What Is Around The Camera?

How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror. An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance.Comment: Accepted to ICCV. Project: http://homes.esat.kuleuven.be/~sgeorgou/multinatillum

Single View Reconstruction for Human Face and Motion with Priors

Single view reconstruction is fundamentally an under-constrained problem. We aim to develop new approaches to model human face and motion with model priors that restrict the space of possible solutions. First, we develop a novel approach to recover the 3D shape from a single view image under challenging conditions, such as large variations in illumination and pose. The problem is addressed by employing the techniques of non-linear manifold embedding and alignment. Specifically, the local image models for each patch of facial images and the local surface models for each patch of 3D shape are learned using a non-linear dimensionality reduction technique, and the correspondences between these local models are then learned by a manifold alignment method. Local models successfully remove the dependency of large training databases for human face modeling. By combining the local shapes, the global shape of a face can be reconstructed directly from a single linear system of equations via least square. Unfortunately, this learning-based approach cannot be successfully applied to the problem of human motion modeling due to the internal and external variations in single view video-based marker-less motion capture. Therefore, we introduce a new model-based approach for capturing human motion using a stream of depth images from a single depth sensor. While a depth sensor provides metric 3D information, using a single sensor, instead of a camera array, results in a view-dependent and incomplete measurement of object motion. We develop a novel two-stage template fitting algorithm that is invariant to subject size and view-point variations, and robust to occlusions. Starting from a known pose, our algorithm first estimates a body configuration through temporal registration, which is used to search the template motion database for a best match. The best match body configuration as well as its corresponding surface mesh model are deformed to fit the input depth map, filling in the part that is occluded from the input and compensating for differences in pose and body-size between the input image and the template. Our approach does not require any makers, user-interaction, or appearance-based tracking. Experiments show that our approaches can achieve good modeling results for human face and motion, and are capable of dealing with variety of challenges in single view reconstruction, e.g., occlusion

What Is Around the Camera?

How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

From motion capture to interactive virtual worlds : towards unconstrained motion-capture algorithms for real-time performance-driven character animation

This dissertation takes performance-driven character animation as a representative application and advances motion capture algorithms and animation methods to meet its high demands. Existing approaches have either coarse resolution and restricted capture volume, require expensive and complex multi-camera systems, or use intrusive suits and controllers. For motion capture, set-up time is reduced using fewer cameras, accuracy is increased despite occlusions and general environments, initialization is automated, and free roaming is enabled by egocentric cameras. For animation, increased robustness enables the use of low-cost sensors input, custom control gesture definition is guided to support novice users, and animation expressiveness is increased. The important contributions are: 1) an analytic and differentiable visibility model for pose optimization under strong occlusions, 2) a volumetric contour model for automatic actor initialization in general scenes, 3) a method to annotate and augment image-pose databases automatically, 4) the utilization of unlabeled examples for character control, and 5) the generalization and disambiguation of cyclical gestures for faithful character animation. In summary, the whole process of human motion capture, processing, and application to animation is advanced. These advances on the state of the art have the potential to improve many interactive applications, within and outside virtual reality.Diese Arbeit befasst sich mit Performance-driven Character Animation, insbesondere werden Motion Capture-Algorithmen entwickelt um den hohen Anforderungen dieser Beispielanwendung gerecht zu werden. Existierende Methoden haben entweder eine geringe Genauigkeit und einen eingeschränkten Aufnahmebereich oder benötigen teure Multi-Kamera-Systeme, oder benutzen störende Controller und spezielle Anzüge. Für Motion Capture wird die Setup-Zeit verkürzt, die Genauigkeit für Verdeckungen und generelle Umgebungen erhöht, die Initialisierung automatisiert, und Bewegungseinschränkung verringert. Für Character Animation wird die Robustheit für ungenaue Sensoren erhöht, Hilfe für benutzerdefinierte Gestendefinition geboten, und die Ausdrucksstärke der Animation verbessert. Die wichtigsten Beiträge sind: 1) ein analytisches und differenzierbares Sichtbarkeitsmodell für Rekonstruktionen unter starken Verdeckungen, 2) ein volumetrisches Konturenmodell für automatische Körpermodellinitialisierung in genereller Umgebung, 3) eine Methode zur automatischen Annotation von Posen und Augmentation von Bildern in großen Datenbanken, 4) das Nutzen von Beispielbewegungen für Character Animation, und 5) die Generalisierung und Übertragung von zyklischen Gesten für genaue Charakteranimation. Es wird der gesamte Prozess erweitert, von Motion Capture bis hin zu Charakteranimation. Die Verbesserungen sind für viele interaktive Anwendungen geeignet, innerhalb und außerhalb von virtueller Realität

From motion capture to interactive virtual worlds : towards unconstrained motion-capture algorithms for real-time performance-driven character animation

This dissertation takes performance-driven character animation as a representative application and advances motion capture algorithms and animation methods to meet its high demands. Existing approaches have either coarse resolution and restricted capture volume, require expensive and complex multi-camera systems, or use intrusive suits and controllers. For motion capture, set-up time is reduced using fewer cameras, accuracy is increased despite occlusions and general environments, initialization is automated, and free roaming is enabled by egocentric cameras. For animation, increased robustness enables the use of low-cost sensors input, custom control gesture definition is guided to support novice users, and animation expressiveness is increased. The important contributions are: 1) an analytic and differentiable visibility model for pose optimization under strong occlusions, 2) a volumetric contour model for automatic actor initialization in general scenes, 3) a method to annotate and augment image-pose databases automatically, 4) the utilization of unlabeled examples for character control, and 5) the generalization and disambiguation of cyclical gestures for faithful character animation. In summary, the whole process of human motion capture, processing, and application to animation is advanced. These advances on the state of the art have the potential to improve many interactive applications, within and outside virtual reality.Diese Arbeit befasst sich mit Performance-driven Character Animation, insbesondere werden Motion Capture-Algorithmen entwickelt um den hohen Anforderungen dieser Beispielanwendung gerecht zu werden. Existierende Methoden haben entweder eine geringe Genauigkeit und einen eingeschränkten Aufnahmebereich oder benötigen teure Multi-Kamera-Systeme, oder benutzen störende Controller und spezielle Anzüge. Für Motion Capture wird die Setup-Zeit verkürzt, die Genauigkeit für Verdeckungen und generelle Umgebungen erhöht, die Initialisierung automatisiert, und Bewegungseinschränkung verringert. Für Character Animation wird die Robustheit für ungenaue Sensoren erhöht, Hilfe für benutzerdefinierte Gestendefinition geboten, und die Ausdrucksstärke der Animation verbessert. Die wichtigsten Beiträge sind: 1) ein analytisches und differenzierbares Sichtbarkeitsmodell für Rekonstruktionen unter starken Verdeckungen, 2) ein volumetrisches Konturenmodell für automatische Körpermodellinitialisierung in genereller Umgebung, 3) eine Methode zur automatischen Annotation von Posen und Augmentation von Bildern in großen Datenbanken, 4) das Nutzen von Beispielbewegungen für Character Animation, und 5) die Generalisierung und Übertragung von zyklischen Gesten für genaue Charakteranimation. Es wird der gesamte Prozess erweitert, von Motion Capture bis hin zu Charakteranimation. Die Verbesserungen sind für viele interaktive Anwendungen geeignet, innerhalb und außerhalb von virtueller Realität