Records |
Author |
Josep Llados; Gemma Sanchez; Enric Marti |
Title |
A string based method to recognize symbols and structural textures in architectural plans |
Type |
Book Chapter |
Year |
1998 |
Publication |
Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers |
Abbreviated Journal |
LNCS |
Volume |
1389 |
Issue |
1998 |
Pages |
91-103 |
Keywords |
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Abstract |
This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Publisher |
Springer Link |
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LNCS |
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Notes |
DAG; IAM |
Approved |
no |
Call Number |
IAM @ iam @ SLE1998 |
Serial |
1573 |
Permanent link to this record |
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Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
Title |
Symbol recognition: current advances and perspectives |
Type |
Book Chapter |
Year |
2002 |
Publication |
Graphics Recognition Algorithms And Applications |
Abbreviated Journal |
LNCS |
Volume |
2390 |
Issue |
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Pages |
104-128 |
Keywords |
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Abstract |
The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
Address |
London, UK |
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Thesis |
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Publisher |
Springer-Verlag |
Place of Publication |
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Editor |
Dorothea Blostein and Young- Bin Kwon |
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Series Title |
Lecture Notes in Computer Science |
Abbreviated Series Title |
LNCS |
Series Volume |
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Edition |
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ISSN |
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ISBN |
3-540-44066-6 |
Medium |
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Area |
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Expedition |
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Conference |
GREC |
Notes |
DAG; IAM; |
Approved |
no |
Call Number |
IAM @ iam @ LVS2002 |
Serial |
1572 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
Title |
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces |
Type |
Book Chapter |
Year |
2013 |
Publication |
Graph Embedding for Pattern Analysis |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1-26 |
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Abstract |
Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis. |
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Publisher |
Springer New York |
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ISBN |
978-1-4614-4456-5 |
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DAG |
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no |
Call Number |
Admin @ si @ LRL2013b |
Serial |
2271 |
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Author |
Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke |
Title |
Median Graph Computation by Means of Graph Embedding into Vector Spaces |
Type |
Book Chapter |
Year |
2013 |
Publication |
Graph Embedding for Pattern Analysis |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
45-72 |
Keywords |
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Abstract |
In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant. |
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Publisher |
Springer New York |
Place of Publication |
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Editor |
Yun Fu; Yungian Ma |
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Original Title |
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ISBN |
978-1-4614-4456-5 |
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Notes |
DAG |
Approved |
no |
Call Number |
Admin @ si @ FBV2013 |
Serial |
2421 |
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Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
Title |
Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey |
Type |
Book Chapter |
Year |
2017 |
Publication |
Gesture Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
539-578 |
Keywords |
Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies |
Abstract |
Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research. |
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Notes |
HUPBA; no proj |
Approved |
no |
Call Number |
Admin @ si @ ACB2017a |
Serial |
2981 |
Permanent link to this record |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
Title |
Deep learning-based vegetation index estimation |
Type |
Book Chapter |
Year |
2021 |
Publication |
Generative Adversarial Networks for Image-to-Image Translation |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
205-234 |
Keywords |
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Abstract |
Chapter 9 |
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Publisher |
Elsevier |
Place of Publication |
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Editor |
A.Solanki; A.Nayyar; M.Naved |
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Notes |
MSIAU; 600.122 |
Approved |
no |
Call Number |
Admin @ si @ SSV2021a |
Serial |
3578 |
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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 |
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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 |
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Series Editor |
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Series Volume |
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Edition |
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ISSN |
0065-2598 |
ISBN |
978-94-007-7422-3 |
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Conference |
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Notes |
IAM; 600.075 |
Approved |
no |
Call Number |
IAM @ iam @ RGG2014 |
Serial |
2197 |
Permanent link to this record |
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Author |
Misael Rosales; Petia Radeva; Oriol Rodriguez; Debora Gil |
Title |
Suppression of IVUS Image Rotation. A Kinematic Approach |
Type |
Book Chapter |
Year |
2005 |
Publication |
Functional Imaging and Modeling of the Heart |
Abbreviated Journal |
LNCS |
Volume |
3504 |
Issue |
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Pages |
889-892 |
Keywords |
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Abstract |
IntraVascular Ultrasound (IVUS) is an exploratory technique used in interventional procedures that shows cross section images of arteries and provides qualitative information about the causes and severity of the arterial lumen narrowing. Cross section analysis as well as visualization of plaque extension in a vessel segment during the catheter imaging pullback are the technique main advantages. However, IVUS sequence exhibits a periodic rotation artifact that makes difficult the longitudinal lesion inspection and hinders any segmentation algorithm. In this paper we propose a new kinematic method to estimate and remove the image rotation of IVUS images sequences. Results on several IVUS sequences show good results and prompt some of the clinical applications to vessel dynamics study, and relation to vessel pathology. |
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Publisher |
Springer Berlin / Heidelberg |
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Editor |
Frangi, Alejandro and Radeva, Petia and Santos, Andres and Hernandez, Monica |
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Lecture Notes in Computer Science |
Abbreviated Series Title |
LNCS |
Series Volume |
3504 |
Series Issue |
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Edition |
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Notes |
IAM;MILAB |
Approved |
no |
Call Number |
IAM @ iam @ RRR2005 |
Serial |
1645 |
Permanent link to this record |
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Author |
Susana Alvarez; Xavier Otazu; Maria Vanrell |
Title |
Image Segmentation Based on Inter-Feature Distance Maps |
Type |
Book Chapter |
Year |
2005 |
Publication |
Frontiers in Artificial Intelligence and Applications, IOS Press, 131: 75–82 |
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CIC |
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no |
Call Number |
CAT @ cat @ AOV2005 |
Serial |
569 |
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Author |
Jordi Vitria; Petia Radeva; I. Aguilo |
Title |
Recent Advances in Artificial Intelligence Research and Development |
Type |
Book Chapter |
Year |
2004 |
Publication |
Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9 |
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Amsterdam |
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OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ VRA2004 |
Serial |
509 |
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Author |
Xavier Baro; Jordi Vitria |
Title |
Feature Selection with Non-Parametric Mutual Information for Adaboost Learning |
Type |
Book Chapter |
Year |
2005 |
Publication |
Frontiers in Artificial Intelligence and Applications / Artificial intelligence Research and Development, 131:131–138, Eds: B. Lopez, J. Melendez, P. Radeva, J. Vitria, IOS Press, ISBN: 1–58603–560–6 |
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Notes |
OR;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ BaV2005b |
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583 |
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Author |
Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva |
Title |
Towards Egocentric Person Re-identification and Social Pattern Analysis. |
Type |
Book Chapter |
Year |
2019 |
Publication |
Frontiers in Artificial Intelligence and Applications |
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310 |
Issue |
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Pages |
203 - 211 |
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Abstract |
CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation. |
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MILAB; no proj |
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no |
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Admin @ si @ TCP2019 |
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3377 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
Title |
Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation |
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Book Chapter |
Year |
2012 |
Publication |
Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions |
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157-178 |
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NOVA Publishers |
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S.E. Carter |
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OR;MV |
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no |
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Admin @ si @ DoR2012 |
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2183 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
Title |
Measuring External Face Appearance for Face Classification. |
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Book Chapter |
Year |
2007 |
Publication |
Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing |
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Vienna (Austria) |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2007b |
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940 |
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Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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Book Chapter |
Year |
2021 |
Publication |
Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 |
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15 |
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89–93 |
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Abstract |
Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces. |
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Springer Nature |
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IAM; DAG; 600.120; 600.145; 600.139 |
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no |
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Admin @ si @ GRP2021 |
Serial |
3594 |
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