Estimating Appearance Models for Image Segmentation via Tensor Factorization

Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions. Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Foreground region detection and tracking for fixed cameras

For real-time foreground detection on videos, probabilistic modeling for background and foreground colors are widely used. Stauffer and Grimson's model is very successful for foreground segmentation. In this method, each pixel is modeled independently with Gaussian mixtures. Explicit foreground probabilities for pixels are not calculated. Spatial and temporal continuity of pixels are omitted. In this thesis, we obtain foreground probabilities for the pixels using Stauffer and Grimson's model and apply hysteresis thresholding to utilize spatial continuity of pixels. For the same purpose, we also use Markov Random Field modeling and optimizations. To leverage the temporal continuity of pixels, mean-shift tracking is integrated into the segmentation to increase accuracy. Wherever applicable, we combine some of these improvements together. Our work shows that using the probabilistic approach with different enhancements results in much higher segmentation accuracy

Unsupervised Image Segmentation using Markov Random Field Models

. We present an unsupervised segmentation algorithm based on a Markov Random Field model for noisy images. The algorithm finds the the most likely number of classes, their associated model parameters and generates a corresponding segmentation of the image into these classes. This is achieved according to the MAP criterion. To facilitate this, an MCMC algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image. To allow the number of classes to be sampled, a reversible jump is incorporated into the Markov Chain. The jump enables the possible splitting and combining of classes and consequently, their associated regions within the image. Experimental results are presented showing rapid convergence of the algorithm to accurate solutions. 1 Introduction The segmentation of noisy or textured images into a number of different regions comprises a difficult optimisation problem. This is compounded when the number of regions into..

Développement d'une nouvelle approche basée objets pour l'extraction automatique de l'information géographique en milieu urbain à partir des images satellitaires à très haute résolution spatiale

Résumé: L'importance de l'information géographique est indéniable pour des prises de décision efficaces dans le milieu urbain. Toutefois, sa disponibilité n'est pas toujours évidente. Les images satellitaires à très résolution spatiale (THRS) constituent une source intéressante pour l'acquisition de ces informations. Cependant, l'extraction de l'information géographique à partir de ces images reste encore problématique. Elle fait face, d'une part, aux spécificités du milieu urbain et celles des images à THRS et d'autre part, au manque de méthodes d'analyse d'images adéquates. Le but de la présente étude est de développer une nouvelle approche basée objets pour l'extraction automatique de l'information géographique en milieu urbain à partir des images à THRS. L'approche proposée repose sur une analyse d'image basée objets. Deux étapes principales sont identifiées : le passage des pixels aux primitives objets et le passage des primitives aux objets finaux. La première étape est assurée par une nouvelle approche de segmentation multispectrale non paramétrée. Elle se base sur la coopération entre les segmentations par régions et par contours. Elle utilise un critère d'homogénéité spectrale dont le seuil est déterminé d'une manière adaptive et automatique. Le deuxième passage part des primitives objets créées par segmentation. Elle utilise une base de règles floues qui traduisent la connaissance humaine utilisée pour l'interprétation des images. Elles se basent sur les propriétés des objets des classes étudiées. Des connaissances de divers types sont prises en considération (spectrales, texturales, géométriques, contextuelles). Les classes concernées sont : arbre, pelouse, sol nu et eau pour les classes naturelles et bâtiment, route, lot de stationnement pour les classes anthropiques. Des concepts de la théorie de la logique floue et celle des possibilités sont intégrés dans le processus d'extraction. Ils ont permis de gérer la complexité du sujet étudié, de raisonner avec des connaissances imprécises et d'informer sur la précision et la certitude des objets extraits. L'approche basée objets proposée a été appliquée sur des extraits d'images Ikonos et Quickbird. Un taux global de 80 % a été observé. Les taux de bonne extraction trouvés pour les classes bâtiment, route et lots de stationnement sont de l'ordre de 81 %, 75 % et 60 % respectivement. Les résultats atteints sont intéressants du moment que la même base des règles a été utilisée. L'aspect original réside dans le fait que son fonctionnement est totalement automatique et qu'elle ne nécessite ni données auxiliaires ni zones d'entraînement. Tout le long des différentes étapes de l'approche, les paramètres et les seuils nécessaires sont déterminés de manière automatique. L'approche peut être transposable sur d'autres sites d'étude. L'approche proposée dans le cadre de ce travail constitue une solution intéressante pour l'extraction automatique de l'information géographique à partir des images à THRS.||Abstract: The importance of the geographical information is incontestable for efficient decision making in urban environment. But, it is not always available.The very high spatial resolution (VHSR) satellite images constitute an interesting source of this information. However, the extraction of the geographical information from these images is until now problematic.The goal of the present study is to develop a new object-based approach for automatic extraction of geographical information in urban environment from very high spatial resolution images.The proposed approach is object-based image analysis. There are two principal steps: passage of pixels to object primitives and passage of primitives to final objects.The first stage uses a new multispectrale cooperative segmentation approach. Cooperation between region and edge information is exploited. Segments are created with respect to their spectral homogeneity.The threshold is adaptive and its determination is automatic.The second passage leaves from object primitives created by segmentation. Fuzzy rule base is generated from the human knowledge used for image interpretation. Several kinds of object proprieties are integrated (spectral, textural, geometric, and contextual).The concerned classes are trees, grass, bare soil and water as natural classes and building, road, parking lot as man made classes. Fuzzy logic and possibilities theories are integrated in the process of extraction. They permitted to manage the complexity of the studied objects, to reason with imprecise knowledge and to inform on precision and certainty of the extracted objects.The approach has been applied with success on various subsets of Ikonos and Quickbird images.The global extraction accuracy was about 80%.The object-based approach was able to extract buildings, roads and parking lots in urban areas with of 81%, 75% and 60% extraction accuracies respectively.The results are interesting with regard to that the same rule base was used.The original aspect resides in the fact that the approach is completely automatic and no auxiliary data or training areas are required. Along the different stages of the approach, the parameters and the thresholds are determined automatically. This allows the transposability of the approach on others VHRS images.The present approach constitutes an interesting solution for automatic extraction of the geographical information from VHSR satellite images

Correspondencia estereoscópica en imágenes obtenidas con proyección omnidireccional para entornos forestales

Los sistemas de visión estereoscópica se han venido utilizando de forma manual desde hace varias décadas para captar información tridimensional del entorno en diferentes aplicaciones. Con el desarrollo experimentado en los últimos años por las técnicas de procesamiento computacional de imágenes, la visión estereoscópica se viene incorporando cada vez más a sistemas automáticos de diferente naturaleza. El problema central en la automatización de un sistema de visión estereoscópica es la determinación de la correspondencia entre píxeles del par de imágenes estereoscópicas que proceden del mismo punto de la escena tridimensional. El trabajo de investigación desarrollado en esta tesis consiste en el diseño de una estrategia global para dar solución al problema de la correspondencia estereoscópica para un tipo característico de imágenes omnidireccionales procedentes de entornos forestales. Las imágenes son obtenidas mediante un sistema óptico basado en las denominadas lentes de ojo de pez. Este trabajo tiene su origen en el interés suscitado por el Centro de Investigación Forestal (CIFOR) del Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) para automatizar el proceso de extracción de información mediante el dispositivo de medición con número de patente MU-200501738. El interés se centra en obtener dicha información de los troncos de los árboles a partir de imágenes estereoscópicas. Con las medidas obtenidas, los técnicos realizan inventarios forestales que incluyen estudios sobre el volumen de madera, la densidad de árboles, la evolución o crecimiento de éstos, entre otros. La contribución principal de este trabajo consiste en la propuesta de una estrategia que combina los dos procesos esenciales en visión estereoscópica artificial como son la segmentación y correspondencia de ciertas estructuras existentes en las dos imágenes del par estereoscópico. La estrategia se diseña para dos tipos de imágenes procedentes de sendos entornos forestales. El primero de dichos entornos se refiere a pinares de pino silvestre (Pinus sylvestris L.) donde las imágenes se han obtenido en días soleados y por tanto con una alta variabilidad de los niveles de intensidad debido a las zonas iluminadas. En el segundo entorno las imágenes proceden de bosques de roble rebollo (Quercus pyrenaica Willd.) cuya característica más relevante es que se obtienen bajo unas condiciones de iluminación relativamente escasas, días nublados o al amanecer o atardecer, pero suficiente como para producir alto contraste entre los troncos y el cielo. .Debido a las características tan diferentes de ambos entornos, tanto en lo relativo a la iluminación como a la naturaleza de los propios árboles y las texturas que les rodean, los procesos de segmentación y correspondencia se diseñan atendiendo al tipo concreto de entorno forestal. Hecho éste, que marca la tendencia de la futura investigación cuando se analicen otros entornos forestales. En el caso de los bosques de pino, el proceso de segmentación se plantea desde el punto de vista del aislamiento de los troncos mediante la exclusión de las texturas que les rodean (hojas de los pinos, suelo, cielo). Por ello, se proponen técnicas específicas de identificación de texturas para las hojas y de clasificación para el resto. En este último caso se combinan dos técnicas de clasificación clásicas como son el método de Agrupamiento Borroso y el estimador paramétrico Bayesiano. El proceso de correspondencia se plantea en dos fases. En primer lugar se identifican los píxeles homólogos en sendas imágenes del par estereoscópico mediante la adaptación a este problema de las siguientes técnicas procedentes de la teoría general de la decisión: Integral Fuzzy de Choquet, Integral Fuzzy de Sugeno, Teoría Dempster-Shafer y Toma de Decisiones Multicriterio Fuzzy. En segundo lugar, los resultados relativos a la correspondencia obtenidos mediante esas técnicas se procesan para conseguir mejorarlos mediante la adaptación de sendos paradigmas: los Mapas Cognitivos Fuzzy y la Red Neuronal de Hopfield. Para el segundo entorno de bosques de roble, la segmentación se plantea como un proceso de identificación de los troncos de los árboles utilizando técnicas específicas de procesamiento de imágenes, en concreto técnicas de extracción y etiquetado de regiones. Para cada región se obtiene un conjunto de atributos o propiedades que la caracterizan, y el proceso de correspondencia establece las regiones homólogas de las dos imágenes del par estereoscópico mediante medidas de similitud entre los atributos de las regiones. La estrategia propuesta, basada en los procesos de segmentación y correspondencia, se compara favorablemente desde la perspectiva de la automatización del proceso y se plantea para su aplicación a cualquier tipo de entorno forestal, si bien con las pertinentes adaptaciones y modificaciones inherentes a los procesos de segmentación y correspondencia en función de la naturaleza del entorno forestal analizado. [ABSTRACT] Stereoscopic vision systems have been used manually for decades to capture three-dimensional information of the environment in different applications. With the growth experienced in recent years by the techniques of computer image processing, stereoscopic vision has been increasingly incorporating automated systems of different nature. The central problem in the automation of a stereoscopic vision system is the determination of the correspondence between pixels of the pair of stereoscopic images that come from the same point in three-dimensional scene. The research undertaken in this thesis comprises the design of a global strategy to solve the stereoscopic correspondence problem for a specific kind of omnidirectional image from forest environments. The images are obtained through an optical system based on the lens known as fisheye. This work stems from the interest generated by the Forest Research Centre (CIFOR) part of the National Institute for Agriculture and Food Research and Technology (INIA) to automate the process of extracting information through the measurement mechanism with patent number MU-200501738. The focus is on obtaining this information from tree trunks using stereoscopic images. The technicians carry out forest inventories which include studies on wood volume and tree density as well as the evolution and growth of the trees with the measurements obtained. This paper’s main contribution is the proposal for a strategy that combines the two essential processes involved in artificial stereo vision: segmentation and correspondence of certain structures in the dual images of the stereoscopic pair. The strategy is designed for two types of images from two forest environments. The first of these refers to Scots pine forests (Pinus sylvestris L.) where images were obtained on sunny days and therefore exhibit highly variable intensity levels due to the illuminated areas. In the second of these, the images come from Rebollo oak forests (Quercus pyrenaica Willd.), the main characteristic of which is that they are obtained under relatively low light conditions, on cloudy days or at dawn or dusk, but with sufficient light to produce high contrast between the trees and sky. Due to the very different characteristics of each environment - both in terms of light and the nature of trees themselves and textures that surround them - the segmentation and correspondence processes are designed specifically according to the specific type of forest environment. This sets the trend for future research when analyzing other forest environments. In the case of pine forests, the segmentation process is approached from the point of view of isolating the trunks by excluding the textures that surround them (pine needles, the ground, the sky). For this reason, we propose the use of the specific techniques of texture identification for the pine needles and of classification for the rest. The latter case combines two classic classification techniques: Fuzzy Clustering and the Bayesian Parametric estimator. The matching process is set out in two phases. The first identifies the homogeneous pixels in separate stereo pair images, by means of the adaptation of the following techniques from general decision theory to this problem: Choquet’s Fuzzy Integral, Sugeno’s Fuzzy Integral, Dempster-Shafer Theory and Fuzzy Multicriteria Decision Making. Second, the results relating to correspondence obtained by these techniques are enhanced through the adaptation of two separate paradigms, namely: Fuzzy Cognitive Maps and the Hopfield Neural Network. Regarding the second type of forest analyzed, i.e. oak, the segmentation process s designed in order to identify the tree trunks by applying specific techniques in image processing, relating to the extraction and labelling of regions, so that each region is given a set of attributes or properties that characterizes it. The matching process establishes the equivalent regions from the two stereo pair images using measures of similarity among the attributes of the regions. The proposed strategy based on segmentation and correspondence processes can be favourably compared from the perspective of the automation of the process and we suggest it can be applied to any type of forest environment, with the appropriate adaptations inherent to the segmentation and correspondence processes in accordance with the nature of the forest environment analyzed