Investigating shape representation in area V4 with HMAX: Orientation and Grating selectivities

The question of how shape is represented is of central interest to understanding visual processing in cortex. While tuning properties of the cells in early part of the ventral visual stream, thought to be responsible for object recognition in the primate, are comparatively well understood, several different theories have been proposed regarding tuning in higher visual areas, such as V4. We used the model of object recognition in cortex presented by Riesenhuber and Poggio (1999), where more complex shape tuning in higher layers is the result of combining afferent inputs tuned to simpler features, and compared the tuning properties of model units in intermediate layers to those of V4 neurons from the literature. In particular, we investigated the issue of shape representation in visual area V1 and V4 using oriented bars and various types of gratings (polar, hyperbolic, and Cartesian), as used in several physiology experiments. Our computational model was able to reproduce several physiological findings, such as the broadening distribution of the orientation bandwidths and the emergence of a bias toward non-Cartesian stimuli. Interestingly, the simulation results suggest that some V4 neurons receive input from afferents with spatially separated receptive fields, leading to experimentally testable predictions. However, the simulations also show that the stimulus set of Cartesian and non-Cartesian gratings is not sufficiently complex to probe shape tuning in higher areas, necessitating the use of more complex stimulus sets.

Sparse shape representation using the Laplace-Beltrami eigenfunctions and its application to modeling subcortical structures

We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes by forming a Fourier series expansion. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we propose to filter out only the significant eigenfunctions by imposing l1-penalty. The new sparse framework can further avoid additional surface-based smoothing often used in the field. The proposed approach is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shapes in the normal population. In addition, we show how the emotional response is related to the anatomy of the subcortical structures.

Capturing the essence of shape of polygonal meshes

Polygonal meshes, essential shape representation, feature detection, external skeleton, mesh decimation, progrssive meshes, silhouettes, hidden line removal, non-photorealistic rendering, OpenNPAR

Representation and Detection of Shapes in Images

We present a set of techniques that can be used to represent and detect shapes in images. Our methods revolve around a particular shape representation based on the description of objects using triangulated polygons. This representation is similar to the medial axis transform and has important properties from a computational perspective. The first problem we consider is the detection of non-rigid objects in images using deformable models. We present an efficient algorithm to solve this problem in a wide range of situations, and show examples in both natural and medical images. We also consider the problem of learning an accurate non-rigid shape model for a class of objects from examples. We show how to learn good models while constraining them to the form required by the detection algorithm. Finally, we consider the problem of low-level image segmentation and grouping. We describe a stochastic grammar that generates arbitrary triangulated polygons while capturing Gestalt principles of shape regularity. This grammar is used as a prior model over random shapes in a low level algorithm that detects objects in images.

Hierarchical Object Recognition Using Libraries of Parameterized Model Sub-Parts

This thesis describes the development of a model-based vision system that exploits hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of non-rigid model objects contained in a large knowledge base despite the presence of noise and occlusion. Robustness is achieved by developing a system that can recognize viewed objects that are scaled or mirror-image instances of the known models or that contain components sub-parts with different relative scaling, rotation, or translation than in models. The approach taken in this thesis is to develop an object shape representation that incorporates a component sub-part hierarchy- to allow for efficient and correct indexing into an automatically generated model library as well as for relative parameterization among sub-parts, and a scale hierarchy- to allow for a general to specific recognition procedure. After analysis of the issues and inherent tradeoffs in the recognition process, a system is implemented using a representation based on significant contour curvature changes and a recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. In conclusion, the system's benefits and limitations are presented.

The Quadric Reference Surface: Theory and Applications

The conceptual component of this work is about "reference surfaces'' which are the dual of reference frames often used for shape representation purposes. The theoretical component of this work involves the question of whether one can find a unique (and simple) mapping that aligns two arbitrary perspective views of an opaque textured quadric surface in 3D, given (i) few corresponding points in the two views, or (ii) the outline conic of the surface in one view (only) and few corresponding points in the two views. The practical component of this work is concerned with applying the theoretical results as tools for the task of achieving full correspondence between views of arbitrary objects.

An fMRI-study of locally oriented perception in autism: altered early visual processing of the block design test

Autism has been associated with enhanced local processing on visual tasks. Originally, this was based on findings that individuals with autism exhibited peak performance on the block design test (BDT) from the Wechsler Intelligence Scales. In autism, the neurofunctional correlates of local bias on this test have not yet been established, although there is evidence of alterations in the early visual cortex. Functional MRI was used to analyze hemodynamic responses in the striate and extrastriate visual cortex during BDT performance and a color counting control task in subjects with autism compared to healthy controls. In autism, BDT processing was accompanied by low blood oxygenation level-dependent signal changes in the right ventral quadrant of V2. Findings indicate that, in autism, locally oriented processing of the BDT is associated with altered responses of angle and grating-selective neurons, that contribute to shape representation, figure-ground, and gestalt organization. The findings favor a low-level explanation of BDT performance in autism.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

The Vase’s Representation (Cantharus, Crater) on the Roman Mosaic in Portugal: A Significant Formal and Iconographic Path from Classic Antiquity to Late Antiquity

11TH INTERNATIONAL COLLOQUIUM ON ANCIENT MOSAICS OCTOBER 16TH  20TH, 2009, BURSA TURKEY Mosaics of Turkey and Parallel Developments in the Rest of the Ancient and Medieval World: Questions of Iconography, Style and Technique from the Beginnings of Mosaic until the Late Byzantine Era

Popular music exhibitions in Portugal: practices of representation from 2007 to 2013

This thesis focuses on the representation of Popular Music in museums by mapping, analyzing, and characterizing its practices in Portugal at the beginning of the 21st century. Now that museums' ability to shape public discourse is acknowledged, the examination of popular music's discourses in museums is of the utmost importance for Ethnomusicology and Popular Music Studies as well as for Museum Studies. The concept of 'heritage' is at the heart of this processes. The study was designed with the aim of moving the exhibiting of popular music in museums forward through a qualitative inquiry of case studies. Data collection involved surveying pop-rock music exhibitions as a qualitative sampling of popular music exhibitions in Portugal from 2007 to 2013. Two of these exhibitions were selected as case studies: No Tempo do Gira-Discos: Um Percurso pela Produção Fonográfica Portuguesa at the Museu da Música in Lisbon in 2007 (also Faculdade de Letras, 2009), and A Magia do Vinil, a Música que Mudou a Sociedade at the Oficina da Cultura in Almada in 2008 (and several other venues, from 2009 to 2013). Two specific domains were observed: popular music exhibitions as instances of museum practice and museum professionals. The first domain encompasses analyzing the types of objects selected for exhibition; the interactive museum practices fostered by the exhibitions; the concepts and narratives used to address popular music discursively, as well as the interpretative practices they allow. The second domain, focuses museum professionals and curators of popular music exhibitions as members of a group, namely their goals, motivations and perspectives. The theoretical frameworks adopted were drawn from the fields of ethnomusicology, popular music studies, and museum studies. The written materials of the exhibitions were subjected of methods of discourse analysis methods. Semi-structured interviews with curators and museum professional were also conducted and analysed. From the museum studies perspective, the study research suggests that the practice adopted by popular music museums largely matches that of conventional museums. From the ethnomusicological and popular music studies stand point, the two case studies reveal two distinct conceptual worlds: the first exhibition, curated by an academic and an independent researcher, points to a mental configuration where popular music is explained through a framework of genres supported by different musical practices. Moreover, it is industry actors such as decision makers and gatekeepers that govern popular music, which implies that the visitors' romantic conception of the musician is to some extent dismantled; the second exhibition, curated by a record collector and specialist, is based on a more conventional process of the everyday historical speech that encodes a mismatch between “good” and “bad music”. Data generated by a survey shows that only one curator, in fact that of my first case study, has an academic background. The backgrounds of all the others are in some way similar to the curator of the second case study. Therefore, I conclude that the second case study best conveys the current practice of exhibiting Popular Music in Portugal.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.