Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change

Attribute Relationship Analysis in Outlier Mining and Stream Processing

The main theme of this thesis is to unite two important fields of data analysis, outlier mining and attribute relationship analysis. In this work we establish the connection between these two fields. We present techniques which exploit this connection, allowing to improve outlier detection in high dimensional data. In the second part of the thesis we extend our work to the emerging topic of data streams

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems

La percepción como muestreo estocástico en grafos dinámicos

Esta tesis estudia y desarrolla técnicas novedosas que permiten a los robots percibir apropiadamente el entorno de forma autónoma. Para conseguir esto es posible y conveniente usar la información del entorno de la que se disponga. Generalmente, dicha información queda plasmada en el código del robot como construcciones if-then-else difíciles de entender cuando el mundo del robot es considerablemente complejo. Se propone el uso de “Active Grammar-based Modeling” (AGM), una técnica desarrollada dentro de la tesis, que usa descripciones de muy alto nivel que permiten al desarrollador obtener más flexibilidad y escalabilidad, así como reducir el tiempo de desarrollo y la cantidad de errores que se cometen al programar los robots. La solución propuesta pasa por describir la gramática del entorno en un lenguaje específico de dominio que posteriormente se traduce a PDDL, permitiendo usar así planificadores de Inteligencia Artificial clásicos para decidir qué ha de hacer el robot para cumplir sus objetivos y comprobar que las modificaciones que el robot hace al modelo del entorno son válidas de acuerdo a la gramática. Además, AGM permite coordinar fácilmente diferentes filtros de partículas para su ejecución simultánea, pudiendo además elegir distintos filtros de partículas dependiendo del contexto en el que el robot se encuentre, optimizando así el sistema perceptivo de los robots. Además de dicha técnica la tesis presenta diferentes algoritmos usados dentro de AGM, así como varios experimentos relacionados con el modelado activo de entornos de interior usando cámaras RGBD.This thesis develops and studies novel techniques that allow robots to properly model their environments autonomously. For this purpose it is possible and feasible to use all the available information that robots can use. Generally this information results in if-then-else constructs that are hard to understand then the environments of the robots are considerably complex. It is proposed to use “Active Grammar-based Modeling” (AGM), a new technique developed within this thesis. It uses very high-level descriptions that allow developers to achieve higher flexibility and scalability, as well as reducing the development time and the amount of programming errors. The solution consists on describing the grammar of the environment using a domain-specific language that is compiled into PDDL, allowing AGM-based systems to use classic AI planners to decide what robots should do to achieve their goales and incrementally verify that the model generated is valid according to the grammar described. Moreover, AGM can coordinate different particle filters so they can work simultaneously, allowing to choose the most appropriate filters depending on the context. This enhances the accuracy and effectivenes of the perceptual systems of the robots Along AGM, this thesis also presents the different algorithms used by AGM, as well as different experiment related to active indoor modeling using RGBD cameras