The MORFO3D foot database

The final publication is available at Springer via http://dx.doi.org/10.1007/11492542_80A foot database comprising 3D foot shapes and footwear fitting reports of more than 300 participants is presented. It was primarily acquired to study footwear fitting, though it can also be used to analyse anatomical features of the foot. In fact, we present a technique for automatic detection of several foot anatomical landmarks, together with some empirical results.Work supported by the “Agència Valenciana de Ciència i Tecnologia” under grant GRUPOS03/031 and the Spanish projects DPI2001-0880-CO2-01 and DPI2001-0880-CO2-02.García Hernández, J.; Heras Barberá, SM.; Juan Císcar, A.; Paredes Palacios, R.; Nácher Rodríguez, B.; Alemany, S.; Alcántara, E.... (2005). The MORFO3D foot database. En Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II. Springer Verlag (Germany). 658-665. https://doi.org/10.1007/11492542_80S658665I-ware laboratory, http://www.i-ware.co.jp/Goonetilleke, R.S., Luximon, A.: Designing for comfort: a footwear application. In: Proceedings of the Computer-Aided Ergonomics and Safety Conference 2001, July 28-August 2 (2001) Plenary session paperNacher, B., Alcántara, E., Alemany, S., García-Hernández, J., Juan, A.: 3d foot digitalizing and its application to footwear fitting. In: Proc. of 3D Modelling (2004

Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning

The final publication is available at Springer via http://dx.doi.org/10.1007/11492542_6Proceedings of Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Part IIA natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noisy patterns that belonging to one class are labelled as members of another. This allows classifier construction to focus on borderline patterns, likely to be the most informative ones. To appropriately define the above subsets, in this work we will use as base classifiers the so–called parallel perceptrons, a novel approach to committee machine training that allows, among other things, to naturally define margins for hidden unit activations. We shall use these margins to define the above pattern types and to iteratively perform subsample selections in an initial training set that enhance classification accuracy and allow for a balanced classifier performance even when class sizes are greatly different.With partial support of Spain’s CICyT, TIC 01–572, TIN2004–0767

Learning to Recover Spectral Reflectance from RGB Images

This paper tackles spectral reflectance recovery (SRR) from RGB images. Since capturing ground-truth spectral reflectance and camera spectral sensitivity are challenging and costly, most existing approaches are trained on synthetic images and utilize the same parameters for all unseen testing images, which are suboptimal especially when the trained models are tested on real images because they never exploit the internal information of the testing images. To address this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy that fine-tunes the well-trained network parameters with each testing image to combine external with internal information. To the best of our knowledge, this is the first work that successfully adapts the MAXL strategy to this problem. Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis. Besides, since the spectral reflectance of a scene is independent to its illumination while the corresponding RGB images are not, we recover the spectral reflectance of a scene from its RGB images captured under multiple illuminations to further reduce the unknown. Qualitative and quantitative evaluations demonstrate the effectiveness of our proposed network and of the MAXL. Our code and data are available at https://github.com/Dong-Huo/SRR-MAXL

2D Phase Unwrapping via Graph Cuts

Phase imaging technologies such as interferometric synthetic aperture radar (InSAR), magnetic resonance imaging (MRI), or optical interferometry, are nowadays widespread and with an increasing usage. The so-called phase unwrapping, which consists in the in- ference of the absolute phase from the modulo-2π phase, is a critical step in many of their processing chains, yet still one of its most challenging problems. We introduce an en- ergy minimization based approach to 2D phase unwrapping. In this approach we address the problem by adopting a Bayesian point of view and a Markov random field (MRF) to model the phase. The maximum a posteriori estimation of the absolute phase gives rise to an integer optimization problem, for which we introduce a family of efficient algo- rithms based on existing graph cuts techniques. We term our approach and algorithms PUMA, for Phase Unwrapping MAx flow. As long as the prior potential of the MRF is convex, PUMA guarantees an exact global solution. In particular it solves exactly all the minimum L p norm (p ≥ 1) phase unwrapping problems, unifying in that sense, a set of existing independent algorithms. For non convex potentials we introduce a version of PUMA that, while yielding only approximate solutions, gives very useful phase unwrap- ping results. The main characteristic of the introduced solutions is the ability to blindly preserve discontinuities. Extending the previous versions of PUMA, we tackle denoising by exploiting a multi-precision idea, which allows us to use the same rationale both for phase unwrapping and denoising. Finally, the last presented version of PUMA uses a frequency diversity concept to unwrap phase images having large phase rates. A representative set of experiences illustrates the performance of PUMA

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

Towards streaming gesture recognition

The emergence of low-cost sensors allows more devices to be equipped with various types of sensors. In this way, mobile device such as smartphones or smartwatches now may contain accelerometers, gyroscopes, etc. This offers new possibilities for interacting with the environment and benefits would come to exploit these sensors. As a consequence, the literature on gesture recognition systems that employ such sensors grow considerably. The literature regarding online gesture recognition counts many methods based on Dynamic Time Warping (DTW). However, this method was demonstrated has non-efficient for time series from inertial sensors unit as a lot of noise is present. In this way new methods based on LCSS (Longest Common SubSequence) were introduced. Nevertheless, none of them focus on a class optimization process. In this master thesis, we present and evaluate a new algorithm for online gesture recognition in noisy streams. This technique relies upon the LM-WLCSS (Limited Memory and Warping LCSS) algorithm that has demonstrated its efficiency on gesture recognition. This new method involves a quantization step (via the K-Means clustering algorithm) that transforms new data to a finite set. In this way, each new sample can be compared to several templates (one per class). Gestures are rejected based on a previously trained rejection threshold. Thereafter, an algorithm, called SearchMax, find a local maximum within a sliding window and output whether or not the gesture has been recognized. In order to resolve conflicts that may occur, another classifier (i.e. C4.5) could be completed. As the K-Means clustering algorithm needs to be initialized with the number of clusters to create, we also introduce a straightforward optimization process. Such an operation also optimizes the window size for the SearchMax algorithm. In order to demonstrate the robustness of our algorithm, an experiment has been performed over two different data sets. However, results on tested data sets are only accurate when training data are used as test data. This may be due to the fact that the method is in an overlearning state. L’apparition de nouveaux capteurs à bas prix a permis d’en équiper dans beaucoup plus d’appareils. En effet, dans les appareils mobiles tels que les téléphones et les montres intelligentes nous retrouvons des accéléromètres, gyroscopes, etc. Ces capteurs présents dans notre vie quotidienne offrent de toutes nouvelles possibilités en matière d’interaction avec notre environnement et il serait avantageux de les utiliser. Cela a eu pour conséquence une augmentation considérable du nombre de recherches dans le domaine de reconnaissance de geste basé sur ce type de capteur. La littérature concernant la reconnaissance de gestes en ligne comptabilise beaucoup de méthodes qui se basent sur Dynamic Time Warping (DTW). Cependant, il a été démontré que cette méthode se révèle inefficace en ce qui concerne les séries temporelles provenant d’une centrale à inertie puisqu’elles contiennent beaucoup de bruit. En ce sens de nouvelles méthodes basées sur LCSS (Longest Common SubSequence) sont apparues. Néanmoins, aucune d’entre elles ne s’est focalisée sur un processus d’optimisation par class. Ce mémoire de maîtrise consiste en une présentation et une évaluation d’un nouvel algorithme pour la reconnaissance de geste en ligne avec des données bruitées. Cette technique repose sur l’algorithme LM-WLCSS (Limited Memory and Warping LCSS) qui a d’ores et déjà démontré son efficacité quant à la reconnaissance de geste. Cette nouvelle méthode est donc composée d’une étape dite de quantification (grâce à l’algorithme de regroupement K-Means) qui se charge de convertir les nouvelles données entrantes vers un ensemble de données fini. Chaque nouvelle donnée peut donc être comparée à plusieurs motifs (un par classe) et un geste est reconnu dès lors que son score dépasse un seuil préalablement entrainé. Puis, un autre algorithme appelé SearchMax se charge de trouver un maximum local au sein d’une fenêtre glissant afin de préciser si oui ou non un geste a été reconnu. Cependant des conflits peuvent survenir et en ce sens un autre classifieur (c.-àd. C4.5) est chainé. Étant donné que l’algorithme de regroupement K-Means a besoin d’une valeur pour le nombre de regroupements à faire, nous introduisons également une technique simple d’optimisation à ce sujet. Cette partie d’optimisation se charge également de trouver la meilleure taille de fenêtre possible pour l’algorithme SearchMax. Afin de démontrer l’efficacité et la robustesse de notre algorithme, nous l’avons testé sur deux ensembles de données différents. Cependant, les résultats sur les ensembles de données testées n’étaient bons que lorsque les données d’entrainement étaient utilisées en tant que données de test. Cela peut être dû au fait que la méthode est dans un état de surapprentissage

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

A fuzzy probabilistic inference methodology for constrained 3D human motion classification

Enormous uncertainties in unconstrained human motions lead to a fundamental challenge that many recognising algorithms have to face in practice: efficient and correct motion recognition is a demanding task, especially when human kinematic motions are subject to variations of execution in the spatial and temporal domains, heavily overlap with each other,and are occluded. Due to the lack of a good solution to these problems, many existing methods tend to be either effective but computationally intensive or efficient but vulnerable to misclassification. This thesis presents a novel inference engine for recognising occluded 3D human motion assisted by the recognition context. First, uncertainties are wrapped into a fuzzy membership function via a novel method called Fuzzy Quantile Generation which employs metrics derived from the probabilistic quantile function. Then, time-dependent and context-aware rules are produced via a genetic programming to smooth the qualitative outputs represented by fuzzy membership functions. Finally, occlusion in motion recognition is taken care of by introducing new procedures for feature selection and feature reconstruction. Experimental results demonstrate the effectiveness of the proposed framework on motion capture data from real boxers in terms of fuzzy membership generation, context-aware rule generation, and motion occlusion. Future work might involve the extension of Fuzzy Quantile Generation in order to automate the choice of a probability distribution, the enhancement of temporal pattern recognition with probabilistic paradigms, the optimisation of the occlusion module, and the adaptation of the present framework to different application domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo