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Author |
Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal |
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Title |
3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos |
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Book Chapter |
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Year |
2015 |
Publication |
Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 |
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Volume |
9515 |
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Pages |
140-152 |
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Keywords |
Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds |
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Abstract |
Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response.
Spatio-temporal stability is achieved by extracting 3D watershed regions on the
temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection. |
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CARE |
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IAM; MV; 600.075 |
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no |
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Admin @ si @ GSF2015 |
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2733 |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
Type |
Book Chapter |
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Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
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Volume |
9534 |
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69-79 |
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Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
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Munich; Germany; January 2015 |
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Springer International Publishing |
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0302-9743 |
ISBN |
978-3-319-28711-9 |
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STACOM |
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ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Admin @ si @ KHM2015 |
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2734 |
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Author |
Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
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Title |
Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
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Book Chapter |
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Year |
2015 |
Publication |
Artificial Intelligence Research and Development |
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Volume |
277 |
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Pages |
247 - 256 |
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Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. |
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IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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MILAB |
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no |
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Call Number |
Admin @ si @TVR2015 |
Serial |
2780 |
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Author |
E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
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Title |
Regularized Clustering for Egocentric Video Segmentation |
Type |
Book Chapter |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Pages |
327-336 |
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Keywords |
Temporal video segmentation ; Egocentric videos ; Clustering |
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Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Springer International Publishing |
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978-3-319-19390-8 |
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MILAB |
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no |
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Call Number |
Admin @ si @TDB2015a |
Serial |
2781 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel |
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Title |
An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres |
Type |
Book Chapter |
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Year |
2015 |
Publication |
The White Book on Public Library Network from Diputació de Barcelona |
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Notes |
MV; DAG;SIAI |
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no |
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Call Number |
Admin @ si @VKC2015 |
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2798 |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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Volume |
D |
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Pages |
489-521 |
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Keywords |
Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation |
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Abstract |
This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. |
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Springer London |
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Editor |
D. Doermann; K. Tombre |
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ISBN |
978-0-85729-858-4 |
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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ LlR2014 |
Serial |
2380 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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Volume |
D |
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Pages |
523-551 |
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Keywords |
Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting |
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Abstract |
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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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ TaT2014 |
Serial |
2489 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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Volume |
8746 |
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Pages |
7-11 |
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Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel |
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Abstract |
Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ DLB2014 |
Serial |
2698 |
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Author |
Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
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Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Augmented Vision and Reality |
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Volume |
6 |
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Pages |
23-47 |
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Abstract |
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
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Springer Berlin Heidelberg |
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ISSN |
2190-5916 |
ISBN |
978-3-642-37840-9 |
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Notes |
ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
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no |
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Call Number |
Admin @ si @ AHM2014 |
Serial |
2223 |
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Permanent link to this record |
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Author |
David Roche; Debora Gil; Jesus Giraldo |
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Title |
Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? |
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Book Chapter |
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Year |
2014 |
Publication |
G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology |
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Volume |
796 |
Issue |
3 |
Pages |
159-181 |
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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 |
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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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Springer Netherlands |
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0065-2598 |
ISBN |
978-94-007-7422-3 |
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Notes |
IAM; 600.075 |
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no |
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Call Number |
IAM @ iam @ RGG2014 |
Serial |
2197 |
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Permanent link to this record |
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Author |
A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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D |
Issue |
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591-646 |
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Keywords |
Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
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Abstract |
The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
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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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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ KeK2014 |
Serial |
2425 |
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Author |
Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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E |
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749-774 |
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Abstract |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-860-7 |
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Notes |
DAG; ADAS; 600.076; 600.077 |
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no |
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Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
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Permanent link to this record |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Robust Head Gestures Recognition for Assistive Technology |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
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Volume |
8495 |
Issue |
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Pages |
152-161 |
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Keywords |
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Abstract |
This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture. |
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Publisher |
Springer International Publishing |
Place of Publication |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-319-07490-0 |
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Area |
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Expedition |
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Conference |
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Notes |
LAMP; |
Approved |
no |
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Call Number |
Admin @ si @ TSR2014b |
Serial |
2505 |
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Permanent link to this record |
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Author |
C. Alejandro Parraga |
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Title |
Color Vision, Computational Methods for |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Encyclopedia of Computational Neuroscience |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1-11 |
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Keywords |
Color computational vision; Computational neuroscience of color |
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Abstract |
The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments. |
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Corporate Author |
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Thesis |
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Publisher |
Springer-Verlag Berlin Heidelberg |
Place of Publication |
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Editor |
Dieter Jaeger; Ranu Jung |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-1-4614-7320-6 |
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Area |
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Conference |
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Notes |
CIC; 600.074 |
Approved |
no |
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Call Number |
Admin @ si @ Par2014 |
Serial |
2512 |
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Author |
Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo |
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Title |
From re-identification to identity inference: Labeling consistency by local similarity constraints |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Person Re-Identification |
Abbreviated Journal |
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Volume |
2 |
Issue |
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Pages |
287-307 |
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Keywords |
re-identification; Identity inference; Conditional random fields; Video surveillance |
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Abstract |
In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery. |
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Publisher |
Springer London |
Place of Publication |
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Original Title |
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Series Issue |
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Edition |
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ISSN |
2191-6586 |
ISBN |
978-1-4471-6295-7 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
LAMP; 600.079 |
Approved |
no |
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Call Number |
Admin @ si @KLB2014b |
Serial |
2521 |
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Permanent link to this record |