field, that is, a probabilistic discriminative graphical model, is used to model medium range dependences between neighbouring patches. Another contribution is an efficient method to consider rich neighbourhoods without having loops in the inference graph. To this end, weak neighbours are introduced, that is, neighbours whose label probability distribution is pre-estimated rather than mutable during the inference. Longer range dependences, that tend to make the inference problem intractable, are addressed as well. A novel descriptor based on local histograms of visual words has been proposed, meant to both complement the feature descriptor of the patches and augment the context awareness in the patch labelling process. Finally, an alternative approach to consider multiple scales in a hierarchical framework based on image pyramids is proposed. An image pyramid is a compositional representation of the image based on hierarchical clustering. All the presented contributions are extensively detailed throughout the thesis, and experimental results performed on publicly available datasets are reported to assess their validity. A critical comparison with the state of the art in this research area is also presented, and the advantage in adopting the proposed improvements are clearly highlighted

Activity Analysis; Finding Explanations for Sets of Events

Automatic activity recognition is the computational process of analysing visual input and reasoning about detections to understand the performed events. In all but the simplest scenarios, an activity involves multiple interleaved events, some related and others independent. The activity in a car park or at a playground would typically include many events. This research assumes the possible events and any constraints between the events can be defined for the given scene. Analysing the activity should thus recognise a complete and consistent set of events; this is referred to as a global explanation of the activity. By seeking a global explanation that satisfies the activity’s constraints, infeasible interpretations can be avoided, and ambiguous observations may be resolved. An activity’s events and any natural constraints are defined using a grammar formalism. Attribute Multiset Grammars (AMG) are chosen because they allow defining hierarchies, as well as attribute rules and constraints. When used for recognition, detectors are employed to gather a set of detections. Parsing the set of detections by the AMG provides a global explanation. To find the best parse tree given a set of detections, a Bayesian network models the probability distribution over the space of possible parse trees. Heuristic and exhaustive search techniques are proposed to find the maximum a posteriori global explanation. The framework is tested for two activities: the activity in a bicycle rack, and around a building entrance. The first case study involves people locking bicycles onto a bicycle rack and picking them up later. The best global explanation for all detections gathered during the day resolves local ambiguities from occlusion or clutter. Intensive testing on 5 full days proved global analysis achieves higher recognition rates. The second case study tracks people and any objects they are carrying as they enter and exit a building entrance. A complete sequence of the person entering and exiting multiple times is recovered by the global explanation

Digital Morphometry : A Taxonomy Of Morphological Filters And Feature Parameters With Application To Alzheimer\u27s Disease Research

In this thesis the expression digital morphometry collectively describes all those procedures used to obtain quantitative measurements of objects within a two-dimensional digital image. Quantitative measurement is a two-step process: the application of geometrical transformations to extract the features of interest, and then the actual measurement of these features. With regard to the first step the morphological filters of mathematical morphology provide a wealth of suitable geometric transfomations. Traditional radiometric and spatial enhancement techniques provide an additional source of transformations. The second step is more classical (e.g. Underwood, 1970; Bookstein, 1978; and Weibull, 1980); yet here again mathematical morphology is applicable - morphologically derived feature parameters. This thesis focuses on mathematical morphology for digital morphometry. In particular it proffers a taxonomy of morphological filters and investigates the morphologically derived feature parameters (Minkowski functionals) for digital images sampled on a square grid. As originally conceived by Georges Matheron, mathematical morphology concerns the analysis of binary images by means of probing with structuring elements [typically convex geometric shapes] (Dougherty, 1993, preface). Since its inception the theory has been extended to grey-level images and most recently to complete lattices. It is within the very general framework of the complete lattice that the taxonomy of morphological filters is presented. Examples are provided to help illustrate the behaviour of each type of filter. This thesis also introduces DIMPAL (Mehnert, 1994) - a PC-based image processing and analysis language suitable for researching and developing algorithms for a wide range of image processing applications. Though DIMPAL was used to produce the majority of the images in this thesis it was principally written to provide an environment in which to investigate the application of mathematical morphology to Alzheimer\u27s disease research. Alzheimer\u27s disease is a form of progressive dementia associated with the degeneration of the brain. It is the commonest type of dementia and probably accounts for half the dementia of old age (Forsythe, 1990, p. 21 ). Post mortem examination of the brain reveals the presence of characteristic neuropathologic lesions; namely neuritic plaques and neurofibrillary tangles. They occur predominantly in the cerebral cortex and hippocampus. Quantitative studies of the distribution of plaques and tangles in normally aged and Alzheimer brains are hampered by the enormous amount of time and effort required to count and measure these lesions. Here in a morphological algorithm is proposed for the automatic segmentation and measurement of neuritic plaques from light micrographs of post mortem brain tissue