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Author | Carlo Gatta; Simone Balocco; Victoria Martin Yuste; Ruben Leta; Petia Radeva | ||||
Title | Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow | Type | Conference Article | ||
Year | 2011 | Publication | 5th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 6669 | Issue | Pages | 159-166 | |
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Abstract | The fusion of clinically relevant information coming from different image modalities is an important topic in medical imaging. In particular, different cardiac imaging modalities provides complementary information for the physician: Computer Tomography Angiography (CTA) provides reliable pre-operative information on arteries geometry, even in the presence of chronic total occlusions, while X-Ray Angiography (XRA) allows intra-operative high resolution projections of a specific artery. The non-rigid registration of arteries between these two modalities is a difficult task. In this paper we propose the use of SIFTflow, in registering CTA and XRA images. At the best of our knowledge, this paper proposed SIFTflow as a XRay-CTA registration method for the first time in the literature. To highlight the arteries, so to guide the registration process, the well known Vesselness method has been employed. Results confirm that, to the aim of registration, the arteries must be highlighted and background objects removed as much as possible. Moreover, the comparison with the well known Free Form Deformation technique, suggests that SIFTflow has a great potential in the registration of multi-modal medical images. | ||||
Address | Las Palmas de Gran Canaria. Spain | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Berlin | Editor | Jordi Vitria; Joao Miguel Sanches; Mario Hernandez |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-21256-7 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ GBM2011 | Serial | 1752 | ||
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Author | David Roche; Debora Gil; Jesus Giraldo | ||||
Title | Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? | Type | Book Chapter | ||
Year | 2014 | Publication | G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology | Abbreviated Journal | |
Volume | 796 | Issue | 3 | Pages | 159-181 |
Keywords | β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model | ||||
Abstract | Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists. | ||||
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Publisher | Springer Netherlands | Place of Publication | Editor | ||
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ISSN | 0065-2598 | ISBN | 978-94-007-7422-3 | Medium | |
Area | Expedition | Conference | |||
Notes | IAM; 600.075 | Approved | no | ||
Call Number | IAM @ iam @ RGG2014 | Serial | 2197 | ||
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Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
Title | Hierarchical Adaptive Structural SVM for Domain Adaptation | Type | Journal Article | ||
Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 119 | Issue | 2 | Pages | 159-178 |
Keywords | Domain Adaptation; Pedestrian Detection | ||||
Abstract | A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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Publisher | Springer US | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0920-5691 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | Admin @ si @ XRV2016 | Serial | 2669 | ||
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Author | Enric Marti; Jordi Regincos; Juan Jose Villanueva; Jaime Lopez-Krahe | ||||
Title | Line drawing interpretation as polyhedral objects to man-machine interaction in CAD systems | Type | Book Chapter | ||
Year | 1994 | Publication | Advances in Pattern Recognition and Image Analysis, | Abbreviated Journal | |
Volume | Issue | Pages | 158-169 | ||
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Publisher | World Scientific Pub. | Place of Publication | Editor | ||
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ISSN | ISBN | 981-02-1872-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | IAM;ISE | Approved | no | ||
Call Number | IAM @ iam @ MRL1994 | Serial | 1609 | ||
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Author | Tadashi Araki; Nobutaka Ikeda; Nilanjan Dey; Sayan Chakraborty; Luca Saba; Dinesh Kumar; Elisa Cuadrado Godia; Xiaoyi Jiang; Ajay Gupta; Petia Radeva; John R. Laird; Andrew Nicolaides; Jasjit S. Suri | ||||
Title | A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound | Type | Journal Article | ||
Year | 2015 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 118 | Issue | 2 | Pages | 158-172 |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ AID2015 | Serial | 2640 | ||
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Author | Fadi Dornaika; Bogdan Raducanu | ||||
Title | Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation | Type | Book Chapter | ||
Year | 2012 | Publication | Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions | Abbreviated Journal | |
Volume | Issue | Pages | 157-178 | ||
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Publisher | NOVA Publishers | Place of Publication | Editor | S.E. Carter | |
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Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ DoR2012 | Serial | 2183 | ||
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Author | Bogdan Raducanu; Fadi Dornaika | ||||
Title | Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning | Type | Conference Article | ||
Year | 2010 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
Volume | Issue | Pages | 156–161 | ||
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Abstract | In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression. | ||||
Address | Anchorage; AK; USA; | ||||
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ISSN | 1050-4729 | ISBN | 978-1-4244-5038-1 | Medium | |
Area | Expedition | Conference | ICRA | ||
Notes | OR; MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ RaD2010 | Serial | 1310 | ||
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Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume | Issue | Pages | 156-160 | ||
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Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
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ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
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Author | Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez | ||||
Title | Data Augmentation from Sketch | Type | Conference Article | ||
Year | 2019 | Publication | International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging | Abbreviated Journal | |
Volume | 11840 | Issue | Pages | 155-162 | |
Keywords | Data augmentation; cycleGANs; Multi-objective optimization | ||||
Abstract | State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
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Address | Shenzhen; China; October 2019 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CLIP | ||
Notes | IAM; 600.145; 601.337; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GES2019 | Serial | 3359 | ||
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Author | Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders | ||||
Title | Selective Search for Object Recognition | Type | Journal Article | ||
Year | 2013 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 104 | Issue | 2 | Pages | 154-171 |
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Abstract | This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0920-5691 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ USG2013 | Serial | 2362 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg | ||||
Title | Evaluating the impact of color on texture recognition | Type | Conference Article | ||
Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 8047 | Issue | Pages | 154-162 | |
Keywords | Color; Texture; image representation | ||||
Abstract | State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets. |
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Address | York; UK; August 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-40260-9 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | CIC; 600.048 | Approved | no | ||
Call Number | Admin @ si @ KWA2013 | Serial | 2263 | ||
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Author | Guillermo Torres; Debora Gil | ||||
Title | A multi-shape loss function with adaptive class balancing for the segmentation of lung structures | Type | Journal Article | ||
Year | 2020 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
Volume | 15 | Issue | 1 | Pages | S154-55 |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ ToG2020 | Serial | 3590 | ||
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Author | Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez | ||||
Title | Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance | Type | Conference Article | ||
Year | 2013 | Publication | Proceedings of the International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | 1 | Issue | Pages | 153--161 | |
Keywords | Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model | ||||
Abstract | Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability | ||||
Address | Barcelona; February 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | SciTePress | Place of Publication | Portugal | Editor | Sebastiano Battiato and José Braz |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-989-8565-47-1 | Medium | ||
Area | 800 | Expedition | Conference | VISAPP | |
Notes | IAM;MV; 600.044; 600.047; 600.060; 605.203 | Approved | no | ||
Call Number | IAM @ iam @ SGR2013 | Serial | 2123 | ||
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Author | Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh | ||||
Title | Facial expression recognition based on multi observations with application to social robotics | Type | Book Chapter | ||
Year | 2015 | Publication | Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance | Abbreviated Journal | |
Volume | Issue | Pages | 153-166 | ||
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Abstract | Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression. |
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Publisher | Nova Science publishers | Place of Publication | Editor | Bruce Flores | |
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Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ DRB2015 | Serial | 2720 | ||
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Author | Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes | ||||
Title | Lost in Transcription of Graphic Signs in Ciphers | Type | Conference Article | ||
Year | 2022 | Publication | International Conference on Historical Cryptology (HistoCrypt 2022) | Abbreviated Journal | |
Volume | Issue | Pages | 153-158 | ||
Keywords | transcription of ciphers; hand-written text recognition of symbols; graphic signs | ||||
Abstract | Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. | ||||
Address | Amsterdam, Netherlands, June 20-22, 2022 | ||||
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Area | Expedition | Conference | HystoCrypt | ||
Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ MBS2022 | Serial | 3731 | ||
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