| Records |
| Author |
Debora Gil; Petia Radeva |
| Title |
Inhibition of False Landmarks |
Type |
Book Chapter |
| Year |
2004 |
Publication |
Recent Advances in Artificial Intelligence Research and Development |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
233-244 |
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| Abstract |
We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Its high sensitivity to changes in vector directions makes it suitable for landmark location in real images prone to need smoothing to reduce the impact of noise. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our Inhibition Orientation Energy (IOE) landmark locator. |
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| Publisher |
IOS Press |
Place of Publication |
Barcelona (Spain) |
Editor |
al, J.V. et |
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IAM;MILAB |
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no |
| Call Number |
IAM @ iam @ GiR2004a |
Serial |
1533 |
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| Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva; E.N.Nofrerias |
| Title |
Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images |
Type |
Conference Article |
| Year |
2004 |
Publication |
Proc. Computers in Cardiology |
Abbreviated Journal |
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| Volume |
31 |
Issue |
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Pages |
229-232 |
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| Abstract |
The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles |
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Chicago (USA) |
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| Notes |
IAM; MILAB |
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no |
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IAM @ iam @ HGR2004 |
Serial |
1555 |
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| Author |
Oriol Rodriguez-Leor; J. Mauri; Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
| Title |
Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria |
Type |
Journal Article |
| Year |
2004 |
Publication |
Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología |
Abbreviated Journal |
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| Volume |
57 |
Issue |
2 |
Pages |
100 |
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SEC |
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IAM;MILAB |
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no |
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IAM @ iam @ RMF2004 |
Serial |
1642 |
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| Author |
Fernando Vilariño; Debora Gil; Petia Radeva |
| Title |
A Novel FLDA Formulation for Numerical Stability Analysis |
Type |
Book Chapter |
| Year |
2004 |
Publication |
Recent Advances in Artificial Intelligence Research and Development |
Abbreviated Journal |
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| Volume |
113 |
Issue |
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Pages |
77-84 |
| Keywords |
Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision |
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Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision. |
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IOS Press |
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Editor |
J. Vitrià, P. Radeva and I. Aguiló |
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ISBN |
978-1-58603-466-5 |
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| Notes |
MV;IAM;MILAB;SIAI |
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no |
| Call Number |
IAM @ iam @ VGR2004 |
Serial |
1663 |
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| Author |
Jaume Garcia |
| Title |
Generalized Active Shape Models Applied to Cardiac Function Analysis |
Type |
Report |
| Year |
2004 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
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Issue |
78 |
Pages |
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| Keywords |
Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography. |
| Abstract |
Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown. |
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CVC (UAB) |
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Thesis |
Master's thesis |
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IAM; |
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no |
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IAM @ iam @ Gar2004 |
Serial |
1513 |
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| Author |
Jordi Gonzalez |
| Title |
Human Sequence Evaluation: the Key-frame Approach |
Type |
Book Whole |
| Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Thesis |
Ph.D. thesis |
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Editor |
Xavier Roca;Javier Varona |
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no |
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ISE @ ise @ Gon2004 |
Serial |
362 |
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| Author |
David Guillamet |
| Title |
Statistical Local Appearance Models for Object Recognition |
Type |
Book Whole |
| Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Address |
Bellaterra |
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Thesis |
Ph.D. thesis |
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Editor |
Jordi Vitria |
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no |
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Admin @ si @ Gui2004 |
Serial |
444 |
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| Author |
Oriol Pujol |
| Title |
A semi-Supervised Statistical Framework and Generative Snakes for IVUS Analysis |
Type |
Book Whole |
| Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Address |
CVC (UAB), Bellaterra |
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Thesis |
Ph.D. thesis |
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Place of Publication |
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Editor |
Petia Radeva |
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| Notes |
HuPBA;MILAB |
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no |
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BCNPCL @ bcnpcl @ Puj2004 |
Serial |
512 |
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| Author |
Debora Gil |
| Title |
Geometric Differential Operators for Shape Modelling |
Type |
Book Whole |
| Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Medical imaging feeds research in many computer vision and image processing fields: image filtering, segmentation, shape recovery, registration, retrieval and pattern matching. Because of their low contrast changes and large variety of artifacts and noise, medical imaging processing techniques relying on an analysis of the geometry of image level sets rather than on intensity values result in more robust treatment. From the starting point of treatment of intravascular images, this PhD thesis ad- dresses the design of differential image operators based on geometric principles for a robust shape modelling and restoration. Among all fields applying shape recovery, we approach filtering and segmentation of image objects. For a successful use in real images, the segmentation process should go through three stages: noise removing, shape modelling and shape recovery. This PhD addresses all three topics, but for the sake of algorithms as automated as possible, techniques for image processing will be designed to satisfy three main principles: a) convergence of the iterative schemes to non-trivial states avoiding image degeneration to a constant image and representing smooth models of the originals; b) smooth asymptotic behav- ior ensuring stabilization of the iterative process; c) fixed parameter values ensuring equal (domain free) performance of the algorithms whatever initial images/shapes. Our geometric approach to the generic equations that model the different processes approached enables defining techniques satisfying all the former requirements. First, we introduce a new curvature-based geometric flow for image filtering achieving a good compromise between noise removing and resemblance to original images. Sec- ond, we describe a new family of diffusion operators that restrict their scope to image level curves and serve to restore smooth closed models from unconnected sets of points. Finally, we design a regularization of snake (distance) maps that ensures its smooth convergence towards any closed shape. Experiments show that performance of the techniques proposed overpasses that of state-of-the-art algorithms. |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona (Spain) |
Editor |
Jordi Saludes i Closa;Petia Radeva |
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ISBN |
84-933652-0-3 |
Medium |
prit |
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| Notes |
IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ GIL2004 |
Serial |
1517 |
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