|
|
Enric Marti, Jordi Regincos, Juan Jose Villanueva, & Jaime Lopez-Krahe. (1994). Line drawing interpretation as polyhedral objects to man-machine interaction in CAD systems. In Advances in Pattern Recognition and Image Analysis, (pp. 158–169). World Scientific Pub.
|
|
|
|
V. Valev, & Petia Radeva. (1992). Determining Structural Description by Boolean Formulas. In H. Bunke (Ed.), Advances in Structural and Syntactic Pattern Recognition (Vol. 5, 131–140). Machine Perception and Artificial Intelligence:. World Scientific.
Abstract: Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.
This book gives an overview of the latest developments and achievements in the field.
|
|
|
|
Alicia Fornes, Josep Llados, & Joana Maria Pujadas-Mora. (2020). Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images. In Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends. World Scientific.
|
|
|
|
Jorge Bernal, F. Javier Sanchez, Cristina Rodriguez de Miguel, & Gloria Fernandez Esparrach. (2015). Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists. In Colonoscopy and Colorectal Cancer.
Abstract: Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly.
Keywords: Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description
|
|
|
|
Cristhian Aguilera, M.Ramos, & Angel Sappa. (2012). Simulated Annealing: A Novel Application of Image Processing in the Wood Area. In Marcos de Sales Guerra Tsuzuki (Ed.), Simulated Annealing – Advances, Applications and Hybridizations (pp. 91–104).
|
|
|
|
Aura Hernandez-Sabate, & Debora Gil. (2012). The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries. In Yasuhiro Honda (Ed.), Intravascular Ultrasound (pp. 185–206). Intech.
|
|
|
|
Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2011). Towards Intelligent Systems for Colonoscopy. In Paul Miskovitz (Ed.), Colonoscopy (Vol. 1, pp. 257–282). Intech.
Abstract: In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions
|
|
|
|
Thierry Brouard, A. Delaplace, Muhammad Muzzamil Luqman, H. Cardot, & Jean-Yves Ramel. (2010). Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures. In Ahmed Rebai (Ed.), Bayesian Network (pp. 13–37). Sciyo.
|
|
|
|
Joana Maria Pujadas-Mora, Alicia Fornes, Josep Llados, & Anna Cabre. (2016). Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources. In K.Matthijs, S.Hin, H.Matsuo, & J.Kok (Eds.), The future of historical demography. Upside down and inside out (pp. 127–131). Acco Publishers.
|
|
|
|
David Roche, Debora Gil, & Jesus Giraldo. (2014). Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? In G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology (Vol. 796, pp. 159–181). Springer Netherlands.
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.
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
|
|
|
|
Laura Igual, Joan Carles Soliva, Antonio Hernandez, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). A Supervised Graph-cut Deformable Model for Brain MRI Segmentation. Deformation models: tracking, animation and applications. In Computational Vision and Biomechanics. LNCS. Springer Netherlands.
|
|
|
|
Carles Fernandez, Jordi Gonzalez, Joao Manuel R. S. Taveres, & Xavier Roca. (2013). Towards Ontological Cognitive System. In Topics in Medical Image Processing and Computational Vision (Vol. 8, pp. 87–99). Springer Netherlands.
Abstract: The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users.
|
|
|
|
Joana Maria Pujadas-Mora, Alicia Fornes, Josep Llados, Gabriel Brea-Martinez, & Miquel Valls-Figols. (2019). The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries. In Nominative Data in Demographic Research in the East and the West: monograph (pp. 29–61).
Abstract: The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially.
|
|
|
|
Angel Sappa, David Geronimo, Fadi Dornaika, & Antonio Lopez. (2007). Stereo Vision Camera Pose Estimation for On-Board Applications. In Scene Reconstruction, Pose Estimation and Traking (pp. 39–50). Rustam Stolking.
|
|
|
|
Anjan Dutta, Josep Llados, Horst Bunke, & Umapada Pal. (2014). A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting. In Bart Lamiroy, & Jean-Marc Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 7–11). LNCS. Springer Berlin Heidelberg.
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.
Keywords: Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel
|
|
