Multispectral imaging system for contaminant detection

An automated inspection system for detecting digestive contaminants on food items as they are being processed for consumption includes a conveyor for transporting the food items, a light sealed enclosure which surrounds a portion of the conveyor, with a light source and a multispectral or hyperspectral digital imaging camera disposed within the enclosure. Operation of the conveyor, light source and camera are controlled by a central computer unit. Light reflected by the food items within the enclosure is detected in predetermined wavelength bands, and detected intensity values are analyzed to detect the presence of digestive contamination

Characterization and classification of textures on natural images

The existing texture classification methods are generally based on a parameter extraction stage followed by a classifier stage . Using this kind of method,for an operational application requires to take into account the risk of classes mixture in the parameters space . We propose to take profit of Gagalowicz conjecture in order ta minimise this risk . The conjecture provides us with a set of parameters which totally describe the texture. We show that a connectionnist classifier is able to deal efficiently with these parameters .La plus grande partie des méthodes de classification de textures existantes consiste à alimenter un classifieur par un ensemble de paramètres caractéristiques calculés localement sur l'image texturée. La mise en œuvre de ces méthodes dans le cadre d'applications opérationnelles suppose la prise en compte d'un élément important : le risque de confusion de classes dans l'espace paramétrique. Pour éviter ce problème, nous proposons d'exploiter la conjecture de Gagalowicz [12], qui nous fournit un ensemble de paramètres suffisants pour caractériser totalement la texture. Nous montrons qu'un classifieur connexionniste est capable d'exploiter efficacement ces paramètre

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

The application of a landscape diversity index using remote sensing and geographical information systems to identify degradation patterns in the Great Fish River Valley, Eastern Cape Province, South Africa

Using a range of satellite-derived indices I describe. monitor and predict vegetation conditions that exist in the Great Fish River Valley, Eastern Cape. The heterogeneous nature of the area necessitates that the mapping of vegetation classes be accomplished using a combination of a supervised approach, an unsupervised approach and the use of a Moving Standard Deviation Index (MSDI). Nine vegetation classes are identified and mapped at an accuracy of 84%. The vegetation classes are strongly related to land-use and the communal areas demonstrate a reduction in palatable species and a shift towards dominance by a single species. Nature reserves and commercial rangeland are by contrast dominated by good condition vegetation types. The Modified Soil Adjusted Vegetation Index (MSA VI) is used to map the vegetation production in the study area. The influence of soil reflectance is reduced using this index. The MSA VI proves to be a good predictor of vegetation condition in the higher rainfall areas but not in the more semi-arid regions. The MSA VI has a significant relationship to rainfall but no absolute relationship to biomass. However, a stratification approach (on the basis of vegetation type) reveals that the MSA VI exhibits relationships to biomass in vegetation types occurring in the higher rainfall areas and consisting of a large cover of shrubs. A technique based on an index which describes landscape spatial variability is presented to assist in the interpretation of landscape condition. The research outlines a method for degradation assessment which overcomes many of the problems associated with cost and repeatability. Indices that attempt to provide a correlation with net primary productivity, e.g. NDVI, do not consider changes in the quality of net primary productivity. Landscape variability represents a measure of ecosystem change in the landscape that underlies the degradation process. The hypothesis is that healthy/undisturbed/stable landscapes tend to be less variable and homogenous than their degraded heterogenous counterparts. The Moving Standard Deviation Index (MSDI) is calculated by performing a 3 x 3 moving standard deviation window across Landsat Thematic Mapper (TM) band 3. The result is a sensitive indicator of landscape condition which is not affected by moisture availability and vegetation type. The MSDI shows a significant negative relationship to NDVI confirming its relationship to condition. The cross-classification of MSDI with NDVI allows the identification of invasive woody weeds which exhibit strong photosynthetic signals and would therefore be categorised as good condition using NDVI. Other ecosystems are investigated to determine the relationship between NDVI and MSDI. Where increase in NDVI is disturbance-induced (such as the Kalahari Desert) the relationship is positive. Where high NDVI values are indicative of good condition rangeland (such as the Fish River Valley) the relationship is negative. The MSDI therefore always exhibits a significant positive relationship to degradation irrespective of the relationship of NDVI to condition in the ecosystem

18F-FDG PET/CT in oncology: contribution to the tumor characterization using quantitative analysis of the signal

Revue de la littérature et résultats/présentation de nos 3 études portant sur la charactértisation tumorale par analyse du signal 18F-FDG PET, en particulier de l'analyse de la texture de l'image