SELF-IMAGE MULTIMEDIA TECHNOLOGIES FOR FEEDFORWARD OBSERVATIONAL LEARNING

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

2D and 3D computer vision analysis of gaze, gender and age

Human-Computer Interaction (HCI) has been an active research area for over four decades. Research studies and commercial designs in this area have been largely facilitated by the visual modality which brings diversified functionality and improved usability to HCI interfaces by employing various computer vision techniques. This thesis explores a number of facial cues, such as gender, age and gaze, by performing 2D and 3D based computer vision analysis. The ultimate aim is to create a natural HCI strategy that can fulfil user expectations, augment user satisfaction and enrich user experience by understanding user characteristics and behaviours. To this end, salient features have been extracted and analysed from 2D and 3D face representations; 3D reconstruction algorithms and their compatible real-world imaging systems have been investigated; case study HCI systems have been designed to demonstrate the reliability, robustness, and applicability of the proposed method.More specifically, an unsupervised approach has been proposed to localise eye centres in images and videos accurately and efficiently. This is achieved by utilisation of two types of geometric features and eye models, complemented by an iris radius constraint and a selective oriented gradient filter specifically tailored to this modular scheme. This approach resolves challenges such as interfering facial edges, undesirable illumination conditions, head poses, and the presence of facial accessories and makeup. Tested on 3 publicly available databases (the BioID database, the GI4E database and the extended Yale Face Database b), and a self-collected database, this method outperforms all the methods in comparison and thus proves to be highly accurate and robust. Based on this approach, a gaze gesture recognition algorithm has been designed to increase the interactivity of HCI systems by encoding eye saccades into a communication channel similar to the role of hand gestures. As well as analysing eye/gaze data that represent user behaviours and reveal user intentions, this thesis also investigates the automatic recognition of user demographics such as gender and age. The Fisher Vector encoding algorithm is employed to construct visual vocabularies as salient features for gender and age classification. Algorithm evaluations on three publicly available databases (the FERET database, the LFW database and the FRCVv2 database) demonstrate the superior performance of the proposed method in both laboratory and unconstrained environments. In order to achieve enhanced robustness, a two-source photometric stereo method has been introduced to recover surface normals such that more invariant 3D facia features become available that can further boost classification accuracy and robustness. A 2D+3D imaging system has been designed for construction of a self-collected dataset including 2D and 3D facial data. Experiments show that utilisation of 3D facial features can increase gender classification rate by up to 6% (based on the self-collected dataset), and can increase age classification rate by up to 12% (based on the Photoface database). Finally, two case study HCI systems, a gaze gesture based map browser and a directed advertising billboard, have been designed by adopting all the proposed algorithms as well as the fully compatible imaging system. Benefits from the proposed algorithms naturally ensure that the case study systems can possess high robustness to head pose variation and illumination variation; and can achieve excellent real-time performance. Overall, the proposed HCI strategy enabled by reliably recognised facial cues can serve to spawn a wide array of innovative systems and to bring HCI to a more natural and intelligent state

Scalable exploration of highly detailed and annotated 3D models

With the widespread availability of mobile graphics terminals andWebGL-enabled browsers, 3D graphics over the Internet is thriving. Thanks to recent advances in 3D acquisition and modeling systems, high-quality 3D models are becoming increasingly common, and are now potentially available for ubiquitous exploration. In current 3D repositories, such as Blend Swap, 3D Café or Archive3D, 3D models available for download are mostly presented through a few user-selected static images. Online exploration is limited to simple orbiting and/or low-fidelity explorations of simplified models, since photorealistic rendering quality of complex synthetic environments is still hardly achievable within the real-time constraints of interactive applications, especially on on low-powered mobile devices or script-based Internet browsers. Moreover, navigating inside 3D environments, especially on the now pervasive touch devices, is a non-trivial task, and usability is consistently improved by employing assisted navigation controls. In addition, 3D annotations are often used in order to integrate and enhance the visual information by providing spatially coherent contextual information, typically at the expense of introducing visual cluttering. In this thesis, we focus on efficient representations for interactive exploration and understanding of highly detailed 3D meshes on common 3D platforms. For this purpose, we present several approaches exploiting constraints on the data representation for improving the streaming and rendering performance, and camera movement constraints in order to provide scalable navigation methods for interactive exploration of complex 3D environments. Furthermore, we study visualization and interaction techniques to improve the exploration and understanding of complex 3D models by exploiting guided motion control techniques to aid the user in discovering contextual information while avoiding cluttering the visualization. We demonstrate the effectiveness and scalability of our approaches both in large screen museum installations and in mobile devices, by performing interactive exploration of models ranging from 9Mtriangles to 940Mtriangles