Shape memory alloys and polymers have been extensively researched recently because of their unique ability to recover large deformations. Shape memory polymers (SMPs) are able to recover large deformations compared to shape memory alloys (SMAs), although SMAs have higher strength and are able to generate more stress during recovery. This project focuses on procedure for fabrication and Finite Element Modeling (FEM) of a shape memory composite actuator. First, SMP was characterized to reveal its mechanical properties. Specifically, glass transition temperature, the effects of temperature and strain rate on compressive response and recovery properties of shape memory polymer were studied. Then, shape memory properties of a NiTi wire, including transformation temperatures and stress generation, were investigated. SMC actuator was fabricated by using epoxy based SMP and NiTi SMA wire. Experimental tests confirmed the reversible behavior of fabricated shape memory composites. The Finite Element Method was used to model the shape memory composite by using a pre-written subroutine for SMA and defining the linear elastic and plastic properties of SMP. ABQUS software was used to simulate shape memory behavior. Beside the animated model in ABAQUS, constitutive models for SMA and SMP were also developed in MATLAB® by using the material properties obtained from experiments. The results of FEM simulation of SMC were found to be in good agreement with experimental results.

This dissertation focuses on the mathematical design of a unified shape kernel for geometric computing, with possible applications to computer aided design (CAM) and manufacturing (CAM), solid geometric modelling, free-form modelling of curves and surfaces, feature-based modelling, finite element meshing, computer animation, etc. The generality of such a unified shape kernel grounds on a shape theory for objects in some Euclidean space. Shape does not mean herein only geometry as usual in geometric modelling, but has been extended to other contexts, e. g. topology, homotopy, convexity theory, etc. This shape theory has enabled to make a shape analysis of the current geometric kernels. Significant deficiencies have been then identified in how these geometric kernels represent shapes from different applications. This thesis concludes that it is possible to construct a general shape kernel capable of representing and manipulating general specifications of shape for objects even in higher-dimensional Euclidean spaces, regardless whether such objects are implicitly or parametrically defined, they have ‘incomplete boundaries’ or not, they are structured with more or less detail or subcomplexes, which design sequence has been followed in a modelling session, etc. For this end, the basic constituents of such a general geometric kernel, say a combinatorial data structure and respective Euler operators for n-dimensional regular stratified objects, have been introduced and discussed.

In recent years, new approaches to the problem of Automated Target Recognition using techniques of shape theory and algebraic geometry have been explored. The power of this shape theoretic approach is that it allows one to develop tests for object/image matching that do not require knowledge of the object’s position in relation to the sensor nor the internal parameters of the sensor. Furthermore, these methods do not depend on the choice of coordinate systems in which the objects and images are represented. In this dissertation, we will expand on existing shape theoretic techniques and adapt these techniques to new sensor models. In each model, we develop an appropriate notion of shape for our objects and images and define the spaces of such shapes. The goal in each case is to develop tests for matching object and image shapes under an appropriate class of projections. The first tests we develop take the form of systems of polynomial equations (the so-called object/image relations) that check for exact matches of object/image pairs. Later, a more robust approach to matching is obtained by defining metrics on the shape spaces. This allows us in each model to develop a measure of “how close” an object is to being able to produce a given image. We conclude this dissertation by computing a number of examples using these tests for object/image matching.

Skeletal shape representations, in spite of their structural instabilities, have proven themselves as effective representation schemes for recognition and classification of visual shapes. They capture part structure in a compact and natural way and provide insensitivity to visual transformations such as occlusion and articulation of parts. In this thesis, we explore the potential use of disconnected skeleton representation for shape recognition and shape classification. Specifically, we first investigate the importance of contextual information in recognition where we extend the previously proposed disconnected skeleton based shape matching methods in different ways by incorporating category knowledge into matching process. Unlike the view in syntactic matching of shapes, our interpretation differentiates the semantic roles of the shapes in comparison in a way that a query shape is being matched with a database shape whose category is known a priori. The presence of context, i.e. the knowledge about the category of the database shape, influences the similarity computations, and helps us to obtain better matching performance. Next, we build upon our category-influenced matching framework in which both shapes and shape categories are represented with depth-1 skeletal trees, and develop a similarity-based shape classification method where the category trees formed for each shape category provide a reference set for learning the relationships between categories. As our classification method takes into account both within-category and between-category information, we attain high classification performance. Moreover, using the suggested classification scheme in a retrieval task improves both the efficiency and accuracy of matching by eliminating unrelated comparisons.

In order to meet the needs of people with hearing loss today hearing aids are custom designed. Increasingly accurate 3-D scanning technology has contributed to the transition from conventional production scenarios to software based processes. Nonetheless, there is a tremendous amount of manual work involved to transform an input 3-D surface mesh of the outer ear into a final hearing aid shape. This manual work is often cumbersome and requires lots of experience which is why automatic solutions are of high practical relevance. This work is concerned with the recognition of 3-D surface meshes of ear implants. In particular we present a semantic part-labeling framework which significantly outperforms existing approaches for this task. We make at least three contributions which may also be found useful for other classes of 3-D meshes. Firstly, we validate the discriminative performance of several local descriptors and show that the majority of them performs poorly on our data except for 3-D shape contexts. The reason for this is that many local descriptor schemas are not rich enough to capture subtle variations in form of bends which is typical for organic shapes. Secondly, based on the observation that the left and the right outer ear of an individual look very similar we raised the question how similar the ear shapes among arbitrary individuals are? In this work, we define a notion of distance between ear shapes as building block of a non-parametric shape model of the ear to better handle the anatomical variability in ear implant labeling. Thirdly, we introduce a conditional random field model with a variety of label priors to facilitate the semantic part-labeling of 3-D meshes of ear implants. In particular we introduce the concept of a global parametric transition prior to enforce transition boundaries between adjacent object parts with an a priori known parametric form. In this way we were able to overcome the issue of inadequate geometric cues (e.g., ridges, bumps, concavities) as natural indicators for the presence of part boundaries. The last part of this work offers an outlook to possible extensions of our methods, in particular the development of 3-D descriptors that are fast to compute whilst at the same time rich enough to capture the characteristic differences between objects residing in the same class.

A central concept in Evolutionary theory is the character trait. It provides a context in which to explore differences and similarities among taxa, both extant and extinct. It is expanded in scope in Evolutionary Developmental theory to functional units with a biological role, "evolutionarily stable configurations." The talo-crural joint is such a configuration, a highly canalized structural unit in primates forming the interface between organism, and foot and substrate. It is a microcosm in which to examine the relationship of shape with environment and function and the interplay of genetics, ontogeny, and use. Geometric Morphometric analysis of landmark data was employed in studying the articular surfaces of the talus in a diverse sample of adult specimens in nine catarrhine taxa. The influence of four factors on talar shape was examined: superfamily, a proxy for phylogeny; size and mass, a proxy for physical attributes; and substrate preference, a proxy for behavior. All significantly affected shape, and substrate preference was unrelated to the others. Appositional articular morphology, the shape of the subchondral bone surfaces of the talo-crural joints in an expanded sample of 12 taxa, showed a significant effect of the four proxies on the tibial and talar components, and substrate preference was weakly related to the other proxies in each. Singular Warp analysis of the cross-covariance matrices of the joints demonstrated sorting of taxa by substrate use and signals of convergent and divergent evolution among hominoids and cercopithecoids in joint shape. The ontogeny of the appositional articular shape was examined using adult and subadult specimens grouped by molar eruption. Singular Warp analysis demonstrated a genetic signal in the subadults, strongest in the slowly maturing African hominoids, and an epigenetic signal across taxa to substrate use in the adults. The talo-crural joint, a highly canalized, modular, and integrated "evolutionarily stable configuration," provides a model for the study of the evolution of shape. The epigenetic signal observed is consistent with plasticity or developmental plasticity in response to the interaction of the joint complex with the environment due to a behavioral effect, substrate use. This dissertation included previously unpublished, co-authored material.

Cette thèse porte sur l’appariement des formes, et la recherche par forme clef. Elle décrit quatrecontributions à ce domaine. La première contribution est une amélioration de la méthode des nuéesdynamiques pour partitionner au mieux les voxels à l’intérieur d’une forme donnée ; les partitionsobtenues permettent d’apparier les objets par un couplage optimal dans un graphe biparti. Laseconde contribution est la fusion de deux descripteurs, l’un local, l’autre global, par la règle duproduit. La troisième contribution considère le graphe complet, dont les sommets sont les formes dela base ou la requête, et les arêtes sont étiquetées par plusieurs distances, une par descripteur ;ensuite cette méthode calcule par programmation linéaire la combinaison convexe des distancesqui maximise soit la somme des longueurs des plus courts chemins entre la requête et les objetsde la base de données, soit la longueur du plus court chemin entre la requête et l’objet comparé àla requête. La quatrième contribution consiste à perturber la requête avec un algorithme génétiquepour la rapprocher des formes de la base de données, pour un ou des descripteur(s) donné(s) ; cetteméthode est massivement parallèle, et une architecture multi-agent est proposée. Ces méthodes sontcomparées aux méthodes classiques, et ont de meilleures performances, en terme de précision. / This thesis concerns shape matching and shape retrieval. It describes four contributions to thisdomain. The first is an improvement of the k-means method, in order to find the best partition ofvoxels inside a given shape ; these best partitions permit to match shapes using an optimal matchingin a bipartite graph. The second contribution is the fusion of two descriptors, one local, the otherglobal, with the product rule. The third contribution considers the complete graph, the vertices ofwhich are the shapes in the database and the query. Edges are labelled with several distances,one per descriptor. Then the method computes, with linear programming, the convex combinationof distances which maximizes either the sum of the lengths of all shortest paths from the query toall shapes of the database, or the length of the shortest path in the graph from query to the currentshape compared to query. The fourth contribution consists in perturbing the shape query, to make itcloser to shapes in the database, for any given descriptors. This method is massively parallel and amulti-agent architecture is proposed. These methods are compared to classical methods in the field,they achieve better retrieval performances.

In order to meet the needs of people with hearing loss today hearing aids are custom designed. Increasingly accurate 3-D scanning technology has contributed to the transition from conventional production scenarios to software based processes. Nonetheless, there is a tremendous amount of manual work involved to transform an input 3-D surface mesh of the outer ear into a final hearing aid shape. This manual work is often cumbersome and requires lots of experience which is why automatic solutions are of high practical relevance. This work is concerned with the recognition of 3-D surface meshes of ear implants. In particular we present a semantic part-labeling framework which significantly outperforms existing approaches for this task. We make at least three contributions which may also be found useful for other classes of 3-D meshes. Firstly, we validate the discriminative performance of several local descriptors and show that the majority of them performs poorly on our data except for 3-D shape contexts. The reason for this is that many local descriptor schemas are not rich enough to capture subtle variations in form of bends which is typical for organic shapes. Secondly, based on the observation that the left and the right outer ear of an individual look very similar we raised the question how similar the ear shapes among arbitrary individuals are? In this work, we define a notion of distance between ear shapes as building block of a non-parametric shape model of the ear to better handle the anatomical variability in ear implant labeling. Thirdly, we introduce a conditional random field model with a variety of label priors to facilitate the semantic part-labeling of 3-D meshes of ear implants. In particular we introduce the concept of a global parametric transition prior to enforce transition boundaries between adjacent object parts with an a priori known parametric form. In this way we were able to overcome the issue of inadequate geometric cues (e.g., ridges, bumps, concavities) as natural indicators for the presence of part boundaries. The last part of this work offers an outlook to possible extensions of our methods, in particular the development of 3-D descriptors that are fast to compute whilst at the same time rich enough to capture the characteristic differences between objects residing in the same class.

No description available.

In this thesis, I investigated how human vision processes colour and luminance information to enable perception of our environment. I first tested how colour can alter the perception of depth from shading. A luminance variation can be interpreted as either variation of reflectance (patterning) or variation of shape. The process of shape-from-shading interprets luminance variation as changes in the shape of the object (e.g. the shading on an object might elicit the perception of curvature). The addition of colour variation is known to modify this shape-from-shading processing. In the experiments presented here I tested how luminance driven percepts can be modified by colour. My first series of experiments confirmed that depth is modulated by colour. I explored a larger number of participants than previously tested. Contrary to previous studies, a wide repertoire of behaviour was found; participants experienced variously more depth, or less depth, or no difference. I hypothesised that the colour modulation effect might be due to a low-level contrast modulation of luminance by colour, rather than a higher-level depth effect. In a second series of experiments, I therefore tested how the perceived contrast of a luminance target can be affected by the presence of an orthogonal mask. I found that colour had a range of effects on the perception of luminance, again dependant on the participants. Luminance also had a similar wide range of effects on the perceived contrast of luminance targets. This showed that, at supra-threshold levels, a luminance target's contrast can be modulated by a component of another orientation (colour or luminance defined). The effects of luminance and colour were not following a particular rule. In a third series of experiments, I explored this interaction at detection levels of contrast. I showed cross-interaction between luminance target and mask but no effects of a colour mask.