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Fadi Dornaika, & Bogdan Raducanu. (2012). Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation. In S.E. Carter (Ed.), Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions (pp. 157–178). NOVA Publishers.
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Jose Manuel Alvarez, & Antonio Lopez. (2012). Photometric Invariance by Machine Learning. In Jan-Mark Geusebroek Joost van de Weijer A. G. Theo Gevers (Ed.), Color in Computer Vision: Fundamentals and Applications (Vol. 7, pp. 113–134). iConcept Press Ltd.
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Joost Van de Weijer, Robert Benavente, Maria Vanrell, Cordelia Schmid, Ramon Baldrich, Jacob Verbeek, et al. (2012). Color Naming. In Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & Jan-Mark Geusebroek (Eds.), Color in Computer Vision: Fundamentals and Applications (pp. 287–317). John Wiley & Sons, Ltd.
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Albert Andaluz, Francesc Carreras, Cristina Santa Marta, & Debora Gil. (2012). Myocardial torsion estimation with Tagged-MRI in the OsiriX platform. In Wiro Niessen(Erasmus MC) and Marc Modat(UCL) (Ed.), ISBI Workshop on Open Source Medical Image Analysis software. IEEE.
Abstract: Myocardial torsion (MT) plays a crucial role in the assessment of the functionality of the
left ventricle. For this purpose, the IAM group at the CVC has developed the Harmonic Phase Flow (HPF) plugin for the Osirix DICOM platform . We have validated its funcionalty on sequences acquired using different protocols and including healthy and pathological cases. Results show similar torsion trends for SPAMM acquisitions, with pathological cases introducing expected deviations from the ground truth. Finally, we provide the plugin free of charge at http://iam.cvc.uab.es
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Naila Murray. (2012). Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective (Xavier Otazu, & Maria Vanrell, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks.
Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification.
As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues.
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Aura Hernandez-Sabate, & Debora Gil. (2012). The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries. In Yasuhiro Honda (Ed.), Intravascular Ultrasound (pp. 185–206). Intech.
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). Optimal Medial Surface Generation for Anatomical Volume Representations. In MichaelW. David and Vannier H. and H. Yoshida (Ed.), Abdominal Imaging. Computational and Clinical Applications (Vol. 7601, pp. 265–273). Lecture Notes in Computer Science. Springer Berlin Heidelberg.
Abstract: Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Keywords: Medial surface representation; volume reconstruction
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