Francesc Serratosa.     List of Publications

francesc.serratosa@urv.cat

https://webs-deim.urv.cat/~francesc.serratosa/

DBLP              ResearchGate              Google Scholar

(Red: In Science Citation Index)

The ultimate aims of research are to generate measurable and testable data, gradually adding to the accumulation of human knowledge

­­­----2024----

188 (63). Francesc Serratosa, GraphFingerprint: Graph Embedding of graphs with almost constant sub-structures, Pattern Analysis and Applications, 2024.

187 (62). Natàlia Segura-Alabarta, Francesc Serratosa, Alberto Fernández, A practical study of the proportion of non-unique neighbor-joining trees of microsatellite markers, Computational and Structural Biotechnology Reports, 2024.

186 (61). Yarkın Çetin, Benjamí Martorell, Francesc Serratosa, Mònica Calatayud, Adsorption of Guanine on Oxygen-Deficient TiO₂ Surface: A Combined MD-DFTB/DFT Strategy, ACS Omega, 2024. https://pubs.acs.org/doi/10.1021/acsomega.4c05806

185.  F. Serratosa, Graph Regression based on Autoencoders and Graph Autoencoders, International Congress on Pattern Recognition, ICPR2024, 2024.

184.  F. Serratosa, A generative algorithm to compute NanoFingerprints, Iberoamerican Congress on Pattern Recognition, CIARP2024, LNCS, pp:, 2024.

183.  N. Segura-Alabart, A. Fernández and F. Serratosa, Evaluation metrics in Saliency Maps applied to Graph Regression, Syntactic and Structural Pattern Recognition, SSPR2024, LNCS, pp:, 2024.

182 (60). Yarkın Çetin, Benjamí Martorell, Francesc Serratosa, Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning, Computational and Structural Biotechnology Reports, 2024. https://www.sciencedirect.com/science/article/pii/S2950363924000085

181 (59). F. Serratosa, ATENA: A web-based tool for modelling metal oxide nanoparticles based on the NanoFingerprint QSAR, Molecules, 29 (10), 2024. https://www.mdpi.com/1420-3049/29/10/2235

­----2023----

180 (58). A. Fernández, N. Segura-Alabart and F. Serratosa, The MultiFurcating Neighbor-Joining Algorithm for Reconstructing Polytomic Phylogenetic Trees, Journal of Molecular Evolution, https://doi.org/10.1007/s00239-023-10134-z

179. F. Serratosa, Graph Embedding of almost constant large graphs, Iberoamerican Congress on Pattern Recognition 2023, Coimbra, Portugal, LNCS: 14469, pp: 16–30, 2023. https://link.springer.com/chapter/10.1007/978-3-031-49018-7_2

178. S. Fadlallah, N. Segura-Alabart, C. Julià and F. Serratosa, Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression, Graph-Based Representations in Pattern Recognition 2023, Salerno, Italy, LNCS: 14121, pp: 81-91, 2023. https://doi.org/10.1007/978-3-031-42795-4_8

177.  A. Qadir, W. González-López, N.  Duncan and F. Serratosa, Automatic fish detection and tracking for analysis of reproductive behaviours in Senegalese sole (Solea senegalensis) using neural networks and the DeepSORT algorithm, 12th International Symposium on Reproductive Physiology of Fish 2023, Crete, Grece, 2023.

176 (57). S. Fadlallah, C. Julià, S. García-Vallvé, G. Pujadas and F. Serratosa, Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model, International Journal of Molecular Sciences, pp:, 2023. https://www.mdpi.com/1422-0067/24/10/8779

----2022----

175.  W.A González López, A. Qadir1, N. Duncan and F. Serratosa, Fish tracking for automatic detection of reproductive behaviour, Aquaculture Europe 2022, Rimini, Italy, pp:, 2022.

174.  S. Fadlallah, C. Julià & F. Serratosa, Graph Regression based on Graph Autoencoders, Syntactic and Structural Pattern Recognition, SSPR2022, LNCS 13813, pp 142:151, 2022. https://doi.org/10.1007/978-3-031-23028-8_15

173.  E. Rica, S.Álvarez & F. Serratosa, Tarragona Graph Database for Machine Learning based on Graphs, Syntactic and Structural Pattern Recognition, SSPR2022, LNCS 13813, pp 302:310, 2022. https://doi.org/10.1007/978-3-031-23028-8_31

172.  E. Rica, S.Álvarez & F. Serratosa, Learning Distances between Graph nodes and edges, Syntactic and Structural Pattern Recognition, SSPR2022, LNCS 13813, pp 103:112, 2022. https://doi.org/10.1007/978-3-031-23028-8_11

171.  E. Rica, S.Álvarez & F. Serratosa, Zero-Error Digitisation and Contextualisation of Piping and Instrumentation Diagrams using Node Classification and Sub-graph Search, Syntactic and Structural Pattern Recognition, SSPR2022, LNCS 13813, pp. 274:282, 2022. https://doi.org/10.1007/978-3-031-23028-8_28

170 (56). N. Segura-Alabart, F. Serratosa, S. Gómez1 and A. Fernández, Non-unique UPGMA clusterings of microsatellite markers, Briefings in Bioinformatics, 2022. https://doi.org/10.1093/bib/bbac312

169. F. Serratosa, S. Álvarez, L. Escorihuela and M. Calatayud, Subgraph NanoFingerprint for modelling metal oxide nanoparticles based on connected atoms exploration. NanoWeek & NanoCommons Final Conference 2022, Cyprus 2022.

168 (55). Y. Çetin, B. Martorell, F. Serratosa, N. Aguilera-Porta, M. Calatayud, Analysing the TiO2 surface reactivity based on oxygen vacancies computed by DFT and DFTB methods, Journal of Physics: Condensed Matter 34, 2022. https://iopscience.iop.org/article/10.1088/1361-648X/ac7025

----2021----

167 (54). E. Rica, S. Álvarez and F. Serratosa, Ligand-Based Virtual Screening based on The Graph Edit Distance, International Journal of Molecular Sciences, 22, 12751, 2021. https://doi.org/10.3390/ijms222312751

166 (53). F. Serratosa, Redefining the Graph Edit Distance, S. N. Computer Science, 2:438, 2021. https://doi.org/10.1007/s42979-021-00792-5

165 (52). E. Rica, S. Álvarez and F. Serratosa, Group of Components Detection in Engineering Drawings based on Graph Matching, Engineering Applications of Artificial Intelligence, 104, 2021.

­­­164.  Y. Çetin, B. Martorell and F. Serratosa, New descriptors in toxicology prediction of nanomaterials: Using quasi-ab initio MD simulations for the estimation of aqueous ZnO and TiO2 surface structure parameters, Nanotox2021, 2021.

163 (51). E. Rica, S. Álvarez and F. Serratosa, On-line learning the graph edit distance costs, Pattern Recognition Letters, 146, pp:52-62, 2021.

­­­----2020----

162.  J. Contreras-García, F. Serratosa, S. Gómez, H. Y. Geng, G. J. Ackland & M. Marqués, Predicting chemical bond at high pressure with machine learning, 58th European High Pressure Research Group International Conference, Tenerife, Spain, EHPRG 2020, 2020.

161.  P. Santacruz and F. Serratosa, Incorporating a graph-matching algorithm into a muscle mechanics model, International Conference on Pattern Recognition, pp: 53 – 58, ICPR2020, 2021.

160.  S. Algabli and F. Serratosa, Learning Graph Matching Substitution Weights based on a Linear Regression, International Conference on Pattern Recognition, pp: 39 – 46, ICPR2020, 2021.

159 (50). D. Conte, F. Serratosa, Interactive Online Learning for Graph Matching using Active Strategies, Knowledge-Based Systems, 105, 106275, 2020. https://doi.org/10.1016/j.knosys.2020.106275

158 (49). F. Serratosa, A general model to define the substitution, insertion and deletion graph edit costs based on an embedded space, Pattern Recognition Letters, 138, pp: 115-122, 2020. https://doi.org/10.1016/j.patrec.2020.07.010

157 (48). P. Santacruz and F. Serratosa, Error-tolerant graph matching in linear computational cost using an initial small partial matching, Pattern Recognition Letters, 134, pp:10-19 2020. https://www.sciencedirect.com/science/article/pii/S0167865518301235

156 (47). C. Garcia-Hernandez, A. Fernández and F Serratosa, Learning the Edit Costs of the Graph Edit Distance Applied to Ligand-Based Virtual Screening, Current Topics in Medicinal Chemistry, 20, pp: 1-11, 2020, https://doi.org/10.2174/1568026620666200603122000

155 (46). Yasin, Haque, Adnan, Rahnuma, Hossain, Naha, Kabir and Serratosa, Localization of Autonomous Robot in an Urban Area Based on SURF Feature Extraction of Images, International Journal of Technology Diffusion, 11 (4), 2020. https://doi.org/10.4018/IJTD.20201001

154.  C. Garcia-Hernandez, A. Fernandez and F. Serratosa, Training a molecular dissimilarity measure for target specific activities, Society of Environmental Toxicology and Chemistry, SETAC 2020, Dublin, Ireland, 2020.

153 (45). E. Rica, C. Moreno-Garcia, S. Alvarez, F. Serratosa, Reducing Human Effort in Engineering Drawing Validation, Computers in Industry 117, pp: 103198, 2020. https://doi.org/10.1016/j.compind.2020.103198

152 (44). P. Santacruz & F. Serratosa, Learning the graph edit costs based on a learning model applied to sub-optimal graph matching, Neural Processing Letters, 51, pp:  881–904, 2020. https://link.springer.com/article/10.1007/s11063-019-10121-w

­­151 (43). F. Serratosa, A commentary on “Learning error-correcting graph matching with a multiclass neural network”, Pattern Recognition Letters, 129, pp:16-18, 2020. https://doi.org/10.1016/j.patrec.2019.10.033

150 (42). C. Moreno-García, F. Serratosa & X. Jiang, Correspondence edit distance to obtain a set of weighted means of graph correspondences, Pattern Recognition Letters, 134 pp: 29-36, 2020. https://doi.org/10.1016/j.patrec.2018.08.027

­­----2019----

149 (41). Junchi Yan, Minsu Cho, Francesc Serratosa, Gui-Song Xia, Yinqiang Zheng, Editorial. Pattern Recognition Letters, 127 pp:1-2, 2019. https://doi.org/10.1016/j.patrec.2019.03.005

148.  S. Algabli, P. Santacruz & F. Serratosa, Learning the Graph Edit Distance Parameters for Point-Set Image Registration, International Conference on Computer Analysis of Images and Patterns, CAIP 2019, Salerno, Italy, LNCS 11678, pp: 447-456, 2019. https://doi.org/10.1007/978-3-030-29888-3_36

147 (40). C. Moreno-García and F. Serratosa, Generalised Median of Graph Correspondences, Pattern Recognition Letters, 125, pp: 389-395, 2019. https://doi.org/10.1016/j.patrec.2019.05.015

146 (39). C. Garcia-Hernandez, A. Fernandez and F. Serratosa, Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure, Journal of Chemical Information and Modelling, 59 (4), pp: 1410-1421, 2019. https://doi.org/10.1021/acs.jcim.8b00820

145.  X. Cortés, D. Conte & F. Serratosa, Sub-optimal Graph Matching by Node-to-node Assignment Classification, Graph based Representations in Pattern Recognition, GbRPR 2019, Tours, France, LNCS 11510, pp: 35–44, 2019.

144.  E. Rica, S. Álvarez & F. Serratosa, On-line Learning the Edit Costs based on an Embedded model , Graph based Representations in Pattern Recognition, GbRPR 2019, Tours, France, LNCS 11510, pp: 121–130, 2019.

143.  E. Rica, S. Álvarez & F. Serratosa, Learning the Graph edit costs: What do we want to optimise? , Graph based Representations in Pattern Recognition, GbRPR 2019, Tours, France, LNCS 11510, pp: 25–34, 2019.

142.  C. García-Hernandez, A. Fernández & F. Serratosa, Graph edit distance as molecular similarity measure for virtual screening, Graph based Representations in Pattern Recognition, SETAC 2019, Helsinki, Finland, 2019.

141 (38). F. Serratosa, Graph edit distance: Restrictions to be a metric, Pattern Recognition, 90, pp: 250-256, 2019. https://doi.org/10.1016/j.patcog.2019.01.043

----2018----

140 (37). S. Algabli & F. Serratosa, Embedding the node-to-node mappings to learn the Graph edit distance parameters, Pattern Recognition Letters, 112, pp: 353-360, 2018. https://doi.org/10.1016/j.patrec.2018.08.026

139.  X. Cortés, D. Conte, H. Cardot & F. Serratosa, A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance, Syntactic and Structural Pattern Recognition, SSPR2018, LNCS 11004, pp: 326-336, 2018. https://doi.org/10.1007/978-3-319-97785-0_31

138.  P. Santacruz & F. Serratosa, Learning the Sub-optimal Graph Edit Distance edit costs based on an embedded model, Syntactic and Structural Pattern Recognition, SSPR2018, LNCS 11004, pp: 282-292, 2018. https://doi.org/10.1007/978-3-319-97785-0_27

137.  C. Moreno-García & F. Serratosa, Modelling the Generalised Median Correspondence through an Edit Distance, Syntactic and Structural Pattern Recognition, SSPR2018, LNCS 11004, pp: 271-281, 2018. https://doi.org/10.1007/978-3-319-97785-0_26

136.  P. Santacruz & F. Serratosa, Graph Edit Distance Testing through Synthetic Graphs Generation, International Conference on Pattern Recognition, ICPR2018, pp: 572-577, 2018.

135 (36). F. Serratosa, A methodology to generate attributed graphs with a bounded graph edit distance for graph-matching testing, International Journal of Pattern Recognition and Artificial Intelligence, 32 (11), pp: 1850038 (19 pages), 2018. https://doi.org/10.1142/S0218001418500386

----2017----

134.  P. Santacruz, S. Algabli & F. Serratosa, Node matching computation between two large graphs in linear computational cost, Graph based Representations in Pattern Recognition, GbRPR 2017, Capri, Italy, LNCS 10310, pp: 143–153, 2017. http://dx.doi.org/10.1007/978-3-319-58961-9_13

133.  C. Moreno-García, F. Serratosa & X. Jiang, An Edit Distance between Graph Correspondences, Graph based Representations in Pattern Recognition, GbRPR 2017, Capri, Italy, LNCS 10310, pp: 232–241, 2017. https://doi.org/10.1007/978-3-319-58961-9_21

132 (35). C. Moreno-García & F. Serratosa, Obtaining the Consensus of Multiple Correspondences between Graphs through Online Learning, Pattern Recognition Letters, 87, pp: 79-86, 2017. http://dx.doi.org/10.1016/j.patrec.2016.09.003

131 (34).  C. Moreno-García & F. Serratosa, Correspondence Consensus of Two Sets of Correspondences through Optimisation Functions, Pattern Analysis and Applications, 20(1), pp: 201-213, 2017. http://dx.doi.org/10.1007/s10044-015-0486-y

----2016----

130. F. Serratosa, X. Cortés & K. Riesen, On the Relevance of Local Neighbourhoods for Greedy Graph Edit Distance, Syntactic and Structural Pattern Recognition, SSPR2016, LNCS 10029, pp. 121-131, Merida, Mexico, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_11

129. C. Moreno-García, F. Serratosa & X. Cortés, Generalised Median of a Set of Correspondences based on the Hamming Distance, Syntactic and Structural Pattern Recognition, SSPR2016, LNCS 10029, pp: 507-518, Merida, Mexico, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_45

128. C. Moreno-García, X. Cortés & F. Serratosa, A Graph Repository for Learning Error-Tolerant Graph Matching, Syntactic and Structural Pattern Recognition, SSPR2016, LNCS 10029, pp: 519-529, Merida, Mexico, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_46

127. F. Serratosa, X. Cortés & C. Moreno-García, Graph Edit Distance or Graph Edit Pseudo-Distance?, Syntactic and Structural Pattern Recognition, SSPR2016, LNCS 10029, pp: 530–540, Merida, Mexico, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_47

126 X. Cortés, F. Serratosa & C. Moreno-García, Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA2016, pp: 1-6, 2016. http://dx.doi.org/10.1109/ETFA.2016.7733640

125 G. Manzo, F. Serratosa & M. Vento, Interactive pose calibration of a set of cameras for video surveillance, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA2016, pp: 1-4, 2016. http://dx.doi.org/10.1109/ETFA.2016.7733663

124 (33) G. Manzo, F. Serratosa & M. Vento, Online Human Assisted and Cooperative Pose Estimation of 2D-cameras, Expert Systems With Applications, 60, pp: 258-268, 2016. http://dx.doi.org/10.1016/j.eswa.2016.05.012

123 C. Moreno-García, M. Aceves-Martins & F. Serratosa, Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research, Computación y Sistemas, 20(1), pp: 7-17, 2016. http://dx.doi.org/10.13053/CyS-20-1-2360

122 (32) G. Sanroma, A Penate-Sanchez, R. Alquezar, F. Serratosa, F. Moreno-Noguer, J. Andrade-Cetto & M.A. Gonzalez, MSClique: Multiple Structure Discovery through the Maximum Weighted Clique Problem, PLOS ONE, 2016. http://dx.doi.org/10.1371/journal.pone.0145846

121 (31) X. Cortés & F. Serratosa, Cooperative Pose Estimation of a Fleet of Robots based on Interactive Points Alignment, Expert Systems With Applications, 45, pp: 150-160, 2016. http://dx.doi.org/10.1016/j.eswa.2015.09.049

120 (30) X. Cortés & F. Serratosa, Learning Graph Matching Substitution Weights based on the Ground Truth Node Correspondence, International Journal of Pattern Recognition and Artificial Intelligence, 30(2), pp: 1650005 [22 pages], 2016. http://dx.doi.org/10.1142/S0218001416500051

119 (29) C. Moreno-García & F. Serratosa, Consensus of Multiple Correspondences to increase the accuracy in Image Registration, Computer Vision and Image Understanding, 142, pp: 50-64, 2016. http://dx.doi.org/10.1016/j.cviu.2015.08.008

----2015----

118 (28) C. Moreno-García & F. Serratosa, Online Learning the Consensus of Multiple Correspondences Between Sets, Knowledge based Systems, 90, pp: 49-57, 2015. http://dx.doi.org/10.1016/j.knosys.2015.09.034

                  Corrigendum: http://deim.urv.cat/~francesc.serratosa/2015_Moreno_Serratosa_KBS_Corrigendum.pdf

117 (27) F. Serratosa & X. Cortés, Graph Edit Distance: moving from global to local structure to solve the graph-matching problem, Pattern Recognition Letters, 65, pp: 204-210, 2015. http://dx.doi.org/10.1016/j.patrec.2015.08.003

116 (26). M. Ferrer, F. Serratosa & K. Riesen, Improving Bipartite Graph Matching by Assessing the Assignment Confidence, Pattern Recognition Letters, 65, pp: 29-36, 2015. http://dx.doi.org/10.1016/j.patrec.2015.07.010

115 (25). F. Serratosa, Computation of Graph Edit Distance: Reasoning about Optimality and Speed-up, Image and Vision Computing, 40, pp: 38-48, 2015. http://dx.doi.org/10.1016/j.imavis.2015.06.005

114. X. Cortés, F. Serratosa & C. Moreno-García, Ground truth Correspondence between nodes to Learn Graph-Matching Edit-Costs, Computer Analysis of Images and Patterns, CAIP 2015, Valletta, Malta, LNCS 9256, pp: 113-124, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_10

113. M. Ferrer, F. Serratosa & K. Riesen, Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation, International Conference on Machine Learning and Data Mining, MLDM 2015, LNAI 9166, pp: 17-31, Hamburg, Germany, 2015. (Best Paper Award). http://dx.doi.org/10.1007/978-3-319-21024-7_2

112.  X. Cortés, F. Serratosa & C. Moreno-García, On the Influence of Node Centralities on Graph Edit Distance for Graph Classification, Graph based Representations in Pattern Recognition, GbRPR 2015, Beijing, China, LNCS 9069, pp: 231-241, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_23

111.  M. Ferrer, F. Serratosa & K. Riesen, A First Step Towards Exact Graph Edit Distance Using Bipartite Graph Matching, Graph based Representations in Pattern Recognition, GbRPR 2015, Beijing, China, LNCS 9069, pp: 77-86, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_8

110.  C. Moreno-García, F. Serratosa & X. Cortés, Consensus of Two Graph Correspondences through a Generalisation of the Bipartite Graph Matching Algorithm, Graph based Representations in Pattern Recognition, GbRPR 2015, Beijing, China, LNCS 9069, pp: 87-97, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_9

109. C. Moreno-García, X. Cortés & F. Serratosa, Iterative Versus Voting Method to Reach Consensus Given Multiple Correspondences of Two Sets, Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, Santiago de Compostela, Spain, LNCS 9117, pp: 530-540, 2015.

108 (24).  X. Cortés & F. Serratosa, Learning Graph-Matching Edit-Costs based on the Optimality of the Oracle's Node Correspondences, Pattern Recognition Letters, 56, pp: 22 - 29, 2015. http://dx.doi.org/10.1016/j.patrec.2015.01.009

107 (23).  F. Serratosa, Speeding up Fast Bipartite Graph Matching trough a new cost matrix, International Journal of Pattern Recognition and Artificial Intelligence, 29 (2), 1550010, [17 pages] 2015. http://dx.doi.org/10.1142/S021800141550010X

106 (22).  F. Serratosa & X. Cortés, Interactive Graph-Matching using Active Query Strategies, Pattern Recognition 48 (4), pp: 1364-1373, 2015. http://dx.doi.org/10.1016/j.patcog.2014.10.033

105. X. Cortés, F. Serratosa & C. Moreno-García, An Interactive Model for Structural Pattern Recognition based on the Bayes Classifier, International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015, Lisbon, Portugal, pp: 240-247, 2015. http://dx.doi.org/10.5220/0005201602400247

104 (21).  X. Cortés & F. Serratosa, An Interactive Method for the Image Alignment problem based on Partially Supervised Correspondence, Expert Systems With Applications 42 (1), pp: 179 - 192, 2015. http://dx.doi.org/10.1016/j.eswa.2014.07.051

----2014---- 

103.   C. Moreno-García & F. Serratosa, Fast and Efficient Palmprint Identification of a Small Sample within a Full Image, Computación y Sistemas, 18 (4), pp: 683–691, 2014. http://dx.doi.org/10.13053/CyS-18-4-2059

102. X. Cortés, C. Moreno-García & F. Serratosa, Learning Graph-Matching Substitution Costs based on the Optimality of the Oracle’s Correspondence, Iberoamerican Congress on Pattern Recognition, CIARP2014, LNCS 8827, pp: 506–514, Puerto Vallarta, Mexico, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_62

101. C. Moreno-García, X. Cortés & F. Serratosa, Partial to Full Image Registration based on Candidate Positions and Multiple Correspondences, Iberoamerican Congress on Pattern Recognition, CIARP2014, LNCS 8827, pp: 745–753, Puerto Vallarta, Mexico, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_90

100. F. Serratosa & X. Cortés, Human Interaction to Improve the Image Alignment on a Cooperative Robotic Framework, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA2014, PD-001198, Barcelona, Spain, 2014. http://dx.doi.org/10.1109/ETFA.2014.7005104

99. C. Moreno-García & F. Serratosa, Weighted Mean Assignment of a Pair of Correspondences using Optimisation functions, Syntactic and Structural Pattern Recognition, SSPR2014, LNCS 8621, pp: 301-311, Joensuu, Finland, 2014. http://dx.doi.org/10.1007/978-3-662-44415-3_31

98. F. Serratosa & X. Cortés, Edit Distance computed by Fast Bipartite Graph Matching, Syntactic and Structural Pattern Recognition, SSPR2014, LNCS 8621, pp: 253-262, Joensuu, Finland, 2014. http://dx.doi.org/10.1007/978-3-662-44415-3_26

97 (20). F. Serratosa, Fast Computation of Bipartite Graph Matching, Pattern Recognition Letters 45, pp: 244 - 250, 2014. http://dx.doi.org/10.1016/j.patrec.2014.04.015

Corrigendum: http://www.sciencedirect.com/science/article/pii/S0167865514003468

96 (19). A. Solé-Ribalta, D. Sánchez, M. Batet, F. Serratosa, Towards the estimation of feature-based semantic similarity using multiple ontologies, Knowledge-Based Systems 55, pp: 101 - 113, 2014. http://dx.doi.org/10.1016/j.knosys.2013.10.015

----2013----

95. X. Cortés, C. Moreno & F. Serratosa, Improving the Correspondence Establishment based on Interactive Homography Estimation, Computer Analysis of Images and Patterns, CAIP2013, York, Unated Kindom, LNCS 8048 , pp: 457-465, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_57

94. X. Cortés & F. Serratosa, Active-Learning Query Strategies applied to select a Graph Node given a Graph Labelling, Graph based Representations GbR2013, Austria, Vienna, LNCS 7877, pp: 61-70, 2013. http://dx.doi.org/10.1007/978-3-642-38221-5_7

93 (18). A. Solé & F. Serratosa, Graduated Assignment Algorithm for Multiple Graph Matching based on a Common Labelling, International Journal of Pattern Recognition and Artificial Intelligence 27 (1), pp: 1350001 [27 pages], 2013.  http://dx.doi.org/10.1142/S0218001413500018

92 (17). F. Serratosa, X. Cortés & A. Solé, Component Retrieval based on a Database of Graphs for Hand-Written Electronic-Scheme Digitalisation, Expert Systems With Applications 40, pp: 2493 -2502, 2013. http://dx.doi.org/10.1016/j.eswa.2012.10.071

----2012----

91 (16). A. Solé, F. Serratosa & A. Sanfeliu, On the Graph Edit Distance cost: Properties and Applications, International Journal of Pattern Recognition and Artificial Intelligence 26 (5), 1260004 [21 pages], 2012. http://dx.doi.org/10.1142/S021800141260004X

90. X. Cortés, F. Serratosa & A. Solé, A comparison between Structural and Embedding methods for Graph Classification, Syntactic and Structural Pattern Recognition, SSPR2012, Hiroshima, Japan, LNCS 7626 , pp: 234-242, 2012. ISBN: 978-3-642-34165-6. http://dx.doi.org/10.1007/978-3-642-34166-3_26

89. N. Rebagliati, A. Solé, M. Pelillo & F. Serratosa, On The Relation Between The Common Labelling and The Median Graph, Syntactic and Structural Pattern Recognition, SSPR2012, Hiroshima, Japan, LNCS 7626 , pp: 107-115, 2012. ISBN: 978-3-642-34165-6. http://dx.doi.org/10.1007/978-3-642-34166-3_12

88. F. Serratosa, X. Cortés & A. Solé, Graph Database Retrieval based on Metric-Trees, Syntactic and Structural Pattern Recognition, SSPR2012, Hiroshima, Japan, LNCS 7626 , pp: 437-447, 2012. ISBN: 978-3-642-34165-6. http://dx.doi.org/10.1007/978-3-642-34166-3_48

87. X. Cortés, F. Serratosa & A. Solé, Active Graph Matching based on Pairwise Probabilities between nodes, Syntactic and Structural Pattern Recognition, SSPR2012, Hiroshima, Japan, LNCS 7626 , pp: 98-106, 2012. ISBN: 978-3-642-34165-6. http://dx.doi.org/10.1007/978-3-642-34166-3_11

86. F. Serratosa, X. Cortés, A. Solé, Interactive Graph Matching by means of Imposing the Pairwise Costs, International Conference on Pattern Recognition, ICPR2012, Tsukuba, Japan, pp: 1298 - 1301, 2012. ISBN: 978-4-9906441-1-6. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6460377

85. N. Rebagliati, A. Solé, M. Pelillo, F. Serratosa, Computing the Graph Edit Distance Using Dominant Sets, International Conference on Pattern Recognition, ICPR2012, Tsukuba, Japan, pp: 1080-1083, 2012. ISBN: 978-4-9906441-1-6. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?reload=true&arnumber=6460323

84 (15). G. Sanromà, R. Alquézar, F. Serratosa & B. Herrera, Smooth Point-set Registration using Neighbouring Constraints, Pattern Recognition Letters 33, pp: 2029-2037, 2012. http://dx.doi.org/10.1016/j.patrec.2012.04.008

83 (14). F. Serratosa, R. Alquézar & N. Amézquita, A Probabilistic Integrated Object Recognition and Tracking Framework, Expert Systems With Applications 39, pp: 7302-7318, 2012. http://dx.doi.org/10.1016/j.eswa.2012.01.088

82. A. Solé, G. Sanromà, F. Serratosa & R. Alquézar, Group-wise sparse correspondences between images based on a common labelling approach, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP2012, Rome, Italy, Volume 1, pp: 269-278, 2012.

81 (13). G. Sanromà, R. Alquézar, & F. Serratosa, A New Graph Matching Method for Point-Set Correspondence using the EM Algorithm and Softassign, Computer Vision and Image Understanding 116(2), pp: 292-304, 2012. http://dx.doi.org/10.1016/j.cviu.2011.10.009

80 (12). D. Sánchez, A. Solé, M. Batet & F. Serratosa, Enabling semantic similarity estimation across multiple ontologies: an evaluation in the biomedical domain, Journal of Biomedical Informatics 45 (1), pp: 141-155, 2012. http://dx.doi.org/10.1016/j.jbi.2011.10.005

----2011----

79. F. Serratosa, A. Solé & X. Cortés, K-nn queries in Graph Databases using M-Trees, Computer Analysis of Images and Patterns, CAIP2011, Seville, Spain, LNCS 6854, pp: 202-210, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_25

78 (11). A. Solé & F. Serratosa, Models and Algorithms for computing the Common Labelling of a set of Attributed Graphs, Computer Vision and Image Understanding 115 (7), pp: 929-945, 2011. http://dx.doi.org/10.1016/j.cviu.2010.12.007

77. F. Serratosa, A. Solé & X. Cortés, Automatic Learning of Edit Costs based on Interactive & Adaptive Graph Recognition, Graph based Representations, GbR2011, Munster, Germany, LNCS 6658 pp: 152,163, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_16

76. G. Sanromà, R. Alquézar, & F. Serratosa, Smooth Simultaneous Structural Graph Matching and Point-Set Registration, Graph based Representations, GbR2011, Munster, Germany, LNCS 6658 pp: 142,151 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_15

75. A. Solé & F. Serratosa, Exploration of the labelling Space given graph edit distance costs, Graph based Representations, GbR2011, Munster, Germany, LNCS 6658 pp: 164,174, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_17

74. D. Ródenas, F. Serratosa & A. Solé, Parallel Graduated Assignment Algorithm for Multiple Graph Matching based on a Common Labelling, Graph based Representations, GbR2011, Munster, Germany, LNCS 6658 pp: 132,141, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_14

73. D. Ródenas, F. Serratosa & A. Solé, Graph Matching on a Low-cost & Parallel Architecture, Iberian Conference on Pattern Recognition and Image Analysis, IBPRIA2011, Gran Canaria, Spain, LNCS 6669, pp: 508-515, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_63

72. F. Serratosa & A. Solé, A Probabilistic Framework to obtain a Common Labelling between Attributed Graphs, Iberian Conference on Pattern Recognition and Image Analysis, IBPRIA2011, Gran Canaria, Spain, LNCS 6669, pp: 180-190, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_64

----2010----

71. F. Serratosa, A. Solé & E. Vidiella, Graph Indexing and Retrieval based on Median Graphs, Mexican Conference on Pattern Recognition, MCPR2010, Puebla, Mexico, LNCS 6256, pp: 311-321, 2010. http://dx.doi.org/10.1007/978-3-642-15992-3_33

70. A. Solé & F. Serratosa, Graduated Assignment Algorithm for Finding the Common Labelling of a set of Graphs, Syntactic and Structural Pattern Recognition, SSPR2010, Izmir, Turkey, LNCS 6218, pp: 180-190, 2010. http://dx.doi.org/10.1007/978-3-642-14980-1_17

69. G. Sanromà, R. Alquézar & F. Serratosa, Attributed Graph Matching for Image-Features Association using SIFT Descriptors, Syntactic and Structural Pattern Recognition, SSPR2010, Izmir, Turkey, LNCS 6218, pp: 254-263, 2010. http://dx.doi.org/10.1007/978-3-642-14980-1_24

68. G. Sanromà, R. Alquézar & F. Serratosa, A Discrete Labelling Approach to Attributed Graph Matching using SIFT Features, International Conference on Pattern Recognition, ICPR2010, Istanbul, Turkey, pp: 954-957, 2010. http://dx.doi.org/10.1109/ICPR.2010.239

67. G. Sanromà, R. Alquézar & F. Serratosa, Graph Matching using SIFT Descriptors, an application to pose recovery of a mobile robot, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP2010, Angers, France, pp: 249-254, 2010.

66 (10). M. Ferrer, E. Valveny, F. Serratosa, K. Riesen & H. Bunke. Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces, Pattern Recognition 43 (4), pp: 1642-1655, 2010. http://dx.doi.org/10.1016/j.patcog.2009.10.013

----2009----

65. F. Serratosa, N. Amézquita & R. Alquézar, Experimental Assessment of Probabilistic Integrated Object Recognition and Tracking Methods, Iberoamerican Congress on Pattern Recognition, CIARP2009, Guadalajara, México, LNCS 5859, pp: 817-824, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_96

64. A. Solé & F. Serratosa, On the Computation of the Common Labelling of a Set of Attributed Graphs, Iberoamerican Congress on Pattern Recognition, CIARP2009, Guadalajara, México, LNCS 5859, pp: 137-144, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_16

63. M. Ferrer, E. Valveny, F. Serratosa, I. Bardaj_ & H. Bunke, Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph, Computer Analysis of Images and Patterns, CAIP2009: Munster, Germany, LNCS 5702, pp: 342-350, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_42

62 (9). M. Ferrer, E. Valveny & F. Serratosa, Median graphs: A genetic approach based on new theoretical properties, Pattern Recognition 42 (9), pp: 2003-2012, 2009. http://dx.doi.org/10.1016/j.patcog.2009.01.034

61 (8). M. Ferrer, E. Valveny & F. Serratosa: Median graph: A new exact algorithm using a distance based on the maximum common subgraph, Pattern Recognition Letters 30 (5), pp: 579-588, 2009. http://dx.doi.org/10.1016/j.patrec.2008.12.014

60. A. Solé & F. Serratosa, A structural and semantic probabilistic model for matching and representing a set of graphs, Graph Based Representations, GbR2009, Venice, Italy, LNCS 5534, pp: 164-173, 2009. http://dx.doi.org/10.1007/978-3-642-02124-4_17

59. M. Ferrer, E. Valveny & F. Serratosa, Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subgraph, Pattern Recognition and Image Analysis, Fourth Iberian Conference, IbPRIA2009, LNCS 5524, Oporto, Portugal, pp: 346-353, 2009. http://dx.doi.org/10.1007/978-3-642-02172-5_45

58. R. Alquézar,  N. Amézquita & F. Serratosa, Tracking Deformable Objects and Dealing with same class Object Occlusion,  International Conference on Computer Vision Theory and Applications, VISAPP2009, Lisbon, Portugal, pp: 590-594 , 2009.

----2008---- 

57. G. Sanromà, F. Serratosa & R. Alquézar, Shape Learning with Function-Described Graphs, International Congress on Image Analysis and Recognition, ICIAR2008, LNCS 5112,  Povoa de Varzim, Portugal, pp: 475-484, 2008. http://dx.doi.org/10.1007/978-3-540-69812-8_47

56. N. Amézquita, R. Alquézar  & F. Serratosa, Dealing with Occlusion in a Probabilistic Object Tracking Method,  IEEE Computer Vision and Pattern Recognition, CVPR2008, Anchorage, Alaska, USA, CVPRW, Print ISBN: 978-1-4244-2339-2, pp: 1-8, 2008.

55. G. Sanromà, F. Serratosa & R. Alquézar, Hybrid Genetic Algorithm and Procrustes Analysis for Enhancing the Matching of Graphs Generated from Shapes, Proc. Syntactic and Structural Pattern Recognition, SSPR2008, LNCS 5342, Orlando, Florida, USA, pp: 298-307, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_34

54. M. Ferrer, E. Valveny, F. Serratosa & H. Bunke, Exact Median Graph Computation via Graph Embedding, Syntactic and Structural Pattern Recognition, SSPR2008, LNCS 5342, Orlando, Florida, pp: 15-24, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_6

53. G. Sanromà, F. Serratosa & R. Alquézar, Improving the Matching of Graphs Generated from Shapes by the Use of Procrustes Distances into a Clique-based MAP Formulation, 19th International Conference on Pattern Recognition, ICPR2008, Tampa, Florida, USA, Volume 2, pp: 1-4, 2008. http://dx.doi.org/10.1109/ICPR.2008.4761107

52. M. Ferrer, E. Valveny, F. Serratosa, K. Riesen & H. Bunke An Approximate Algorithm for Median Graph Computation using Graph Embedding, 19th International Conference on Pattern Recognition, ICPR2008, Tampa, Florida, USA, Volume 2, pp: 1-4, 2008. http://dx.doi.org/10.1109/ICPR.2008.4761354

51 (7). F. Serratosa & G. Sanromà, A Fast Approximation of the Earth-Movers Distance between Multi-Dimensional Histograms, International Journal of Pattern Recognition and Artificial Intelligence 22 (8), pp: 1539 -1558, 2008.  http://dx.doi.org/10.1142/S0218001408006880

----2007----

50. M. Ferrer, E. Valveny & F. Serratosa, Bounding the Size of the Median Graph, Pattern Recognition and Image Analysis, Third Iberian Conference, IbPRIA2007, LNCS 4478, Girona, Spain, pp: 491-498, 2007. http://dx.doi.org/10.1007/978-3-540-72849-8_62

49. M. Ferrer, F. Serratosa & E. Valveny, Evaluation of the Spectral Methods for Median Graph Computation, Pattern Recognition and Image Analysis, Third Iberian Conference, IbPRIA2007, LNCS 4478, Girona, Spain, pp: 580-587, 2007. http://dx.doi.org/10.1007/978-3-540-72849-8_73

48. M. Ferrer, F. Serratosa & E. Valveny, On the relation between the median and the maximum common subgraph of a set of graphs, 6th IAPR -TC-15  Workshop on Graph-based Representations in Pattern Recognition, Gbr2007, LNCS 4538, Alacant, Spain, pp: 351-360 , 2007. http://dx.doi.org/10.1007/978-3-540-72903-7_32

47. N. Amézquita, R. Alquézar  & F. Serratosa, A New Method for Object Tracking Based on Regions Instead of Contours,  IEEE Computer Vision and Pattern Recognition, CVPR2007, Minneapolis, Minnesota, USA, Print ISBN: 1-4244-1180-7, pp: 1 -8 , 2007.

46. F. Serratosa, G. Sanromà, & A. Sanfeliu, A New Algorithm to Compute the Distance between Multi-dimensional Histograms, Iberoamerican Congress on Pattern Recognition, CIARP2007, LNCS, 4756, Villa del Mar-Valparaiso, Chile, pp: 115 - 123, 2007. http://dx.doi.org/10.1007/978-3-540-76725-1_13

45. F. Serratosa & G. Sanromà, Modelling Intermittently Present Features using non-Linear Point Distribution Models, IEEE, Pacific-Rim on Image and Video Technology, PSIVT2007, LNCS 4872, Santigao, Chile, pp: 260-273 , 2007. http://dx.doi.org/10.1007/978-3-540-77129-6_25

----2006---- 

44 (6). F. Serratosa, A. Sanfeliu, Signatures versus histograms: Definitions, distances and algorithms. Pattern Recognition 39 (5), pp: 921-934, 2006. http://dx.doi.org/10.1016/j.patcog.2005.12.005

43. F. Serratosa & G. Sanromà, An Efficient Distance between Multi-dimensional Histograms for Comparing images, Proc. Syntactic and Structural Pattern Recognition, SSPR2006, LNCS 4109, Hong Kong China, pp: 412-421, 2006. http://dx.doi.org/10.1007/11815921_45

42. F. Serratosa & A. Sanfeliu, A Fast and Exact Modulo-Distance between Histograms, Proc. Syntactic and Structural Pattern Recognition, SSPR2006, LNCS 4109, Hong Kong, China, pp: 394-402, 2006. http://dx.doi.org/10.1007/11815921_43

41. F. Serratosa & A. Sanfeliu, Vision-Based Robot Positioning by an Exact Distance Between Histograms, 18th International Conference on Pattern Recognition, ICPR2006, Hong Kong China, Volume 2, pp: 849-852, 2006. http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.1179

40. M. Ferrer, E. Valveny & F. Serratosa, Spectral median graphs applied to graphical recognition, 11th Iberoamerican Congress on Pattern Recognition, CIARP2006, LNCS, 4225, Cancun, Mexico, pp:  774-783 , 2006. http://dx.doi.org/10.1007/11892755_80

39. N. Amézquita, R. Alquézar & F. Serratosa, Object recognition and tracking in video sequences: a new integrated methodology, 11th Iberoamerican Congress on Pattern Recognition, CIARP2006, LNCS, 4225, Cancun, Mexico, pp:  481-490 , 2006. http://dx.doi.org/10.1007/11892755_50

38. F. Serratosa, N. Amézquita & R. Alquézar, Combining neural networks and clustering techniques for object recognition in indoor video sequences, 11th Iberoamerican Congress on Pattern Recognition, CIARP2006, LNCS, 4225, Cancun, Mexico, pp:  399-405, 2006. http://dx.doi.org/10.1007/11892755_41

----2005---- 

37 (5). E. Staffeti, A. Grau, F. Serratosa & A. Sanfeliu, Object and Image indexing based on Region Connection Calculus and Oriented Matroids theory, Discrete Applied Mathematics 147 (2-3), pp: 345-361, 2005. http://dx.doi.org/10.1016/j.dam.2004.09.019

36. F. Serratosa & A. Sanfeliu, Matching Attributed Graphs: Second Order Probabilities for pruning the search tree, Pattern Recognition and Image Analysis, Second Iberian Conference, IbPRIA2005, LNCS 3523, Estoril, Portugal, pp: 131-138, 2005. http://dx.doi.org/10.1007/11492542_17

35. M. Ferrer, F. Serratosa & A. Sanfeliu, Synthesis of Median Spectral Graphs, Pattern Recognition and Image Analysis, Second Iberian Conference, IbPRIA2005, LNCS 3523, Estoril, Portugal, pp: 139-146, 2005. http://dx.doi.org/10.1007/11492542_18

34. F. Serratosa & A. Sanfeliu, A Fast Distance Between Histograms, Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP2005, LNCS 3773, Havana, Cuba, pp. 1027-1035, 2005. http://dx.doi.org/10.1007/11578079_105

----2004----

33 (4). A. Sanfeliu, F. Serratosa & R. Alquézar, Second-Order Random Graphs for modelling sets of Attributed Graphs and their application to object learning and recognition, International Journal of Pattern Recognition and Artificial Intelligence 18 (3), pp: 375-396, 2004. http://dx.doi.org/10.1142/S0218001404003253

32. F. Serratosa & A. Sanfeliu, Distance measures between Attributed Graphs and Second-order Random Graphs, Proc. Syntactic and Structural Pattern Recognition, SSPR2004, LNCS 3138, pp: 1135-1144, 2004. http://dx.doi.org/10.1007/978-3-540-27868-9_125

31. F. Serratosa, A. Grau, & A. Sanfeliu, Distance between 2D-scenes based on Oriented Matroids theory, 17th International Conference on Pattern Recognition, ICPR2004, Cambridge, United Kingdom, Volume 2, pp: 196-199, 2004. http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334094

----2003----

30 (3). F. Serratosa, R. Alquézar & A. Sanfeliu, Function-Described Graphs for modelling objects represented by attributed graphs, Pattern Recognition 36 (3), pp: 781-798, 2003. http://dx.doi.org/10.1016/S0031-3203(02)00107-3

29. E. Staffeti, A. Grau, F. Serratosa & A. Sanfeliu, Oriented Matroids for Shape Representation and Indexing, Pattern Recognition and Image Analysis, First Iberian Conference, IbPRIA2003, LNCS 2652, Palma de Mallorca, Spain, pp: 1012-1019, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_117

28. E. Staffeti, A. Grau, F. Serratosa & A. Sanfeliu, Indexación de Imágenes Basada en la Teoría de las Matroides Orientadas, Jornadas de Automática, León Spain, 2003.

27. A. Sanfeliu & F. Serratosa, Learning and recognising 3D models represented by multiple views by means of methods based on random graphs, International Congress on Image Processing, ICIP2003, Barcelona, Spain, Volume 2 pp: 29,32, 2003.

26. E. Staffeti, A. Grau, F. Serratosa & A. Sanfeliu, Shape Representation and Indexing based on Region Connection Calculus and Oriented Matroids theory, Discrete Geometry for Computer Imagery, 11th International Conference, DGCI2003, LNCS 2886, Napoli, Italy, pp: 267-276, 2003. http://dx.doi.org/10.1007/978-3-540-39966-7_25

----2002----

25 (2). A. Sanfeliu, R. Alquézar, J. Andrade, J. Climent, F. Serratosa & J. Vergés, Graph-based Representations and Techniques for Image Processing and Image Analysis, Pattern Recognition 35 (3), pp: 639-650, 2002. http://dx.doi.org/10.1016/S0031-3203(01)00066-8

24. F. Serratosa, R. Alquézar & A. Sanfeliu, Estimating the Joint Probability Distribution of Random Vertices and Arcs by means of Second-order Random Graphs, Proc. Syntactic and Structural Pattern Recognition, SSPR2002, LNCS 2396, Windsor, Canada, pp: 252-262, 2002. http://dx.doi.org/10.1007/3-540-70659-3_26

23. A. Grau, J. Climent, F. Serratosa & A. Sanfeliu, Textprint: A new algorithm to discriminate textures structurally, Syntactic and Structural Pattern Recognition, SSPR2002, LNCS 2396, Windsor, Canada, pp: 368-377, 2002. http://dx.doi.org/10.1007/3-540-70659-3_38

22. F. Serratosa, R. Alquézar & A. Sanfeliu, Modelling and recognising 3D-objects described by multiple views using Function-Described Graphs, 16th International Conference on Pattern Recognition, ICPR2002, Quebec, Canada, vol. 2, pp: 140-143, 2002. http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.1048257

21(1). F. Serratosa, R. Alquézar & A. Sanfeliu, Synthesis of function-described graphs and clustering of attributed graphs, International Journal of Pattern Recognition and Artificial Intelligence 16 (6), pp: 621-655, 2002. http://dx.doi.org/10.1142/S0218001402001915

----2001---- 

20. A. Sanfeliu, J. Andrade-Cetto, R. Alquézar, J. Aranda, J. Climent, A. Grau, F. Serratosa & J. Vergés-Llahí, MARCO: A mobile robot with learning capabilities to perceive and interact with its environment, Simposium Nacional de Reconocimiento de Formas y Analisis de Im_genes, SNRFAI2001, Castelló, Spain, vol. 2, pp: 219-224, 2001.

----2000---- 

19. F. Serratosa, A. Sanfeliu & R. Alquézar, Function-Described Graphs: A measure of similarity based on probabilities, Pattern Recognition and Applications, IOS Press, Edited by M.I. Torres and A. Sanfeliu, pp: 59-68, 2000.

18. F. Serratosa, R. Alquézar & A. Sanfeliu, Efficient algorithms for matching attributed graphs and function-described graphs, Proc.15th International Conference on Pattern Recognition, ICPR2000, Barcelona, Spain, vol. 2, pp: 871-876, 2000. http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.906212

17. Sanfeliu, F. Serratosa & R. Alquézar, Clustering of attributed graphs and unsupervised synthesis of function-described graphs, Proc. 15th International Conference on Pattern Recognition, ICPR2000, Barcelona, Spain, vol. 2, pp: 1026-1029, 2000. http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.906248

16. R. Alquézar, F. Serratosa & A. Sanfeliu, Distance measures between Attributed Graphs and Function-Described Graphs relaxing 2nd-order constraints, Proc. Syntactic and Structural Pattern Recognition, SSPR2000, LNCS 1876, Alacant, Spain, pp: 277-286, 2000. http://dx.doi.org/10.1007/3-540-44522-6_29

15. A. Sanfeliu, A. Grau, J. Climent, R. Alquézar, F. Serratosa, J. Aranda, J. Vergés-Llahí & J. Andrade-Cetto, Pattern Recognition at the IRI-CSIC/ESAII Group, Pattern Recognition Advances in Iberoamerica, FORO2000, Barcelona, Spain, pp. 347-354, 2000.

14. F. Serratosa, Function-Described Graphs for Structural Pattern Recognition. Doctoral PhD, Universitat Politècnica de Catalunya, 2000.  http://deim.urv.cat/~francesc.serratosa/FDG_Thesis_Dissertation.pdf

----1999---- 

13. F. Serratosa, A. Sanfeliu & R. Alquézar, Function-described graphs: an improvement on random graphs. IV Simposium Iberoamericano de Reconocimiento de Patrones, SIARP1999, La Habana, Cuba, pp. 655-665, 1999.

12. F. Serratosa, A. Sanfeliu & R. Alquézar, Function-Described Graphs: A measure of similarity based on probabilities, 8th National Symposium on Pattern Recognition and Image Analysis, NSPRIA1999, Bilbao, Spain, Vol. I, pp. 421-428, 1999.

11. J. Vergés-Lahí, A. Sanfeliu, F. Serratosa & R. Alquézar, Face recognition: Graph matching versus neural techniques, 8th National Symposium on Pattern Recognition and Image Analysis, NSPRIA1999, Bilbao, Spain, Vol. I, pp. 259-266, 1999.

10. D. Riaño & F. Serratosa, Unsupervised synthesis of Function-Described Graphs, Proceedings of the 2ond Workshop on Graph Based Representations GbR1999, pp: 165-171, Vienna, Austria, 1999.

9. F. Serratosa, R. Alquézar & A. Sanfeliu, Function-Described Graphs: A fast algorithm to compute a sub-optimal matching measure, Proceedings of the 2ond Workshop on Graph Based Representations GbR1999, pp: 71-77, Vienna, Austria, 1999.

8. F. Serratosa, R. Alquézar & A. Sanfeliu, Function-Described Graphs for structural pattern recognition, Technical report DEIM-RR-99-006, Universitat Rovira i Virgili, Tarragona, Spain, 1999.

----1998----

7. R. Alquézar, A. Sanfeliu & F. Serratosa, Synthesis of Function-Described Graphs, Advances in Pattern Recognition, Proc. Joint IAPR Int. Workshops SSPR1998 and SPR1998, SSPR1998, LNCS 1451,Sydney, Australia, pp. 112-121, 1998. http://dx.doi.org/10.1007/BFb0033229

6. F. Serratosa, A. Sanfeliu & R. Alquézar, Function-described graphs: Distance and matching. Technical Report IRI-DT-9803, Universitat Politècnica de Catalunya, Institut de Robòtica i Informàtica Industrial, Barcelona, Spain, 1998.

----1997---- 

5. F. Serratosa & A. Sanfeliu, Function-Described Graphs applied to 3D object recognition. 9th Int. Conf. Image Analysis and Processing, Image Analysis and Processing, ICIAP1997, LNCS 1310, Firenze, Italy, Vol. I, pp. 701-708, 1997. http://dx.doi.org/10.1007/3-540-63507-6_263

4. F. Serratosa & A. Sanfeliu, Function-Described Graphs. 7th National Symposium on Pattern Recognition and Image Analysis, NSPRIA1997, Barcelona, Spain, Vol. I, pp. 37-42, 1997.

----1995---- 

3. F. Serratosa, Comparación de cadenas para el reconocimiento de patrones,  Seminario anual de automática y electrónica industrial SAAEI1995, Tarragona, Spain, pp. 469-472, 1995.

2. 1995. F. Serratosa, P. Millán & E. Montseny, Systolic processors applied to computer vision systems.  Computer Architectures for Machine Perception CAMP1995, Como, Italy, pp. 178-183, 1995.

----1994---- 

1. P. Millán, F. Serratosa & E. Montseny, Procesadores sistólicos aplicados a visión por computador.  III Jornadas de Visión por Computador JVC1994, Málaga, Spain, pp. 167-178, 1994.