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N.S.T. Hirata
Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil

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Journal article
Published: 05 August 2021 in Mathematics
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Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.

ACS Style

Nina Hirata; George Papakostas. On Machine-Learning Morphological Image Operators. Mathematics 2021, 9, 1854 .

AMA Style

Nina Hirata, George Papakostas. On Machine-Learning Morphological Image Operators. Mathematics. 2021; 9 (16):1854.

Chicago/Turabian Style

Nina Hirata; George Papakostas. 2021. "On Machine-Learning Morphological Image Operators." Mathematics 9, no. 16: 1854.

Review article
Published: 18 May 2020 in Information Visualization
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Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.

ACS Style

Mateus Espadoto; Nina Hirata; Alexandru C Telea. Deep learning multidimensional projections. Information Visualization 2020, 19, 247 -269.

AMA Style

Mateus Espadoto, Nina Hirata, Alexandru C Telea. Deep learning multidimensional projections. Information Visualization. 2020; 19 (3):247-269.

Chicago/Turabian Style

Mateus Espadoto; Nina Hirata; Alexandru C Telea. 2020. "Deep learning multidimensional projections." Information Visualization 19, no. 3: 247-269.

Original paper
Published: 03 January 2020 in International Journal on Document Analysis and Recognition (IJDAR)
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We revisit graph grammar and graph parsing as tools for recognizing graphics. A top-down approach for parsing families of handwritten graphics containing different kinds of symbols and of structural relations is proposed. It has been tested on two distinct domains, namely the recognition of handwritten mathematical expressions and of handwritten flowcharts. In the proposed approach, a graphic is considered as a labeled graph generated by a graph grammar. The recognition problem is translated into a graph parsing problem: Given a set of strokes (input data), a parse tree which represents the best interpretation is extracted. The graph parsing algorithm generates multiple interpretations (consistent with the grammar) that can be ranked according to a global cost function that takes into account the likelihood of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to rules defined in the graph grammar. To constrain the number of partitions to be evaluated, we propose the use of a hypothesis graph, built from data-driven machine learning techniques, to encode the most likely symbol and relation hypotheses. Within this approach, it is easy to relax the stroke ordering constraint allowing interspersed symbols, as opposed to some previous works. Experiments show that our method obtains accuracy comparable to methods specifically developed to recognize domain-dependent data.

ACS Style

Frank Julca-Aguilar; Harold Mouchère; Christian Viard-Gaudin; Nina Hirata. A general framework for the recognition of online handwritten graphics. International Journal on Document Analysis and Recognition (IJDAR) 2020, 23, 143 -160.

AMA Style

Frank Julca-Aguilar, Harold Mouchère, Christian Viard-Gaudin, Nina Hirata. A general framework for the recognition of online handwritten graphics. International Journal on Document Analysis and Recognition (IJDAR). 2020; 23 (2):143-160.

Chicago/Turabian Style

Frank Julca-Aguilar; Harold Mouchère; Christian Viard-Gaudin; Nina Hirata. 2020. "A general framework for the recognition of online handwritten graphics." International Journal on Document Analysis and Recognition (IJDAR) 23, no. 2: 143-160.

Conference paper
Published: 01 January 2020 in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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ACS Style

Mateus Espadoto; Nina Hirata; Alexandre Falcão; Alexandru Telea. Improving Neural Network-based Multidimensional Projections. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020, 1 .

AMA Style

Mateus Espadoto, Nina Hirata, Alexandre Falcão, Alexandru Telea. Improving Neural Network-based Multidimensional Projections. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 2020; ():1.

Chicago/Turabian Style

Mateus Espadoto; Nina Hirata; Alexandre Falcão; Alexandru Telea. 2020. "Improving Neural Network-based Multidimensional Projections." Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , no. : 1.

Conference paper
Published: 01 January 2020 in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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ACS Style

Ana Martinazzo; Mateus Espadoto; Nina Hirata. Deep Learning for Astronomical Object Classification: A Case Study. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020, 1 .

AMA Style

Ana Martinazzo, Mateus Espadoto, Nina Hirata. Deep Learning for Astronomical Object Classification: A Case Study. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 2020; ():1.

Chicago/Turabian Style

Ana Martinazzo; Mateus Espadoto; Nina Hirata. 2020. "Deep Learning for Astronomical Object Classification: A Case Study." Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , no. : 1.

Journal article
Published: 27 September 2019 in IEEE Transactions on Visualization and Computer Graphics
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Dimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.

ACS Style

Mateus Espadoto; Rafael M. Martins; Andreas Kerren; Nina S. T. Hirata; Alexandru Cristian Telea. Toward a Quantitative Survey of Dimension Reduction Techniques. IEEE Transactions on Visualization and Computer Graphics 2019, 27, 2153 -2173.

AMA Style

Mateus Espadoto, Rafael M. Martins, Andreas Kerren, Nina S. T. Hirata, Alexandru Cristian Telea. Toward a Quantitative Survey of Dimension Reduction Techniques. IEEE Transactions on Visualization and Computer Graphics. 2019; 27 (3):2153-2173.

Chicago/Turabian Style

Mateus Espadoto; Rafael M. Martins; Andreas Kerren; Nina S. T. Hirata; Alexandru Cristian Telea. 2019. "Toward a Quantitative Survey of Dimension Reduction Techniques." IEEE Transactions on Visualization and Computer Graphics 27, no. 3: 2153-2173.

Conference paper
Published: 01 September 2019 in 2019 IEEE International Conference on Image Processing (ICIP)
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In this work, we employ deep learning models for detecting QR Codes in natural scenes. A series of different model configurations are evaluated in terms of Average Precision, and an architecture modification that allows detection aided by object subparts annotations is proposed. This modification is implemented in our best scoring model, which is compared to a traditional technique, achieving a substantial improvement in the considered metrics. The dataset used in our evaluation, with bounding box annotations for both QR Codes and their Finder Patterns (FIPs), will be made publicly available. This dataset is significantly bigger than known available options at the moment, so we expect it to provide a common benchmark tool for QR Code detection in natural scenes.

ACS Style

Leonardo Blanger; Nina Hirata. An Evaluation of Deep Learning Techniques for Qr Code Detection. 2019 IEEE International Conference on Image Processing (ICIP) 2019, 1625 -1629.

AMA Style

Leonardo Blanger, Nina Hirata. An Evaluation of Deep Learning Techniques for Qr Code Detection. 2019 IEEE International Conference on Image Processing (ICIP). 2019; ():1625-1629.

Chicago/Turabian Style

Leonardo Blanger; Nina Hirata. 2019. "An Evaluation of Deep Learning Techniques for Qr Code Detection." 2019 IEEE International Conference on Image Processing (ICIP) , no. : 1625-1629.

Journal article
Published: 05 August 2019 in Monthly Notices of the Royal Astronomical Society
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The Southern Photometric Local Universe Survey (S-PLUS) is imaging ∼9300 deg2 of the celestial sphere in 12 optical bands using a dedicated 0.8 m robotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 $\rm {\mu m}$ pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel−1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30°, 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, H δ, G band, Mg b triplet, H α, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than $1\, (\mathrm{Gpc}/h)^3$. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ∼336 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.

ACS Style

C. Mendes de Oliveira; T Ribeiro; W Schoenell; A Kanaan; R A Overzier; A Molino; L Sampedro; Paula Coelho; Carlos Eduardo Barbosa; A Cortesi; M V Costa-Duarte; F R Herpich; J. A. Hernandez-Jimenez; Vinicius Placco; H S Xavier; L R Abramo; R K Saito; A L Chies-Santos; A Ederoclite; R Lopes De Oliveira; D R Gonçalves; S Akras; L A Almeida; F Almeida-Fernandes; T C Beers; C Bonatto; S Bonoli; Eduardo Cypriano; E Vinicius-Lima; Rafael S. de Souza; G Fabiano De Souza; F Ferrari; T S Gonçalves; A H Gonzalez; L A Gutiérrez-Soto; E A Hartmann; Y Jaffe; L O Kerber; C Lima-Dias; P A A Lopes; K Menendez-Delmestre; L M I Nakazono; P M Novais; R A Ortega-Minakata; E S Pereira; Helio Perottoni; C Queiroz; Ribamar Reis; W A Santos; T Santos-Silva; R M Santucci; Beatriz B Siffert; L Sodré; S Torres-Flores; P Westera; D D Whitten; J S Alcaniz; Javier Alonso-García; Silvia Alencar; A Alvarez-Candal; P Amram; L Azanha; R H Barbá; Pedro Bernardinelli; M Borges Fernandes; Vinicius Branco; D Brito-Silva; M L Buzzo; J Caffer; A Campillay; Z Cano; J M Carvano; M Castejon; Roberto Cid Fernandes; Maria Luiza Linhares Dantas; S Daflon; G Damke; R De La Reza; L J De Melo De Azevedo; D F De Paula; K G Diem; R Donnerstein; O L Dors; R Dupke; S Eikenberry; Carlos G Escudero; Favio R Faifer; H Farías; B Fernandes; S Fontes; A Galarza; Nina Hirata; L Katena; Jane Gregorio-Hetem; J D Hernández-Fernández; Luca Izzo; M Jaque Arancibia; V Jatenco-Pereira; Yolanda Jimenez-Teja; D A Kann; Angela Cristina Krabbe; C Labayru; D Lazzaro; G B Lima Neto; Amanda R Lopes; R Magalhães; M Makler; R De Menezes; Jordi Miralda-Escudé; Rogério Monteiro-Oliveira; Antonio D. Montero-Dorta; N Muñoz-Elgueta; R S Nemmen; José Luis Nilo Castellón; Alexandre Oliveira; D Ortíz; E Pattaro; C B Pereira; B Quint; L Riguccini; H J Rocha Pinto; I Rodrigues; F Roig; Silvia Rossi; Kanak Saha; R Santos; A Schnorr Müller; Leandro A Sesto; R Silva; Analia V Smith Castelli; R Teixeira; Jose Eduardo Telles; R C Thom De Souza; C Thöne; M Trevisan; Antonio De Ugarte Postigo; F Urrutia-Viscarra; C H Veiga; Marina Vika; André Zamorano Vitorelli; Ariel Werle; S V Werner; D Zaritsky. The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters. Monthly Notices of the Royal Astronomical Society 2019, 489, 241 -267.

AMA Style

C. Mendes de Oliveira, T Ribeiro, W Schoenell, A Kanaan, R A Overzier, A Molino, L Sampedro, Paula Coelho, Carlos Eduardo Barbosa, A Cortesi, M V Costa-Duarte, F R Herpich, J. A. Hernandez-Jimenez, Vinicius Placco, H S Xavier, L R Abramo, R K Saito, A L Chies-Santos, A Ederoclite, R Lopes De Oliveira, D R Gonçalves, S Akras, L A Almeida, F Almeida-Fernandes, T C Beers, C Bonatto, S Bonoli, Eduardo Cypriano, E Vinicius-Lima, Rafael S. de Souza, G Fabiano De Souza, F Ferrari, T S Gonçalves, A H Gonzalez, L A Gutiérrez-Soto, E A Hartmann, Y Jaffe, L O Kerber, C Lima-Dias, P A A Lopes, K Menendez-Delmestre, L M I Nakazono, P M Novais, R A Ortega-Minakata, E S Pereira, Helio Perottoni, C Queiroz, Ribamar Reis, W A Santos, T Santos-Silva, R M Santucci, Beatriz B Siffert, L Sodré, S Torres-Flores, P Westera, D D Whitten, J S Alcaniz, Javier Alonso-García, Silvia Alencar, A Alvarez-Candal, P Amram, L Azanha, R H Barbá, Pedro Bernardinelli, M Borges Fernandes, Vinicius Branco, D Brito-Silva, M L Buzzo, J Caffer, A Campillay, Z Cano, J M Carvano, M Castejon, Roberto Cid Fernandes, Maria Luiza Linhares Dantas, S Daflon, G Damke, R De La Reza, L J De Melo De Azevedo, D F De Paula, K G Diem, R Donnerstein, O L Dors, R Dupke, S Eikenberry, Carlos G Escudero, Favio R Faifer, H Farías, B Fernandes, S Fontes, A Galarza, Nina Hirata, L Katena, Jane Gregorio-Hetem, J D Hernández-Fernández, Luca Izzo, M Jaque Arancibia, V Jatenco-Pereira, Yolanda Jimenez-Teja, D A Kann, Angela Cristina Krabbe, C Labayru, D Lazzaro, G B Lima Neto, Amanda R Lopes, R Magalhães, M Makler, R De Menezes, Jordi Miralda-Escudé, Rogério Monteiro-Oliveira, Antonio D. Montero-Dorta, N Muñoz-Elgueta, R S Nemmen, José Luis Nilo Castellón, Alexandre Oliveira, D Ortíz, E Pattaro, C B Pereira, B Quint, L Riguccini, H J Rocha Pinto, I Rodrigues, F Roig, Silvia Rossi, Kanak Saha, R Santos, A Schnorr Müller, Leandro A Sesto, R Silva, Analia V Smith Castelli, R Teixeira, Jose Eduardo Telles, R C Thom De Souza, C Thöne, M Trevisan, Antonio De Ugarte Postigo, F Urrutia-Viscarra, C H Veiga, Marina Vika, André Zamorano Vitorelli, Ariel Werle, S V Werner, D Zaritsky. The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters. Monthly Notices of the Royal Astronomical Society. 2019; 489 (1):241-267.

Chicago/Turabian Style

C. Mendes de Oliveira; T Ribeiro; W Schoenell; A Kanaan; R A Overzier; A Molino; L Sampedro; Paula Coelho; Carlos Eduardo Barbosa; A Cortesi; M V Costa-Duarte; F R Herpich; J. A. Hernandez-Jimenez; Vinicius Placco; H S Xavier; L R Abramo; R K Saito; A L Chies-Santos; A Ederoclite; R Lopes De Oliveira; D R Gonçalves; S Akras; L A Almeida; F Almeida-Fernandes; T C Beers; C Bonatto; S Bonoli; Eduardo Cypriano; E Vinicius-Lima; Rafael S. de Souza; G Fabiano De Souza; F Ferrari; T S Gonçalves; A H Gonzalez; L A Gutiérrez-Soto; E A Hartmann; Y Jaffe; L O Kerber; C Lima-Dias; P A A Lopes; K Menendez-Delmestre; L M I Nakazono; P M Novais; R A Ortega-Minakata; E S Pereira; Helio Perottoni; C Queiroz; Ribamar Reis; W A Santos; T Santos-Silva; R M Santucci; Beatriz B Siffert; L Sodré; S Torres-Flores; P Westera; D D Whitten; J S Alcaniz; Javier Alonso-García; Silvia Alencar; A Alvarez-Candal; P Amram; L Azanha; R H Barbá; Pedro Bernardinelli; M Borges Fernandes; Vinicius Branco; D Brito-Silva; M L Buzzo; J Caffer; A Campillay; Z Cano; J M Carvano; M Castejon; Roberto Cid Fernandes; Maria Luiza Linhares Dantas; S Daflon; G Damke; R De La Reza; L J De Melo De Azevedo; D F De Paula; K G Diem; R Donnerstein; O L Dors; R Dupke; S Eikenberry; Carlos G Escudero; Favio R Faifer; H Farías; B Fernandes; S Fontes; A Galarza; Nina Hirata; L Katena; Jane Gregorio-Hetem; J D Hernández-Fernández; Luca Izzo; M Jaque Arancibia; V Jatenco-Pereira; Yolanda Jimenez-Teja; D A Kann; Angela Cristina Krabbe; C Labayru; D Lazzaro; G B Lima Neto; Amanda R Lopes; R Magalhães; M Makler; R De Menezes; Jordi Miralda-Escudé; Rogério Monteiro-Oliveira; Antonio D. Montero-Dorta; N Muñoz-Elgueta; R S Nemmen; José Luis Nilo Castellón; Alexandre Oliveira; D Ortíz; E Pattaro; C B Pereira; B Quint; L Riguccini; H J Rocha Pinto; I Rodrigues; F Roig; Silvia Rossi; Kanak Saha; R Santos; A Schnorr Müller; Leandro A Sesto; R Silva; Analia V Smith Castelli; R Teixeira; Jose Eduardo Telles; R C Thom De Souza; C Thöne; M Trevisan; Antonio De Ugarte Postigo; F Urrutia-Viscarra; C H Veiga; Marina Vika; André Zamorano Vitorelli; Ariel Werle; S V Werner; D Zaritsky. 2019. "The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters." Monthly Notices of the Royal Astronomical Society 489, no. 1: 241-267.

Conference paper
Published: 01 October 2018 in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
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We propose a new approach for segmenting a document image into its page components (e.g. text, graphics and tables). Our approach consists of two main steps. In the first step, a set of scores corresponding to the output of a convolutional neural network, one for each of the possible page component categories, is assigned to each connected component in the document. The labeled connected components define a fuzzy over-segmentation of the page. In the second step, spatially close connected components that are likely to belong to a same page component are grouped together. This is done by building an attributed region adjacency graph of the connected components and modeling the problem as an edge removal problem. Edges are then kept or removed based on a pre-trained classifier. The resulting groups, defined by the connected subgraphs, correspond to the detected page components. We evaluate our method on the ICDAR2009 dataset. Results show that our method effectively segments pages, being able to detect the nine types of page components. Furthermore, as our approach is based on simple machine learning models and graph-based techniques, it should be easily adapted to the segmentation of a variety of document types.

ACS Style

Ana Lucia Lima Marreiros Maia; Frank Dennis Julca-Aguilar; Nina Sumiko Tomita Hirata. A Machine Learning Approach for Graph-Based Page Segmentation. 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2018, 424 -431.

AMA Style

Ana Lucia Lima Marreiros Maia, Frank Dennis Julca-Aguilar, Nina Sumiko Tomita Hirata. A Machine Learning Approach for Graph-Based Page Segmentation. 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 2018; ():424-431.

Chicago/Turabian Style

Ana Lucia Lima Marreiros Maia; Frank Dennis Julca-Aguilar; Nina Sumiko Tomita Hirata. 2018. "A Machine Learning Approach for Graph-Based Page Segmentation." 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) , no. : 424-431.

Conference paper
Published: 01 April 2018 in 2018 13th IAPR International Workshop on Document Analysis Systems (DAS)
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Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.

ACS Style

Frank D. Julca-Aguilar; Nina Hirata. Symbol Detection in Online Handwritten Graphics Using Faster R-CNN. 2018 13th IAPR International Workshop on Document Analysis Systems (DAS) 2018, 151 -156.

AMA Style

Frank D. Julca-Aguilar, Nina Hirata. Symbol Detection in Online Handwritten Graphics Using Faster R-CNN. 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). 2018; ():151-156.

Chicago/Turabian Style

Frank D. Julca-Aguilar; Nina Hirata. 2018. "Symbol Detection in Online Handwritten Graphics Using Faster R-CNN." 2018 13th IAPR International Workshop on Document Analysis Systems (DAS) , no. : 151-156.

Conference paper
Published: 01 January 2018 in Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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ACS Style

Francisco Caio Maia Rodrigues; Nina S. T. Hirata; Antonio A. Abello; Leandro T. De La Cruz; Rubens M. Lopes; R. Hirata Jr.. Evaluation of Transfer Learning Scenarios in Plankton Image Classification. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2018, 359 -366.

AMA Style

Francisco Caio Maia Rodrigues, Nina S. T. Hirata, Antonio A. Abello, Leandro T. De La Cruz, Rubens M. Lopes, R. Hirata Jr.. Evaluation of Transfer Learning Scenarios in Plankton Image Classification. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 2018; ():359-366.

Chicago/Turabian Style

Francisco Caio Maia Rodrigues; Nina S. T. Hirata; Antonio A. Abello; Leandro T. De La Cruz; Rubens M. Lopes; R. Hirata Jr.. 2018. "Evaluation of Transfer Learning Scenarios in Plankton Image Classification." Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , no. : 359-366.

Preprint
Published: 13 December 2017
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Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.

ACS Style

Frank D. Julca-Aguilar; Nina S. T. Hirata. Symbol detection in online handwritten graphics using Faster R-CNN. 2017, 1 .

AMA Style

Frank D. Julca-Aguilar, Nina S. T. Hirata. Symbol detection in online handwritten graphics using Faster R-CNN. . 2017; ():1.

Chicago/Turabian Style

Frank D. Julca-Aguilar; Nina S. T. Hirata. 2017. "Symbol detection in online handwritten graphics using Faster R-CNN." , no. : 1.

Conference paper
Published: 01 November 2017 in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
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Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.

ACS Style

Frank Dennis Julca Aguilar; Nina S.T. Hirata. Image Operator Learning Coupled with CNN Classification and Its Application to Staff Line Removal. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 2017, 1, 53 -58.

AMA Style

Frank Dennis Julca Aguilar, Nina S.T. Hirata. Image Operator Learning Coupled with CNN Classification and Its Application to Staff Line Removal. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). 2017; 1 ():53-58.

Chicago/Turabian Style

Frank Dennis Julca Aguilar; Nina S.T. Hirata. 2017. "Image Operator Learning Coupled with CNN Classification and Its Application to Staff Line Removal." 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 1, no. : 53-58.

Conference paper
Published: 01 October 2017 in 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
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Text segmentation is an important problem in document analysis related applications. We address the problem of classifying connected components of a document image as text or non-text. Inspired from previous works in the literature, besides common size and shape related features extracted from the components, we also consider component images, without and with context information, as inputs of the classifiers. Muli-layer perceptrons and convolutional neural networks are used to classify the components. High precision and recall is obtained with respect to both text and non-text components.

ACS Style

Frank D. Julca-Aguilar; Ana L.L.M. Maia; Nina S.T. Hirata. Text/Non-Text Classification of Connected Components in Document Images. 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2017, 450 -455.

AMA Style

Frank D. Julca-Aguilar, Ana L.L.M. Maia, Nina S.T. Hirata. Text/Non-Text Classification of Connected Components in Document Images. 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 2017; ():450-455.

Chicago/Turabian Style

Frank D. Julca-Aguilar; Ana L.L.M. Maia; Nina S.T. Hirata. 2017. "Text/Non-Text Classification of Connected Components in Document Images." 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) , no. : 450-455.

Preprint
Published: 19 September 2017
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Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.

ACS Style

Frank D. Julca-Aguilar; Nina S. T. Hirata. Image operator learning coupled with CNN classification and its application to staff line removal. 2017, 1 .

AMA Style

Frank D. Julca-Aguilar, Nina S. T. Hirata. Image operator learning coupled with CNN classification and its application to staff line removal. . 2017; ():1.

Chicago/Turabian Style

Frank D. Julca-Aguilar; Nina S. T. Hirata. 2017. "Image operator learning coupled with CNN classification and its application to staff line removal." , no. : 1.

Preprint
Published: 19 September 2017
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We propose a new framework for the recognition of online handwritten graphics. Three main features of the framework are its ability to treat symbol and structural level information in an integrated way, its flexibility with respect to different families of graphics, and means to control the tradeoff between recognition effectiveness and computational cost. We model a graphic as a labeled graph generated from a graph grammar. Non-terminal vertices represent subcomponents, terminal vertices represent symbols, and edges represent relations between subcomponents or symbols. We then model the recognition problem as a graph parsing problem: given an input stroke set, we search for a parse tree that represents the best interpretation of the input. Our graph parsing algorithm generates multiple interpretations (consistent with the grammar) and then we extract an optimal interpretation according to a cost function that takes into consideration the likelihood scores of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to structures defined in the grammar and it does not impose constraints present in some previous works (e.g. stroke ordering). By avoiding such constraints and thanks to the powerful representativeness of graphs, our approach can be adapted to the recognition of different graphic notations. We show applications to the recognition of mathematical expressions and flowcharts. Experimentation shows that our method obtains state-of-the-art accuracy in both applications.

ACS Style

Frank Julca-Aguilar; Harold Mouchère; Christian Viard-Gaudin; Nina S. T. Hirata. A General Framework for the Recognition of Online Handwritten Graphics. 2017, 1 .

AMA Style

Frank Julca-Aguilar, Harold Mouchère, Christian Viard-Gaudin, Nina S. T. Hirata. A General Framework for the Recognition of Online Handwritten Graphics. . 2017; ():1.

Chicago/Turabian Style

Frank Julca-Aguilar; Harold Mouchère; Christian Viard-Gaudin; Nina S. T. Hirata. 2017. "A General Framework for the Recognition of Online Handwritten Graphics." , no. : 1.

Journal article
Published: 01 March 2017 in Pattern Recognition
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Igor S. Montagner; Nina S.T. Hirata; Roberto Hirata. Staff removal using image operator learning. Pattern Recognition 2017, 63, 310 -320.

AMA Style

Igor S. Montagner, Nina S.T. Hirata, Roberto Hirata. Staff removal using image operator learning. Pattern Recognition. 2017; 63 ():310-320.

Chicago/Turabian Style

Igor S. Montagner; Nina S.T. Hirata; Roberto Hirata. 2017. "Staff removal using image operator learning." Pattern Recognition 63, no. : 310-320.

Conference paper
Published: 04 December 2016 in Proceedings of the 1st International Workshop on Real World Domain Specific Languages
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We employ an image operator learning method to segment text in comic images. Since the method is based on learning from pairs of input and corresponding expected output images, it is flexible with respect to alphabet sets and text orientation. The method is applied on both Japanese and European comics. Results indicate that most text regions can be straightforwardly identified from the output images.

ACS Style

Nina Hirata; Igor Dos Santos Montagner; Roberto Hirata Jr.. Comics image processing. Proceedings of the 1st International Workshop on Real World Domain Specific Languages 2016, 11 -11:6.

AMA Style

Nina Hirata, Igor Dos Santos Montagner, Roberto Hirata Jr.. Comics image processing. Proceedings of the 1st International Workshop on Real World Domain Specific Languages. 2016; ():11-11:6.

Chicago/Turabian Style

Nina Hirata; Igor Dos Santos Montagner; Roberto Hirata Jr.. 2016. "Comics image processing." Proceedings of the 1st International Workshop on Real World Domain Specific Languages , no. : 11-11:6.

Conference paper
Published: 01 December 2016 in 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI)
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Due to image quality related issues, classification of plankton images, particularly of those collected in situ, strongly relies on shape features. Thus, image segmentation is a critical step in the classification pipeline. In general, the segmentation algorithm that leads to the best overall classification accuracy does not necessarily imply best classification accuracy with respect to each of the individual classes. In addition, in real time applications, changes in the environment or in the image acquisition devices require fast adjustments in the classification pipeline. Customizing segmentation algorithms for each situation may demand considerable effort. Motivated by these issues, we address the problem of using multiple segmentation algorithms and letting the classifier decide how to make best use of them. Some case studies and results are presented and discussed.

ACS Style

Nina Hirata; Mariela A. Fernández; Rubens Lopes. Plankton Image Classification Based on Multiple Segmentations. 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI) 2016, 55 -60.

AMA Style

Nina Hirata, Mariela A. Fernández, Rubens Lopes. Plankton Image Classification Based on Multiple Segmentations. 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI). 2016; ():55-60.

Chicago/Turabian Style

Nina Hirata; Mariela A. Fernández; Rubens Lopes. 2016. "Plankton Image Classification Based on Multiple Segmentations." 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI) , no. : 55-60.

Conference paper
Published: 01 December 2016 in 2016 23rd International Conference on Pattern Recognition (ICPR)
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Recognition of spatial relations between pairs of subexpressions is a key problem of recognition of handwritten mathematical expressions. Most methods for spatial relation classification are based on handcrafted rules and geometric indices extracted from the subexpression bounding boxes. In this work, we propose new spatial relation features that combine subexpression bounding box and intra-subexpression information, along with prior knowledge about the general position and size of symbols. Instead of handcrafting features, we train artificial neural networks to learn the useful features from two kinds of histograms. The first type captures the relative positions and sizes of the subexpression bounding boxes. The second captures the relative positions and shape of a pair of symbols, called dominant symbols, extracted from the main baselines of the evaluated subexpressions. We evaluate and compare our features with two state-of-the-art features on a benchmark dataset. Experimental results show that our features obtain better accuracy than these two features.

ACS Style

Frank Julca-Aguilar; Nina Hirata; Harold Mouchere; Christian Viard-Gaudin. Subexpression and dominant symbol histograms for spatial relation classification in mathematical expressions. 2016 23rd International Conference on Pattern Recognition (ICPR) 2016, 3446 -3451.

AMA Style

Frank Julca-Aguilar, Nina Hirata, Harold Mouchere, Christian Viard-Gaudin. Subexpression and dominant symbol histograms for spatial relation classification in mathematical expressions. 2016 23rd International Conference on Pattern Recognition (ICPR). 2016; ():3446-3451.

Chicago/Turabian Style

Frank Julca-Aguilar; Nina Hirata; Harold Mouchere; Christian Viard-Gaudin. 2016. "Subexpression and dominant symbol histograms for spatial relation classification in mathematical expressions." 2016 23rd International Conference on Pattern Recognition (ICPR) , no. : 3446-3451.