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Author |
Jorge Bernal; Debora Gil; Carles Sanchez; F. Javier Sanchez |
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Title |
Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis |
Type |
Conference Article |
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Year |
2014 |
Publication |
1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy |
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Volume |
8899 |
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1-10 |
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Keywords |
Image Segmentation; Polyps, Colonoscopy; Valley Information; Energy Maps |
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Abstract |
In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation. |
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Boston; USA; September 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-13409-3 |
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CARE |
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MV; IAM; 600.044; 600.047; 600.060; 600.075 |
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Admin @ si @ BGS2014b |
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2503 |
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Author |
Jorge Bernal; Joan M. Nuñez; F. Javier Sanchez; Fernando Vilariño |
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Title |
Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation |
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Conference Article |
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Year |
2014 |
Publication |
3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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Volume |
8680 |
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41-49 |
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Keywords |
Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps |
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Abstract |
In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation. |
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Boston; USA; September 2014 |
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CLIP |
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MV; 600.060; 600.044; 600.047;SIAI |
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no |
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Admin @ si @ BNS2014 |
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2502 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras |
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Title |
Combining Priors, Appearance and Context for Road Detection |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
Issue |
3 |
Pages |
1168-1178 |
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Keywords |
Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout |
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Abstract |
Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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1524-9050 |
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ADAS; 600.076;ISE |
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no |
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Admin @ si @ ALG2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2501 |
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Author |
Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez |
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Title |
Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters |
Type |
Journal Article |
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Year |
2014 |
Publication |
Expert Systems With Applications |
Abbreviated Journal |
EXSY |
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Volume |
41 |
Issue |
16 |
Pages |
7281–7290 |
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Keywords |
Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks |
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Abstract |
Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour. |
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Notes |
ADAS; 600.055; 600.057; 600.076 |
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no |
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Call Number |
Admin @ si @ LPA2014 |
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2500 |
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Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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Conference Article |
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Year |
2015 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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Pages |
231 - 238 |
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Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Abstract |
Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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ACDC |
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WACV |
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Notes |
ADAS; 600.076 |
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no |
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ADAS @ adas @ RRG2015 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2499 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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Pages |
2903 - 2908 |
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Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag
of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships.
Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods. |
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Stockholm; Sweden; August 2014 |
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ICPR |
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Notes |
DAG; 600.056; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ GRK2014b |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2497 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases |
Type |
Conference Article |
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Year |
2014 |
Publication |
Document Recognition and Retrieval XXI |
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Volume |
9021 |
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Keywords |
Document image retrieval; distance transform; MSER; spatial database |
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Abstract |
The structure of document images plays a signicant role in document analysis thus considerable eorts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signicant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. |
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Amsterdam; September 2014 |
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SPIE-DRR |
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Notes |
DAG; 600.056; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ GRK2014a |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2496 |
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Permanent link to this record |
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Author |
Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen |
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Title |
Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging |
Type |
Conference Article |
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Year |
2014 |
Publication |
17th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Volume |
8896 |
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231-238 |
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Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging |
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Abstract |
Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across dierent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three dierent OF methods, including HARP. |
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Boston; USA; September 2014 |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-14677-5 |
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STACOM |
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Notes |
IAM; ADAS; 600.060; 601.145; 600.076; 600.075 |
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no |
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Admin @ si @ MKF2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2495 |
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Author |
Oualid M. Benkarim; Petia Radeva; Laura Igual |
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Title |
Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation |
Type |
Conference Article |
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Year |
2014 |
Publication |
8th Conference on Articulated Motion and Deformable Objects |
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Volume |
8563 |
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138-147 |
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MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classication |
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The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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Palma de Mallorca; July 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-08848-8 |
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AMDO |
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MILAB; OR |
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no |
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Admin @ si @ BRI2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2494 |
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Author |
Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual |
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Title |
Spatial Discriminant ICA for RS-fMRI characterisation |
Type |
Conference Article |
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Year |
2014 |
Publication |
4th International Workshop on Pattern Recognition in Neuroimaging |
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1-4 |
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Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. |
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Tübingen; June 2014 |
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978-1-4799-4150-6 |
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PRNI |
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OR;MILAB |
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no |
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Admin @ si @ TBI2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2493 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
MSER-based Real-Time Text Detection and Tracking |
Type |
Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3110 - 3115 |
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We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices. |
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Stockholm; August 2014 |
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1051-4651 |
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ICPR |
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DAG; 600.056; 601.158; 601.197; 600.077 |
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no |
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Admin @ si @ GoK2014a |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2492 |
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Author |
Dimosthenis Karatzas; Sergi Robles; Lluis Gomez |
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Title |
An on-line platform for ground truthing and performance evaluation of text extraction systems |
Type |
Conference Article |
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Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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242 - 246 |
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This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 600.056; 600.077 |
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Admin @ si @ KRG2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2491 |
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Author |
Carlo Gatta; Adriana Romero; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Unrolling loopy top-down semantic feedback in convolutional deep networks |
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Conference Article |
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2014 |
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Workshop on Deep Vision: Deep Learning for Computer Vision |
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498-505 |
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In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches. |
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Columbus; Ohio; June 2014 |
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CVPRW |
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LAMP; MILAB; 601.160; 600.079 |
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Admin @ si @ GRW2014 |
Serial ![sorted by Serial field, descending order (down)](img/sort_desc.gif) |
2490 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
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2014 |
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Handbook of Document Image Processing and Recognition |
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D |
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523-551 |
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Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting |
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According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ TaT2014 |
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2489 |
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Author |
Victor Ponce; Sergio Escalera; Xavier Baro |
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Title |
Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings |
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2013 |
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15th ACM International Conference on Multimodal Interaction |
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495-502 |
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In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions. |
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Sidney; Australia; December 2013 |
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978-1-4503-2129-7 |
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ICMI |
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HuPBA;MV |
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no |
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Admin @ si @ PEB2013 |
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2488 |
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