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Publications (10 of 64) Show all publications
Hast, A. (2023). Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers. In: Jaques Blanc-Talon; Patrice Delmas; Wilfried Philips; Paul Scheunders (Ed.), Advanced Concepts for Intelligent Vision Systems: . Paper presented at 21st International Conference, ACIVS 2023, Kumamoto, Japan, August 21–23, 2023 (pp. 88-99). Springer Nature
Open this publication in new window or tab >>Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers
2023 (English)In: Advanced Concepts for Intelligent Vision Systems / [ed] Jaques Blanc-Talon; Patrice Delmas; Wilfried Philips; Paul Scheunders, Springer Nature, 2023, p. 88-99Conference paper, Published paper (Refereed)
Abstract [en]

One of the major difficulties in face recognition while comparing photographs of individuals of different ages is the influence of age progression on their facial features. As a person ages, the face undergoes many changes, such as geometrical changes, changes in facial hair, and the presence of glasses, among others. Although biometric markers like computed face feature vectors should ideally remain unchanged by such factors, face recognition becomes less reliable as the age range increases. Therefore, this investigation was carried out to examine how the use of Embedded Prototype Subspace Classifiers could improve face recognition accuracy when dealing with age-related variations using face feature vectors only.

Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 14124
Keywords
Face Recognition, Embedded Prototype Subspace Classifier, Biometric Markers
National Category
Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-516663 (URN)10.1007/978-3-031-45382-3_8 (DOI)001166637000008 ()978-3-031-45382-3 (ISBN)978-3-031-45381-6 (ISBN)
Conference
21st International Conference, ACIVS 2023, Kumamoto, Japan, August 21–23, 2023
Projects
City Faces
Funder
Swedish Research Council, 2020-04652Swedish Research Council, 2022-02056Swedish National Infrastructure for Computing (SNIC), 2021/22-918UPPMAX
Available from: 2023-11-27 Created: 2023-11-27 Last updated: 2024-03-15Bibliographically approved
Hast, A. (2023). Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall. In: Image Analysis and Processing – ICIAP 2023: . Paper presented at 22nd International Conference on Image Analysis and Processing, SEP 11-15, 2023, Udine, Italy (pp. 159-170). Springer Nature, 14233
Open this publication in new window or tab >>Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall
2023 (English)In: Image Analysis and Processing – ICIAP 2023, Springer Nature, 2023, Vol. 14233, p. 159-170Conference paper, Published paper (Refereed)
Abstract [en]

Efficient face image retrieval, i.e. searching for existing  photographs of a person in unlabelled photo collections using a query photo, is  evaluated for a novel method to find the top $n$ results for Consensus Ranking. The approach aims to maximise precision and recall by using the retrieved photos, all ranked on similarity. The proposed method aims to retrieve all photos of the queried person while excluding images of other individuals. To achieve this, the method uses the top n results as temporary queries, recalculates similarities, and combines the obtained ranked lists to produce a better overall ranking. The method includes a novel and reliable procedure for selecting $n$, which is evaluated on two datasets, and considers the impact of age variation in the datasets.

Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14233
National Category
Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-516665 (URN)10.1007/978-3-031-43148-7_14 (DOI)001156196000014 ()978-3-031-43148-7 (ISBN)978-3-031-43147-0 (ISBN)
Conference
22nd International Conference on Image Analysis and Processing, SEP 11-15, 2023, Udine, Italy
Funder
Swedish Research Council, 2020-04652Swedish Research Council, 2022-02056
Available from: 2023-11-27 Created: 2023-11-27 Last updated: 2024-03-08Bibliographically approved
Hast, A. (2022). Magnitude of Semicircle Tiles in Fourier-space: A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers. Journal of WSCG, 30(1-2), 82-90
Open this publication in new window or tab >>Magnitude of Semicircle Tiles in Fourier-space: A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers
2022 (English)In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 30, no 1-2, p. 82-90Article in journal (Refereed) Published
Abstract [en]

The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edges in an image. In comparison to Histogram of Oriented Gradients, the proposed feature descriptor performs better in this scenario. Compression using PCA turns out to be able to increase both the F1-score as well as decreasing the Variability.

Keywords
Discrete Fourier Transform, Gabor Filters, Subspaces, Embedded Prototypes, Clustering, F1 score, Variability, Deep Learning, t-SNE
National Category
Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-481348 (URN)10.24132/JWSCG.2022.10 (DOI)
Funder
Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2022-08-09 Created: 2022-08-09 Last updated: 2022-08-09Bibliographically approved
Heil, R., Vats, E. & Hast, A. (2022). Paired Image to Image Translation for Strikethrough Removal from Handwritten Words. In: Uchida, S Barney, E Eglin, V (Ed.), DOCUMENT ANALYSIS SYSTEMS, DAS 2022: . Paper presented at 15th IAPR International Workshop on Document Analysis Systems (DAS), MAY 22-25, 2022, La Rochelle Univ, La Rochelle, FRANCE (pp. 309-322). Springer Nature, 13237
Open this publication in new window or tab >>Paired Image to Image Translation for Strikethrough Removal from Handwritten Words
2022 (English)In: DOCUMENT ANALYSIS SYSTEMS, DAS 2022 / [ed] Uchida, S Barney, E Eglin, V, Springer Nature, 2022, Vol. 13237, p. 309-322Conference paper, Published paper (Refereed)
Abstract [en]

Transcribing struck-through, handwritten words, for example for the purpose of genetic criticism, can pose a challenge to both humans and machines, due to the obstructive properties of the superimposed strokes. This paper investigates the use of paired image to image translation approaches to remove strikethrough strokes from handwritten words. Four different neural network architectures are examined, ranging from a few simple convolutional layers to deeper ones, employing Dense blocks. Experimental results, obtained from one synthetic and one genuine paired strikethrough dataset, confirm that the proposed paired models outperform the CycleGAN-based state of the art, while using less than a sixth of the trainable parameters.

Place, publisher, year, edition, pages
Springer Nature, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Strikethrough removal, Paired image to image translation, Handwritten words, Document image processing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-488232 (URN)10.1007/978-3-031-06555-2_21 (DOI)000870314500021 ()978-3-031-06555-2 (ISBN)978-3-031-06554-5 (ISBN)
Conference
15th IAPR International Workshop on Document Analysis Systems (DAS), MAY 22-25, 2022, La Rochelle Univ, La Rochelle, FRANCE
Funder
Swedish Research Council, 2018-05973Riksbankens Jubileumsfond, P19-0103:1
Available from: 2022-11-14 Created: 2022-11-14 Last updated: 2023-09-05Bibliographically approved
Heil, R., Nauwerck, M. & Hast, A. (2021). Shorthand Secrets: Deciphering Astrid Lindgren's Stenographed Drafts with HTR Methods. In: Dennis Dosso, Stefano Ferilli, Paolo Manghi, Antonella Poggi, Giuseppe Serra, Gianmaria Silvello (Ed.), : . Paper presented at Italian Research Conference on Digital Libraries IRCDL 2021 (pp. 169-177).
Open this publication in new window or tab >>Shorthand Secrets: Deciphering Astrid Lindgren's Stenographed Drafts with HTR Methods
2021 (English)In: / [ed] Dennis Dosso, Stefano Ferilli, Paolo Manghi, Antonella Poggi, Giuseppe Serra, Gianmaria Silvello, 2021, p. 169-177Conference paper, Published paper (Refereed)
Abstract [en]

Astrid Lindgren, Swedish author of children’s books, is knownfor having both composed and edited her literary work in the Melin sys-tem of shorthand (a Swedish shorthand system based on Gabelsberger).Her original drafts and manuscripts are preserved in 670 stenographednotepads kept at the National Library of Sweden and The Swedish Insti-tute of Children’s Books. For long these notepads have been consideredundecipherable and are until recently untouched by research.This paper introduces handwritten text recognition (HTR) and docu-ment image analysis (DIA) approaches to address the challenges inherentin Lindgren’s original drafts and manuscripts. It broadly covers aspectssuch as preprocessing and extraction of words, alignment of transcrip-tions and the fast transcription of large amounts of words.This is the first work to apply HTR and DIA to Gabelsberger-basedshorthand material. In particular, it presents early-stage results whichdemonstrate that these stenographed manuscripts can indeed be tran-scribed, both manually by experts and by employing computerised ap-proaches.

Keywords
Stenography, Handwritten Text Recognition, Digital Transcription, Document Image Analysis
National Category
Computer Vision and Robotics (Autonomous Systems) General Literature Studies
Identifiers
urn:nbn:se:uu:diva-455891 (URN)
Conference
Italian Research Conference on Digital Libraries IRCDL 2021
Funder
Riksbankens Jubileumsfond, P19-0103:1
Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2023-01-09Bibliographically approved
Heil, R., Vats, E. & Hast, A. (2021). Strikethrough Removal from Handwritten Words Using CycleGANs. In: Lladós J., Lopresti D., Uchida S. (Ed.), Document Analysis and Recognition -- ICDAR 2021: . Paper presented at International Conference on Document Analysis and Recognition (ICDAR) (pp. 572-586). Springer, 12824
Open this publication in new window or tab >>Strikethrough Removal from Handwritten Words Using CycleGANs
2021 (English)In: Document Analysis and Recognition -- ICDAR 2021 / [ed] Lladós J., Lopresti D., Uchida S., Springer, 2021, Vol. 12824, p. 572-586Conference paper, Published paper (Refereed)
Abstract [en]

Obtaining the original, clean forms of struck-through handwritten words can be of interest to literary scholars, focusing on tasks such as genetic criticism. In addition to this, replacing struck-through words can also have a positive impact on text recognition tasks. This work presents a novel unsupervised approach for strikethrough removal from handwritten words, employing cycle-consistent generative adversarial networks (CycleGANs). The removal performance is improved upon by extending the network with an attribute-guided approach. Furthermore, two new datasets, a synthetic multi-writer set, based on the IAM database, and a genuine single-writer dataset, are introduced for the training and evaluation of the models. The experimental results demonstrate the efficacy of the proposed method, where the examined attribute-guided models achieve F1 scores above 0.8 on the synthetic test set, improving upon the performance of the regular CycleGAN. Despite being trained exclusively on the synthetic dataset, the examined models even produce convincing cleaned images for genuine struck-through words. 

Place, publisher, year, edition, pages
Springer, 2021
Keywords
Strikethrough removal, CycleGAN, Handwritten words, Document image processing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-455889 (URN)10.1007/978-3-030-86337-1_38 (DOI)000711880100038 ()
Conference
International Conference on Document Analysis and Recognition (ICDAR)
Funder
Riksbankens Jubileumsfond, P19-0103:1Swedish Research Council, 2018-05973
Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2023-09-05Bibliographically approved
Hast, A. & Vats, E. (2021). Word Recognition using Embedded Prototype Subspace Classifiers on a new Imbalanced Dataset. Journal of WSCG, 29(1-2), 39-47
Open this publication in new window or tab >>Word Recognition using Embedded Prototype Subspace Classifiers on a new Imbalanced Dataset
2021 (English)In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 29, no 1-2, p. 39-47Article in journal (Refereed) Published
Abstract [en]

This paper presents an approach towards word recognition based on embedded prototype subspace classification. The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extracted from the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clustering techniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classes of words is heavily imbalanced, and some word classes are very similar, which renders the classification task rather challenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits is evaluated for different clustering techniques. It will be demonstrated that the original clustering technique based on scaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that finds k clusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clusters based on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both a clustering and embedding technique.

Place, publisher, year, edition, pages
University of West Bohemia, 2021
Keywords
Subspaces, Embedded Prototypes, Clustering, Deep Learning, Self Organising Maps, t-SNE, Data splits
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-453762 (URN)10.24132/JWSCG.2021.29.5 (DOI)
Funder
Riksbankens Jubileumsfond, NHS14-2068:1Swedish National Infrastructure for Computing (SNIC), 2020/15-177
Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2023-09-01Bibliographically approved
Hast, A. (2020). Consensus Ranking for Increasing Mean Average Precision in Keyword Spotting. In: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding. co-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020) Bari, Italy, January 29, 2020.: . Paper presented at VIPERC 2020, 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding.Bari, Italy, 29 January, 2020. (pp. 46-57). , 2602
Open this publication in new window or tab >>Consensus Ranking for Increasing Mean Average Precision in Keyword Spotting
2020 (English)In: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding. co-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020) Bari, Italy, January 29, 2020., 2020, Vol. 2602, p. 46-57Conference paper, Published paper (Refereed)
Abstract [en]

Word spotting use a query word image to find any instances of that word among document images. The obtained list of words is ranked according to similarity to the query word. Ideally, any false positives should only occur in the end of that list. However, in reality they often occur higher up, which decreases the so called mean average precision. It is shown how creating new ranked lists by re-scoring using the top n occurrences in the original list, and then fusing the scores, can increase the mean average precision.

National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-410224 (URN)
Conference
VIPERC 2020, 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding.Bari, Italy, 29 January, 2020.
Available from: 2020-05-12 Created: 2020-05-12 Last updated: 2020-05-13Bibliographically approved
Hast, A. & Lind, M. (2020). Ensembles and Cascading of Embedded Prototype Subspace Classifiers. Journal of WSCG, 28(1/2), 89-95
Open this publication in new window or tab >>Ensembles and Cascading of Embedded Prototype Subspace Classifiers
2020 (English)In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 28, no 1/2, p. 89-95Article in journal (Refereed) Published
Abstract [en]

Deep learning approaches suffer from the so called interpretability problem and can therefore be very hard to visualise. Embedded Prototype Subspace Classifiers is one attempt in the field of explainable AI, which is both fast and efficient since it does not require repeated learning epochs and has no hidden layers. In this paper we investigate how ensembles and cascades of ensembles perform on some popular datasets. The focus is on handwritten data such as digits, letters and signs. It is shown how cascading can be efficiently implemented in order to both increase accuracy as well as speed up the classification.

Keywords
Subspaces, Ensembles, Cascading, Embedded Prototypes, Neural Networks, Deep Learning.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-416637 (URN)10.24132/JWSCG.2020.28.11 (DOI)
Projects
q2b
Available from: 2020-07-25 Created: 2020-07-25 Last updated: 2020-08-12Bibliographically approved
Amelio, A., Borgefors, G. & Hast, A. (Eds.). (2020). Visual Pattern Extraction and Recognition for Cultural Heritage Understanding: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understandingco-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020). CEUR: CEUR
Open this publication in new window or tab >>Visual Pattern Extraction and Recognition for Cultural Heritage Understanding: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understandingco-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020)
2020 (English)Collection (editor) (Refereed)
Place, publisher, year, edition, pages
CEUR: CEUR, 2020
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-410226 (URN)
Available from: 2020-05-12 Created: 2020-05-12 Last updated: 2020-09-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1054-2754

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