Object detection and activity recognition in digital image and video libraries

This thesis is a comprehensive study of object-based image and video retrieval, specifically for car and human detection and activity recognition purposes. The thesis focuses on the problem of connecting low level features to high level semantics by developing relational object and activity presentations. With the rapid growth of multimedia information in forms of digital image and video libraries, there is an increasing need for intelligent database management tools. The traditional text based query systems based on manual annotation process are impractical for today\u27s large libraries requiring an efficient information retrieval system. For this purpose, a hierarchical information retrieval system is proposed where shape, color and motion characteristics of objects of interest are captured in compressed and uncompressed domains. The proposed retrieval method provides object detection and activity recognition at different resolution levels from low complexity to low false rates. The thesis first examines extraction of low level features from images and videos using intensity, color and motion of pixels and blocks. Local consistency based on these features and geometrical characteristics of the regions is used to group object parts. The problem of managing the segmentation process is solved by a new approach that uses object based knowledge in order to group the regions according to a global consistency. A new model-based segmentation algorithm is introduced that uses a feedback from relational representation of the object. The selected unary and binary attributes are further extended for application specific algorithms. Object detection is achieved by matching the relational graphs of objects with the reference model. The major advantages of the algorithm can be summarized as improving the object extraction by reducing the dependence on the low level segmentation process and combining the boundary and region properties. The thesis then addresses the problem of object detection and activity recognition in compressed domain in order to reduce computational complexity. New algorithms for object detection and activity recognition in JPEG images and MPEG videos are developed. It is shown that significant information can be obtained from the compressed domain in order to connect to high level semantics. Since our aim is to retrieve information from images and videos compressed using standard algorithms such as JPEG and MPEG, our approach differentiates from previous compressed domain object detection techniques where the compression algorithms are governed by characteristics of object of interest to be retrieved. An algorithm is developed using the principal component analysis of MPEG motion vectors to detect the human activities; namely, walking, running, and kicking. Object detection in JPEG compressed still images and MPEG I frames is achieved by using DC-DCT coefficients of the luminance and chrominance values in the graph based object detection algorithm. The thesis finally addresses the problem of object detection in lower resolution and monochrome images. Specifically, it is demonstrated that the structural information of human silhouettes can be captured from AC-DCT coefficients

Lower Rotational Inertia and Larger Leg Muscles Indicate More Rapid Turns in Tyrannosaurids Than in Other Large Theropods

Synopsis: Tyrannosaurid dinosaurs had large preserved leg muscle attachments and low rotational inertia relative to their body mass, indicating that they could turn more quickly than other large theropods. Methods: To compare turning capability in theropods, we regressed agility estimates against body mass, incorporating superellipse-based modeled mass, centers of mass, and rotational inertia (mass moment of inertia). Muscle force relative to body mass is a direct correlate of agility in humans, and torque gives potential angular acceleration. Agility scores therefore include rotational inertia values divided by proxies for (1) muscle force (ilium area and estimates of m. caudofemoralis longus cross-section), and (2) musculoskeletal torque. Phylogenetic ANCOVA (phylANCOVA) allow assessment of differences in agility between tyrannosaurids and non-tyrannosaurid theropods (accounting for both ontogeny and phylogeny). We applied conditional error probabilities a(p) to stringently test the null hypothesis of equal agility. Results: Tyrannosaurids consistently have agility index magnitudes twice those of allosauroids and some other theropods of equivalent mass, turning the body with both legs planted or pivoting over a stance leg. PhylANCOVA demonstrates definitively greater agilities in tyrannosaurids, and phylogeny explains nearly all covariance. Mass property results are consistent with those of other studies based on skeletal mounts, and between different figure-based methods (our main mathematical slicing procedures, lofted 3D computer models, and simplified graphical double integration). Implications: The capacity for relatively rapid turns in tyrannosaurids is ecologically intriguing in light of their monopolization of large (\u3e400 kg), toothed dinosaurian predator niches in their habitats

Rib-reinforced Shell Structure

Shell structures are extensively used in engineering due to their efficient load-carrying capacity relative to material volume. However, large-span shells require additional supporting structures to strengthen fragile regions. The problem of designing optimal stiffeners is therefore becoming a major challenge for shell applications. To address it, we propose a computational framework to design and optimize rib layout on arbitrary shell to improve the overall structural stiffness and mechanical performance. The essential of our method is to place ribs along the principal stress lines which reflect paths of material continuity and indicates trajectories of internal forces. Given a surface and user-specified external loads, we perform a Finite Element Analysis. Using the resulting principal stress field, we generate a quad-mesh whose edges align with this cross field. Then we extract an initial rib network from the quad-mesh. After simplifying rib network by removing ribs with little contribution, we perform a rib flow optimization which allows ribs to swing on surface to further adjust rib distribution. Finally, we optimize rib cross-section to maximally reduce material usage while achieving certain structural stiffness requirements. We demonstrate that our rib-reinforced shell structures achieve good static performances. And experimental results by 3D printed objects show the effectiveness of our method

Methods for modeling the dynamic mass flows in a large two-stroke diesel engine with EGR

In this project different methods to model two-stroke diesel engines have been investigated. The goal of the project is to obtain a model capable to capture both the steady states and the transients of the engine system. The interest MAN Diesel&Turbo has in this model is to obtain a reliable simulation platform to be used as a tool to evaluate the performance of new control systems for the Exhaust Gas Recirculation (EGR) system. A non-linear model of the engine air-path with EGR is derived and validated against measurements. The specific engine corresponds to a 4T50ME-X located at MAN Diesel&Turbo research center. The model consists of the following components: the turbocharger, the scavenging and exhaust manifolds, the cylinders and the EGR system. The turbocharger model is externalized to an advance simulator software (GT-Power), all other components are modeled in Simulink. A Seiliger cycle capable of handling variable exhaust valve opening and closing is proposed. The polytropic coefficients for the compression and expansion are analytically estimated from in-cylinder pressure measurements. The model derived in this project is capable to fit well the measured data in steady states. The appropriate dynamics are obtained in transient operations, although the model shows a generalized faster response than the measured data.Outgoin

Segmentation automatique d'images sur des critères géométriques, application à l'inspection visuelle de produits agroalimentaires

À l'échelle mondiale, la récolte de céréales atteint plusieurs milliards de tonnes chaque année. Les producteurs céréaliers échangent leurs récoltes selon un prix déterminé par la qualité de leur production. Cette évaluation, appelée agréage, est réalisée pour chaque lot sur un échantillon jugé représentatif. La difficulté au cours de cette évaluation est de parvenir à parfaitement caractériser l'échantillon. Il faut pour cela qualifier chacun de ses éléments, en d'autres termes, il est nécessaire d'évaluer chaque grain de céréale de manière individuelle. Cette opération est historiquement réalisée par un opérateur qui isole chaque grain manuellement pour l'inspecter et l'évaluer. Ce procédé est exposé à différents problèmes : d'une part les résultats obtenus par un opérateur ne sont pas parfaitement répétables : son état de fatigue visuelle peut influencer son appréciation ; d'autre part l'évaluation dépend de l'opérateur : elle n'est pas reproductible, les résultats peuvent varier d'un opérateur à l'autre. Cette thèse a donc pour but de mettre au point un système capable de réaliser cette inspection visuelle. Le système d'acquisition est présenté dans un premier temps. Cette enceinte contient les dispositifs d'éclairage et d'acquisition d'images. Différents outils ont été mis en œuvre pour s'assurer de la justesse et de la stabilité des acquisitions. Une méthode d'apprentissage de modèles de forme est ensuite présentée : elle a pour but de définir et de modéliser le type de forme des grains de l'application considérée (blé, riz, orge). Cette étape est réalisée sur une image d'objets isolés. Deux méthodes de détection sont ensuite présentées : une approche déterministe et une approche probabiliste. Ces deux méthodes, mises au point pour segmenter les objets d'une image, utilisent des outils communs bien qu'elles soient conçues différemment. Les résultats obtenus et présentés dans cette thèse démontrent la capacité du système automatique à se positionner comme une solution fiable à la problématique d'inspection visuelle de grains de céréales.In agriculture, the global grain harvest reached several billion tons each year. Cereal producers exchange their crops at a price determined by the quality of their production. This assessment, called grading, is performed for each set on a representative sample. The difficulty of this assessment is to fully characterize the sample. To do so, it is necessary to qualify each of its elements. In other words, it is necessary to evaluate each individual cereal grain. Historically, this has been performed manually by an operator who isolates each evaluated grain. This method is exposed to various problems: firstly, results obtained by an operator are not perfectly repeatable. For example, eyestrain can influence the assessment. On the other hand the evaluation depends on the operator: it is not reproducible. The results can vary from one operator to another. The aim of this thesis is to develop a system that can handle this visual inspection. In a first time, the acquisition system is introduced. Image acquisition and lighting parts are placed in a cabin. Several methods have been introduced to manage accuracy and stability of the acquisitions. Then, a shape model learning is detailed: this step, based on an image with manually separated objects, defines and modelizes shape of the considered cereal grains (wheat, rice, barley). Two detection approaches are then introduced: a deterministic method and a probabilistic one. Both are based on the same tools to process the objects segmentation of an image, but they deal with the question in a different way. The results provided by the system and presented in this thesis emphasize the ability of this automatic system to process the visual inspection of food products

Fitting superellipses to incomplete contours

Affine invariant regions have proved a powerful feature for object recognition and categorization. These features heavily rely on object textures rather than shapes, however. Typically, their shapes have been fixed to ellipses or parallelograms. The paper proposes a novel affine invariant region type, that is built up from a combination of fitted superellipses. These novel features have the advantage of offering a much wider range of shapes through the addition of a very limited number of shape parameters, with the traditional ellipses and parallelograms as subsets. The paper offers a solution for the robust fitting of superellipses to partial contours, which is a crucial step towards the implementation of the novel features.Proceedings IEEE workshop on perceptual organization in computer vision - POCV '04, 8 pp., June 28, 2004, Washington, DC, USAstatus: publishe