9 research outputs found
A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data
Biological movement control and planning is based upon motor primitives. In our approach, we presume that each motor primitive takes responsibility for controlling a small sub-block of motion, containing coherent muscle activation outputs. A central timing controller cues these subroutines of movement, creating complete movement strategies that are built up by overlaying primitives, thus creating synergies of muscle activation. This partitioning allows the movement to be defined by a sparse code representing the timing of primitive activations. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. The variation in the handwriting data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. The model is naturally partitioned into a low level primitive output stage, and a top-down primitive timing stage. This partitioning gives us an insight into behaviours such as scribbling, and what is learnt in order to write a new character.
Signature features with the visibility transformation
The signature in rough path theory provides a graduated summary of a path
through an examination of the effects of its increments. Inspired by recent
developments of signature features in the context of machine learning, we
explore a transformation that is able to embed the effect of the absolute
position of the data stream into signature features. This unified feature is
particularly effective for its simplifying role in allowing the signature
feature set to accommodate nonlinear functions of absolute and relative values
Extracting Motion Primitives from Natural Handwriting Data
Institute for Adaptive and Neural ComputationHumans and animals can plan and execute movements much more adaptably and
reliably than current computers can calculate robotic limb trajectories. Over recent
decades, it has been suggested that our brains use motor primitives as blocks
to build up movements. In broad terms a primitive is a segment of pre-optimised
movement allowing a simplified movement planning solution. This thesis explores
a generative model of handwriting based upon the concept of motor primitives.
Unlike most primitive extraction studies, the primitives here are time extended
blocks that are superimposed with character specific offsets to create a pen trajectory.
This thesis shows how handwriting can be represented using a simple fixed
function superposition model, where the variation in the handwriting arises from
timing variation in the onset of the functions. Furthermore, it is shown how handwriting
style variations could be due to primitive function differences between individuals,
and how the timing code could provide a style invariant representation
of the handwriting. The spike timing representation of the pen movements provides
an extremely compact code, which could resemble internal spiking neural
representations in the brain. The model proposes an novel way to infer primitives
in data, and the proposed formalised probabilistic model allows informative priors
to be introduced providing a more accurate inference of primitive shape and
timing
Représentations parcimonieuses pour les signaux multivariés
Dans cette thèse, nous étudions les méthodes d'approximation et d'apprentissage qui fournissent des représentations parcimonieuses. Ces méthodes permettent d'analyser des bases de données très redondantes à l'aide de dictionnaires d'atomes appris. Etant adaptés aux données étudiées, ils sont plus performants en qualité de représentation que les dictionnaires classiques dont les atomes sont définis analytiquement. Nous considérons plus particulièrement des signaux multivariés résultant de l'acquisition simultanée de plusieurs grandeurs, comme les signaux EEG ou les signaux de mouvements 2D et 3D. Nous étendons les méthodes de représentations parcimonieuses au modèle multivarié, pour prendre en compte les interactions entre les différentes composantes acquises simultanément. Ce modèle est plus flexible que l'habituel modèle multicanal qui impose une hypothèse de rang 1. Nous étudions des modèles de représentations invariantes : invariance par translation temporelle, invariance par rotation, etc. En ajoutant des degrés de liberté supplémentaires, chaque noyau est potentiellement démultiplié en une famille d'atomes, translatés à tous les échantillons, tournés dans toutes les orientations, etc. Ainsi, un dictionnaire de noyaux invariants génère un dictionnaire d'atomes très redondant, et donc idéal pour représenter les données étudiées redondantes. Toutes ces invariances nécessitent la mise en place de méthodes adaptées à ces modèles. L'invariance par translation temporelle est une propriété incontournable pour l'étude de signaux temporels ayant une variabilité temporelle naturelle. Dans le cas de l'invariance par rotation 2D et 3D, nous constatons l'efficacité de l'approche non-orientée sur celle orientée, même dans le cas où les données ne sont pas tournées. En effet, le modèle non-orienté permet de détecter les invariants des données et assure la robustesse à la rotation quand les données tournent. Nous constatons aussi la reproductibilité des décompositions parcimonieuses sur un dictionnaire appris. Cette propriété générative s'explique par le fait que l'apprentissage de dictionnaire est une généralisation des K-means. D'autre part, nos représentations possèdent de nombreuses invariances, ce qui est idéal pour faire de la classification. Nous étudions donc comment effectuer une classification adaptée au modèle d'invariance par translation, en utilisant des fonctions de groupement consistantes par translation.In this thesis, we study approximation and learning methods which provide sparse representations. These methods allow to analyze very redundant data-bases thanks to learned atoms dictionaries. Being adapted to studied data, they are more efficient in representation quality than classical dictionaries with atoms defined analytically. We consider more particularly multivariate signals coming from the simultaneous acquisition of several quantities, as EEG signals or 2D and 3D motion signals. We extend sparse representation methods to the multivariate model, to take into account interactions between the different components acquired simultaneously. This model is more flexible that the common multichannel one which imposes a hypothesis of rank 1. We study models of invariant representations: invariance to temporal shift, invariance to rotation, etc. Adding supplementary degrees of freedom, each kernel is potentially replicated in an atoms family, translated at all samples, rotated at all orientations, etc. So, a dictionary of invariant kernels generates a very redundant atoms dictionary, thus ideal to represent the redundant studied data. All these invariances require methods adapted to these models. Temporal shift-invariance is an essential property for the study of temporal signals having a natural temporal variability. In the 2D and 3D rotation invariant case, we observe the efficiency of the non-oriented approach over the oriented one, even when data are not revolved. Indeed, the non-oriented model allows to detect data invariants and assures the robustness to rotation when data are revolved. We also observe the reproducibility of the sparse decompositions on a learned dictionary. This generative property is due to the fact that dictionary learning is a generalization of K-means. Moreover, our representations have many invariances that is ideal to make classification. We thus study how to perform a classification adapted to the shift-invariant model, using shift-consistent pooling functions.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF
Entwicklung von Methoden zur Unterscheidung und Interpretation von Bewegungsmustern in dynamischen Szenen
Im Forschungsfeld der mobilen Assistenzroboter spielen Bewegungsabläufe eine zunehmend wichtige Rolle. Gerade in den Bewegungen der mit dem Assistenzroboter handelnden Person verstecken sich eine ganze Reihe Informationen, die zur Verbesserung der Interaktion herangezogen werden können. Eine wichtige Fragestellung bezüglich der Analyse von Bewegungen stellt die Repräsentation der Bewegungstrajektorien dar. Außerdem muss geklärt werden, welche Ähnlichkeitsmaße in den komplexeren Verfahren zum Einsatz
kommen können bzw. welche speziellen Anforderungen sie erfüllen müssen. Den Kern der Arbeit stellen drei Verfahren dar, die im Wesentlichen den weiteren Verlauf einer beobachteten Bewegung über einen längeren Zeitraum vorhersagen können. Dabei handelt es sich um Echo State Netzwerke, Local Models und die spatio-temporale nicht-negative Matrixfaktorisierung (NMF). Die Arbeit als Ganzes versteht sich als einer der ersten Schritte zur systematischen Untersuchung von Bewegungsabläufen. Mit dieser Arbeit soll ein Entwickler in der Lage sein, aus einer breiten Palette an Werkzeugen sich für das
Richtige für seinen speziellen Anwendungsfall zu entscheiden
Cognitive design. Creating the sets of categories and labels that structure our shared experience
A Dissertation submitted to the
Graduate School — New Brunswick
Rutgers, The State University of New Jersey
in partial fulfillment of the requirements
for the degree of
Doctor of PhilosophyFollowing in the tradition of studies of categorization in everyday life, this dissertation
focuses on the specific case of sets of categories. The concept of the "contrast set," developed
by cognitive anthropologists in the 1950s, is the central focus of analysis. Canonical examples
of everyday life contrast sets include alphabets, identification numbers, standard pitches,
and the elements of geographical categorizations. This dissertation focuses on the design
issues surrounding the deliberate, conscious construction of such sets (rather than on
contrast sets which are natural or emergent). The chapters focus respectively on the creation
of contrast sets; the way contrast sets are used as labels for other contrast sets; the use of
rules, principles, and set topologies in this labeling process; the standardization and
institutionalization of contrast sets; the way in which people justify, legitimate, and attempt
to change standardized contrast sets; and the ways people learn about unfamiliar contrast
sets.
The dissertation uses the method of pattern analysis. It identifies and describes
abstract social forms, gives numerous concrete examples of each form, and includes sixty
images. The goal is to understand a recurrent type of human activity that affects and
structures many everyday life experiences. The dissertation is practically oriented as well,
and directly addresses the concerns of those responsible for designing contrast sets for public
use