Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

On Motion Analysis in Computer Vision with Deep Learning: Selected Case Studies

Motion analysis is one of the essential enabling technologies in computer vision. Despite recent significant advances, image-based motion analysis remains a very challenging problem. This challenge arises because the motion features are extracted directory from a sequence of images without any other meta data information. Extracting motion information (features) is inherently more difficult than in other computer vision disciplines. In a traditional approach, the motion analysis is often formulated as an optimisation problem, with the motion model being hand-crafted to reflect our understanding of the problem domain. The critical element of these traditional methods is a prior assumption about the model of motion believed to represent a specific problem. Data analytics’ recent trend is to replace hand-crafted prior assumptions with a model learned directly from observational data with no, or very limited, prior assumptions about that model. Although known for a long time, these approaches, based on machine learning, have been shown competitive only very recently due to advances in the so-called deep learning methodologies. This work's key aim has been to investigate novel approaches, utilising the deep learning methodologies, for motion analysis where the motion model is learned directly from observed data. These new approaches have focused on investigating the deep network architectures suitable for the effective extraction of spatiotemporal information. Due to the estimated motion parameters' volume and structure, it is frequently difficult or even impossible to obtain relevant ground truth data. Missing ground truth leads to choose the unsupervised learning methodologies which is usually represents challenging choice to utilize in already challenging high dimensional motion representation of the image sequence. The main challenge with unsupervised learning is to evaluate if the algorithm can learn the data model directly from the data only without any prior knowledge presented to the deep learning model during In this project, an emphasis has been put on the unsupervised learning approaches. Owning to a broad spectrum of computer vision problems and applications related to motion analysis, the research reported in the thesis has focused on three specific motion analysis challenges and corresponding practical case studies. These include motion detection and recognition, as well as 2D and 3D motion field estimation. Eyeblinks quantification has been used as a case study for the motion detection and recognition problem. The approach proposed for this problem consists of a novel network architecture processing weakly corresponded images in an action completion regime with learned spatiotemporal image features fused using cascaded recurrent networks. The stereo-vision disparity estimation task has been selected as a case study for the 2D motion field estimation problem. The proposed method directly estimates occlusion maps using novel convolutional neural network architecture that is trained with a custom-designed loss function in an unsupervised manner. The volumetric data registration task has been chosen as a case study for the 3D motion field estimation problem. The proposed solution is based on the 3D CNN, with a novel architecture featuring a Generative Adversarial Network used during training to improve network performance for unseen data. All the proposed networks demonstrated a state-of-the-art performance compared to other corresponding methods reported in the literature on a number of assessment metrics. In particular, the proposed architecture for 3D motion field estimation has shown to outperform the previously reported manual expert-guided registration methodology

Freeform 3D interactions in everyday environments

PhD ThesisPersonal computing is continuously moving away from traditional input using mouse and keyboard, as new input technologies emerge. Recently, natural user interfaces (NUI) have led to interactive systems that are inspired by our physical interactions in the real-world, and focus on enabling dexterous freehand input in 2D or 3D. Another recent trend is Augmented Reality (AR), which follows a similar goal to further reduce the gap between the real and the virtual, but predominately focuses on output, by overlaying virtual information onto a tracked real-world 3D scene. Whilst AR and NUI technologies have been developed for both immersive 3D output as well as seamless 3D input, these have mostly been looked at separately. NUI focuses on sensing the user and enabling new forms of input; AR traditionally focuses on capturing the environment around us and enabling new forms of output that are registered to the real world. The output of NUI systems is mainly presented on a 2D display, while the input technologies for AR experiences, such as data gloves and body-worn motion trackers are often uncomfortable and restricting when interacting in the real world. NUI and AR can be seen as very complimentary, and bringing these two fields together can lead to new user experiences that radically change the way we interact with our everyday environments. The aim of this thesis is to enable real-time, low latency, dexterous input and immersive output without heavily instrumenting the user. The main challenge is to retain and to meaningfully combine the positive qualities that are attributed to both NUI and AR systems. I review work in the intersecting research fields of AR and NUI, and explore freehand 3D interactions with varying degrees of expressiveness, directness and mobility in various physical settings. There a number of technical challenges that arise when designing a mixed NUI/AR system, which I will address is this work: What can we capture, and how? How do we represent the real in the virtual? And how do we physically couple input and output? This is achieved by designing new systems, algorithms, and user experiences that explore the combination of AR and NUI

A study on detection of risk factors of a toddler’s fall injuries using visual dynamic motion cues

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The research in this thesis is intended to aid caregivers’ supervision of toddlers to prevent accidental injuries, especially injuries due to falls in the home environment. There have been very few attempts to develop an automatic system to tackle young children’s accidents despite the fact that they are particularly vulnerable to home accidents and a caregiver cannot give continuous supervision. Vision-based analysis methods have been developed to recognise toddlers’ fall risk factors related to changes in their behaviour or environment. First of all, suggestions to prevent fall events of young children at home were collected from well-known organisations for child safety. A large number of fall records of toddlers who had sought treatment at a hospital were analysed to identify a toddler’s fall risk factors. The factors include clutter being a tripping or slipping hazard on the floor and a toddler moving around or climbing furniture or room structures. The major technical problem in detecting the risk factors is to classify foreground objects into human and non-human, and novel approaches have been proposed for the classification. Unlike most existing studies, which focus on human appearance such as skin colour for human detection, the approaches addressed in this thesis use cues related to dynamic motions. The first cue is based on the fact that there is relative motion between human body parts while typical indoor clutter does not have such parts with diverse motions. In addition, other motion cues are employed to differentiate a human from a pet since a pet also moves its parts diversely. They are angle changes of ellipse fitted to each object and history of its actual heights to capture the various posture changes and different body size of pets. The methods work well as long as foreground regions are correctly segmented

Walking Seven Walks : solitaries, spirit-mediums and matrilineal influence in Lisa Robertson’s poetics of soft architecture

“Walking Seven Walks” comprises a full-length manuscript of poems and an exegesis on Canadian poet Lisa Robertson’s poetics of soft architecture and her millennial poem Seven Walks. Entitled Song of the Year, my creative manuscript presents chronologically the results of a daily urban walking practice undertaken between 2017 and 2022. Informed by Robertson’s poetics of soft architecture, a counter-discipline that recomposes space via a feminist lens, my poems reconsider Charles Olson’s “composition by field” (1950), investigate open-form possibilities for reading poems, and pursue the lyric mode’s capacity for realising the temporary and everchanging constitutions of material space. My exegesis makes a literary-historical argument for Lisa Robertson’s Seven Walks as modelling a collective, feminist practice of urban walking that converses with the hidden labours of previous generations of women and confers prophetic possibilities for world- building. I argue Seven Walks, published between 1999 and 2003, represents what I call the “climate fin de siècle” in Robertson’s deployment of baroque aesthetics and the poem’s registration of late-industrial crises. Chapter One positions Robertson in a Romantic context, arguing that her figuration of the soft architect and guide as plural and gender-fluid walkers ironises Rousseau’s model of the “solitary walker” as depicted in Reveries of a Solitary Walker (1782). I demonstrate that an alternative and collaborative matrilineal genealogy, via the works of Mary Wollstonecraft and Dorothy Wordsworth, informs Robertson’s walking strategy, which registers and reflects on the effects of late-industrial capitalism, including accelerating financial economies and environmental destruction. Chapter Two argues that spirit-medium discourse is a significant line of matrilineal influence in Seven Walks. I analyse two key mediumistic tropes at work in Seven Walks: the figure of the guide and the spatial unit of the room. I argue that the guide opens the text to modes of more-than-human relating. I also argue that Robertson’s use of the room channels the spirit and influence of Virginia Woolf in “A Room of One’s Own” (1929) and beyond

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Variationelle 3D-Rekonstruktion aus Stereobildpaaren und Stereobildfolgen

This work deals with 3D reconstruction and 3D motion estimation from stereo images using variational methods that are based on dense optical flow. In the first part of the thesis, we will investigate a novel application for dense optical flow, namely the estimation of the fundamental matrix of a stereo image pair. By exploiting the high interdependency between the recovered stereo geometry and the established image correspondences, we propose a coupled refinement of the fundamental matrix and the optical flow as a second contribution, thereby improving the accuracy of both. As opposed to many existing techniques, our joint method does not solve for the camera pose and scene structure separately, but recovers them in a single optimisation step. True to our principle of joint optimisation, we further couple the dense 3D reconstruction of the scene to the estimation of its 3D motion in the final part of this thesis. This is achieved by integrating spatial and temporal information from multiple stereo pairs in a novel model for scene flow computation.Diese Arbeit befasst sich mit der 3D Rekonstruktion und der 3D Bewegungsschätzung aus Stereodaten unter Verwendung von Variationsansätzen, die auf dichten Verfahren zur Berechnung des optischen Flusses beruhen. Im ersten Teil der Arbeit untersuchen wir ein neues Anwendungsgebiet von dichtem optischen Fluss, nämlich die Bestimmung der Fundamentalmatrix aus Stereobildpaaren. Indem wir die Abhängigkeit zwischen der geschätzten Stereogeometrie in Form der Fundamentalmatrix und den berechneten Bildkorrespondenzen geeignet ausnutzen, sind wir in der Lage, im zweiten Teil der Arbeit eine gekoppelte Bestimmung der Fundamentalmatrix und des optischen Flusses vorzuschlagen, die zur einer Erhöhung der Genauigkeit beider Schätzungen führt. Im Gegensatz zu vielen existierenden Verfahren berechnet unser gekoppelter Ansatz dabei die Lage der Kameras und die 3D Szenenstruktur nicht einzeln, sondern bestimmt sie in einem einzigen gemeinsamen Optimierungsschritt. Dem Prinzip der gemeinsamen Schätzung weiter folgend koppeln wir im letzten Teil der Arbeit die dichte 3D Rekonstruktion der Szene zusätzlich mit der Bestimmung der zugehörigen 3D Bewegung. Dies wird durch die Intergation von räumlicher und zeitlicher Information aus mehreren Stereobildpaaren in ein neues Modell zur Szenenflussschätzung realisiert

Variationelle 3D-Rekonstruktion aus Stereobildpaaren und Stereobildfolgen

This work deals with 3D reconstruction and 3D motion estimation from stereo images using variational methods that are based on dense optical flow. In the first part of the thesis, we will investigate a novel application for dense optical flow, namely the estimation of the fundamental matrix of a stereo image pair. By exploiting the high interdependency between the recovered stereo geometry and the established image correspondences, we propose a coupled refinement of the fundamental matrix and the optical flow as a second contribution, thereby improving the accuracy of both. As opposed to many existing techniques, our joint method does not solve for the camera pose and scene structure separately, but recovers them in a single optimisation step. True to our principle of joint optimisation, we further couple the dense 3D reconstruction of the scene to the estimation of its 3D motion in the final part of this thesis. This is achieved by integrating spatial and temporal information from multiple stereo pairs in a novel model for scene flow computation.Diese Arbeit befasst sich mit der 3D Rekonstruktion und der 3D Bewegungsschätzung aus Stereodaten unter Verwendung von Variationsansätzen, die auf dichten Verfahren zur Berechnung des optischen Flusses beruhen. Im ersten Teil der Arbeit untersuchen wir ein neues Anwendungsgebiet von dichtem optischen Fluss, nämlich die Bestimmung der Fundamentalmatrix aus Stereobildpaaren. Indem wir die Abhängigkeit zwischen der geschätzten Stereogeometrie in Form der Fundamentalmatrix und den berechneten Bildkorrespondenzen geeignet ausnutzen, sind wir in der Lage, im zweiten Teil der Arbeit eine gekoppelte Bestimmung der Fundamentalmatrix und des optischen Flusses vorzuschlagen, die zur einer Erhöhung der Genauigkeit beider Schätzungen führt. Im Gegensatz zu vielen existierenden Verfahren berechnet unser gekoppelter Ansatz dabei die Lage der Kameras und die 3D Szenenstruktur nicht einzeln, sondern bestimmt sie in einem einzigen gemeinsamen Optimierungsschritt. Dem Prinzip der gemeinsamen Schätzung weiter folgend koppeln wir im letzten Teil der Arbeit die dichte 3D Rekonstruktion der Szene zusätzlich mit der Bestimmung der zugehörigen 3D Bewegung. Dies wird durch die Intergation von räumlicher und zeitlicher Information aus mehreren Stereobildpaaren in ein neues Modell zur Szenenflussschätzung realisiert

Mathematical surfaces models between art and reality

In this paper, I want to document the history of the mathematical surfaces models used for the didactics of pure and applied “High Mathematics” and as art pieces. These models were built between the second half of nineteenth century and the 1930s. I want here also to underline several important links that put in correspondence conception and construction of models with scholars, cultural institutes, specific views of research and didactical studies in mathematical sciences and with the world of the figurative arts furthermore. At the same time the singular beauty of form and colour which the models possessed, aroused the admiration of those entirely ignorant of their mathematical attraction