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Ayyalasomayajula, Kalyan Ram
Publications (9 of 9) Show all publications
Ayyalasomayajula, K. R. (2019). Learning based segmentation and generation methods for handwritten document images. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Learning based segmentation and generation methods for handwritten document images
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Computerized analysis of handwritten documents is an active research area in image analysis and computer vision. The goal is to create tools that can be available for use at university libraries and for researchers in the humanities. Working with large collections of handwritten documents is very time consuming and many old books and letters remain unread for centuries. Efficient computerized methods could help researchers in history, philology and computer linguistics to cost-effectively conduct a whole new type of research based on large collections of documents. The thesis makes a contribution to this area through the development of methods based on machine learning. The passage of time degrades historical documents. Humidity, stains, heat, mold and natural aging of the materials for hundreds of years make the documents increasingly difficult to interpret. The first half of the dissertation is therefore focused on cleaning the visual information in these documents by image segmentation methods based on energy minimization and machine learning. However, machine learning algorithms learn by imitating what is expected of them. One prerequisite for these methods to work is that ground truth is available. This causes a problem for historical documents because there is a shortage of experts who can help to interpret and interpret them. The second part of the thesis is therefore about automatically creating synthetic documents that are similar to handwritten historical documents. Because they are generated from a known text, they have a given facet. The visual content of the generated historical documents includes variation in the writing style and also imitates degradation factors to make the images realistic. When machine learning is trained on synthetic images of handwritten text, with a known facet, in many cases they can even give an even better result for real historical documents.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 97
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1783
Keywords
Machine learning, handwriting, handwritten document anlysis, deep learning, image processing
National Category
Computer Systems
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-379636 (URN)978-91-513-0599-8 (ISBN)
Public defence
2019-05-08, TLS, Carolina Rediviva Library, Dag Hammarskjölds Väg 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2019-04-15 Created: 2019-03-19 Last updated: 2019-06-17Bibliographically approved
Ayyalasomayajula, K. R., Malmberg, F. & Brun, A. (2019). PDNet: Semantic segmentation integrated with a primal-dual network for document binarization. Pattern Recognition Letters, 121, 52-60
Open this publication in new window or tab >>PDNet: Semantic segmentation integrated with a primal-dual network for document binarization
2019 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 121, p. 52-60Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366933 (URN)10.1016/j.patrec.2018.05.011 (DOI)000459876700008 ()
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2018-05-16 Created: 2018-11-27 Last updated: 2019-04-04Bibliographically approved
Dhara, A. K., Ayyalasomayajula, K. R., Arvids, E., Fahlström, M., Wikström, J., Larsson, E.-M. & Strand, R. (2018). Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Refinement. In: Proceedings, Brain Lesion (BrainLes) workshop: . Paper presented at 21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION, September 16-20, 2018, Granada, Spain.
Open this publication in new window or tab >>Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Refinement
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2018 (English)In: Proceedings, Brain Lesion (BrainLes) workshop, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Accurate volumetric change estimation of glioblastoma is very important for post-surgical treatment follow-up. In this paper, an interactive segmentation method was developed and evaluated with the aim to guide volumetric estimation of glioblastoma. U-Net based fully convolutional network is used for initial segmentation of glioblastoma from post contrast MR images. The max flow algorithm is applied on the probability map of U-Net to update the initial segmentation and the result is displayed to the user for interactive refinement. Network update is performed based on the corrected contour by considering patient specific learning to deal with large context variations among dierent images. The proposed method is evaluated on a clinical MR image databas eof 15 glioblastoma patients with longitudinal scan data. The experimental results depict an improvement of segmentation performance due to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-upwith minimal user intervention.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-366550 (URN)
Conference
21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION, September 16-20, 2018, Granada, Spain
Funder
Swedish Research Council, 2014-6199Vinnova, 2017-02447
Note

Extended versions of all accepted papers will be published as LCNS proceedings by Springer-Verlag. http://www.brainlesion-workshop.org/

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-03-14Bibliographically approved
Ayyalasomayajula, K. R. & Brun, A. (2017). Document Binarization Combining with Graph Cuts and Deep Neural Networks. In: : . Paper presented at 36th Swedish Symposium on Image Analysis (SSBA) 2017.
Open this publication in new window or tab >>Document Binarization Combining with Graph Cuts and Deep Neural Networks
2017 (English)Conference paper, Published paper (Other academic)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-336171 (URN)
Conference
36th Swedish Symposium on Image Analysis (SSBA) 2017
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2017-12-12 Created: 2017-12-12 Last updated: 2018-01-13Bibliographically approved
Ayyalasomayajula, K. R. & Brun, A. (2017). Historical document binarization combining semantic labeling and graph cuts. In: Image Analysis: Part I. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 386-396). Springer
Open this publication in new window or tab >>Historical document binarization combining semantic labeling and graph cuts
2017 (English)In: Image Analysis: Part I, Springer, 2017, p. 386-396Conference paper, Published paper (Refereed)
Abstract [en]

Most data mining applications on collections of historical documents require binarization of the digitized images as a pre-processing step. Historical documents are often subjected to degradations such as parchment aging, smudges and bleed through from the other side. The text is sometimes printed, but more often handwritten. Mathematical modeling of appearance of the text, background and all kinds of degradations, is challenging. In the current work we try to tackle binarization as pixel classification problem. We first apply semantic segmentation, using fully convolutional neural networks. In order to improve the sharpness of the result, we then apply a graph cut algorithm. The labels from the semantic segmentation are used as approximate estimates of the text and background, with the probability map of background used for pruning the edges in the graph cut. The results obtained show significant improvement over the state of the art approach.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10269
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-335335 (URN)10.1007/978-3-319-59126-1_32 (DOI)000454359300032 ()978-3-319-59125-4 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2017-05-19 Created: 2017-12-04 Last updated: 2019-03-19Bibliographically approved
Ayyalasomayajula, K. R. & Brun, A. (2017). Semantic Labeling using Convolutional Networks coupled with Graph-Cuts for Document binarization. In: : . Paper presented at First Swedish Symposium on Deep Learning (SSDL 2017).
Open this publication in new window or tab >>Semantic Labeling using Convolutional Networks coupled with Graph-Cuts for Document binarization
2017 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-336174 (URN)
Conference
First Swedish Symposium on Deep Learning (SSDL 2017)
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2017-12-12 Created: 2017-12-12 Last updated: 2018-01-13Bibliographically approved
Ayyalasomayajula, K. R., Nettelblad, C. & Brun, A. (2016). Feature evaluation for handwritten character recognition with regressive and generative Hidden Markov Models. In: Advances in Visual Computing: Part I. Paper presented at ISVC 2016, December 12–14, Las Vegas, NV (pp. 278-287). Springer
Open this publication in new window or tab >>Feature evaluation for handwritten character recognition with regressive and generative Hidden Markov Models
2016 (English)In: Advances in Visual Computing: Part I, Springer, 2016, p. 278-287Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science ; 10072
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-308662 (URN)10.1007/978-3-319-50835-1_26 (DOI)978-3-319-50834-4 (ISBN)
Conference
ISVC 2016, December 12–14, Las Vegas, NV
Projects
q2b – From Quill to Bytes
Available from: 2016-12-10 Created: 2016-11-29 Last updated: 2019-03-19Bibliographically approved
Ayyalasomayajula, K. R. & Brun, A. (2015). Topological clustering guided document binarization.
Open this publication in new window or tab >>Topological clustering guided document binarization
2015 (English)Report (Other academic)
Abstract [en]

The current approach for text binarization proposes a clustering algorithm as a preprocessing stage to an energy-based segmentation method. It uses a clustering algorithm to obtain a coarse estimate of the background (BG) and foreground (FG) pixels. These estimates are usedas a prior for the source and sink points of a graph cut implementation, which is used to efficiently find the minimum energy solution of an objective function to separate the BG and FG. The binary image thus obtained is used to refine the edge map that guides the graph cut algorithm. A final binary image is obtained by once again performing the graph cut guided by the refined edges on Laplacian of the image.

Publisher
p. 6
Series
Svenska Sällskapet för Automatiserad Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-268019 (URN)
Funder
Swedish Research Council, 2012-5743
Available from: 2015-12-01 Created: 2015-12-01 Last updated: 2018-01-10Bibliographically approved
Ayyalasomayajula, K. R. & Brun, A. (2014). Document binarization using topological clustering guided Laplacian Energy Segmentation. In: Proceedings International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014: . Paper presented at International Conference on Frontiers in Handwriting Recognition (ICFHR),September 1-4, 2014, Crete, Greece. (pp. 523-528).
Open this publication in new window or tab >>Document binarization using topological clustering guided Laplacian Energy Segmentation
2014 (English)In: Proceedings International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, 2014, p. 523-528Conference paper, Published paper (Refereed)
Abstract [en]

The current approach for text binarization proposesa clustering algorithm as a preprocessing stage toan energy-based segmentation method. It uses a clusteringalgorithm to obtain a coarse estimate of the background (BG)and foreground (FG) pixels. These estimates are used as a priorfor the source and sink points of a graph cut implementation,which is used to efficiently find the minimum energy solution ofan objective function to separate the BG and FG. The binaryimage thus obtained is used to refine the edge map that guidesthe graph cut algorithm. A final binary image is obtained byonce again performing the graph cut guided by the refinededges on a Laplacian of the image.

Series
Frontiers in Handwriting Recognition, ISSN 2167-6445 ; 14
Keywords
Image Processing; Classification; Machine Learning; Graph-theoretic methods.
National Category
Computer Systems Signal Processing
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-238316 (URN)10.1109/ICFHR.2014.94 (DOI)978-1-4799-4335-7 (ISBN)
Conference
International Conference on Frontiers in Handwriting Recognition (ICFHR),September 1-4, 2014, Crete, Greece.
Funder
Swedish Research Council, 2012-5743
Available from: 2014-12-11 Created: 2014-12-11 Last updated: 2019-03-19Bibliographically approved
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