Automatic gender detection using on-line and off-line information

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors' knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literatur

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Multimodal Character Representation for Visual Story Understanding

Stories are one of the main tools that humans use to make sense of the world around them. This ability is conjectured to be uniquely human, and concepts of agency and interaction have been found to develop during childhood. However, state-of-the-art artificial intelligence models still find it very challenging to represent or understand such information about the world. Over the past few years, there has been a lot of research into building systems that can understand the contents of images, videos, and text. Despite several advances made, computers still struggle to understand high-level discourse structures or how visuals and language are organized to tell a coherent story. Recently, several efforts have been made towards building story understanding benchmarks. As characters are the key component around which the story events unfold, character representations are crucial for deep story understanding such as their names, appearances, and relations to other characters. As a step towards endowing systems with a richer understanding of characters in a given narrative, this thesis develops new techniques that rely on the vision, audio and language channels to address three important challenges: i) speaker recognition and identification, ii) character representation and embedding, and iii) temporal modeling of character relations. We propose a multi-modal unsupervised model for speaker naming in movies, a novel way to represent movie character names in dialogues, and a multi-modal supervised character relation classification model. We also show that our approach improves systems ability to understand narratives, which is measured using several tasks such as their ability to answer questions about stories on several benchmarks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153444/1/mazab_1.pd

Application Of Morphometric Analysis And Tissue Engineering To Bioengineering Personalised Autologous Bone Tissues For The Reconstruction Of Congenital Midface Deformities

Congenital craniofacial bone deformities frequently occur in conditions such as Craniofacial microsomia (CM) and Treacher Collins Syndrome (TCS). Affected children may suffer from functional impairment and poor self-esteem. Reconstruction aims to restore form and function but often involves multiple invasive surgeries throughout childhood. The reliance on foreign-body implants and autologous tissue-transfer is also associated with morbidity. The aim of this work was to assess whether morphometric analysis and tissue engineering using paediatric adipose derived stem cells could facilitate bioengineering personalised autologous facial bone implants to provide a more accurate and life-long solution for the treatment of midface deformities. Paediatric facial CT scans (n=70) from control, CM and TCS subjects were used to build a dense surface model of the midface to study normal and dysmorphic postnatal midface development. This enabled relating of soft and skeletal tissue growth, analysis of asymmetry and evaluation of surgical correction. This work also establishes the foundations for developing a surgical planning tool. Paediatric craniofacial bone was also analysed in order to establish a reference for tissue engineering and reverse engineer the bone microenvironment to fabricate biomaterials and culture conditions that enhance osteogenic maturation. It was possible to bioengineer bone tissue using hADSC cultured on a bone biomimetic hybrid POSS-PCL-Fibrin scaffold. Cellularised scaffolds survived subcutaneous implantation in nude mice for 4 months, underwent vascularisation and showed evidence of mature extracellular matrix formation and cellular composition similar to native bone The results of this work support a multi-faceted approach to bone tissue engineering. Increased understanding of paediatric facial bones permits recreation of the bone microenvironment to enable osteogenic maturation of hADSC. These tissues could eventually be custom-shaped using an operative planning tool based on these computer models. Future work using larger data sets, bioreactors, 3D printing and large animal defect models will seek to build on these promising results

Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun