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Bešenić, K., Ahlberg, J. & Pandžić, I. (2019). Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019): . Paper presented at International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 209-217). SciTePress, 5
Open this publication in new window or tab >>Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation
2019 (English)In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), SciTePress, 2019, Vol. 5, p. 209-217Conference paper, Published paper (Refereed)
Abstract [en]

Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with age and gender annotations are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts weakly labeled noisy data. The unsupervised facial biometric data filtering method presented in this paper greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped facial datasets demonstrate the effectiveness of the proposed method, with respect to training and validation scores, training convergence, and generalization capabilities of trained age and gender estimators.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Biometric, Web-Scraping, Age, Gender
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-154867 (URN)978-989-758-354-4 (ISBN)
Conference
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-04-03
Berg, A., Ahlberg, J. & Felsberg, M. (2018). Generating Visible Spectrum Images from Thermal Infrared. In: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018: . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8-22 June 2018, Salt Lake City, UT, USA (pp. 1143-1152). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Generating Visible Spectrum Images from Thermal Infrared
2018 (English)In: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1143-1152Conference paper, Published paper (Refereed)
Abstract [en]

Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre- nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, E-ISSN 2160-7516
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-149429 (URN)10.1109/CVPRW.2018.00159 (DOI)9781538661000 (ISBN)9781538661017 (ISBN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8-22 June 2018, Salt Lake City, UT, USA
Funder
Swedish Research Council, 2013-5703Swedish Research Council, 2014-6227
Note

Print on Demand(PoD) ISSN: 2160-7508.

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2019-06-17Bibliographically approved
Markus, N., Gogic, I., Pandžic, I. & Ahlberg, J. (2018). Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment. In: : . Paper presented at British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018. Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition, Article ID 896.
Open this publication in new window or tab >>Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

Place, publisher, year, edition, pages
Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition, 2018
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-152550 (URN)
Conference
British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-11-16Bibliographically approved
Hatami, S., Dahl-Jendelin, A., Ahlberg, J. & Nelsson, C. (2018). Selective Laser Melting Process Monitoring by Means of Thermography. In: Proceedings of Euro Powder Metallurgy Congress (Euro PM): . Paper presented at Euro Powder Metallurgy Congress (Euro PM). European Powder Metallurgy Association (EPMA), Article ID 3957771.
Open this publication in new window or tab >>Selective Laser Melting Process Monitoring by Means of Thermography
2018 (English)In: Proceedings of Euro Powder Metallurgy Congress (Euro PM), European Powder Metallurgy Association (EPMA) , 2018, article id 3957771Conference paper, Published paper (Refereed)
Abstract [en]

Selective laser melting (SLM) enables production of highly intricate components. From this point of view, the capabilities of this technology are known to the industry and have been demonstrated in numerous applications. Nonetheless, for serial production purposes the manufacturing industry has so far been reluctant in substituting its conventional methods with SLM. One underlying reason is the lack of simple and reliable process monitoring methods. This study examines the feasibility of using thermography for process monitoring. To this end, an infra-red (IR) camera was mounted off-axis to monitor and record the temperature of every layer. The recorded temperature curves are analysed and interpreted with respect to different stages of the process. Furthermore, the possibility of detecting variations in laser settings by means of thermography is demonstrated. The results show that once thermal patterns are identified, this data can be utilized for in-process and post-process monitoring of SLM production.

Place, publisher, year, edition, pages
European Powder Metallurgy Association (EPMA), 2018
National Category
Metallurgy and Metallic Materials
Identifiers
urn:nbn:se:liu:diva-152570 (URN)978-1-899072-50-7 (ISBN)
Conference
Euro Powder Metallurgy Congress (Euro PM)
Funder
VINNOVA, 2016-04486
Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-21
Nawaz, T., Berg, A., Ferryman, J., Ahlberg, J. & Felsberg, M. (2017). Effective evaluation of privacy protection techniques in visible and thermal imagery. Journal of Electronic Imaging (JEI), 26(5), Article ID 051408.
Open this publication in new window or tab >>Effective evaluation of privacy protection techniques in visible and thermal imagery
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2017 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 26, no 5, article id 051408Article in journal (Refereed) Published
Abstract [en]

Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This work proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset, called TST-Priv, is made available online below for community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of the proposed method over existing methods.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-140495 (URN)10.1117/1.JEI.26.5.051408 (DOI)000414251400009 ()
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784
Note

Funding agencies:  Swedish Research Council through the project Learning Systems for Remote Thermography [D0570301]; European Community [312784]

Available from: 2017-09-05 Created: 2017-09-05 Last updated: 2018-01-13Bibliographically approved
Berg, A., Ahlberg, J. & Felsberg, M. (2017). Object Tracking in Thermal Infrared Imagery based on Channel Coded Distribution Fields. In: : . Paper presented at Swedish Symposium on Image Analysis. Svenska sällskapet för automatiserad bildanalys (SSBA)
Open this publication in new window or tab >>Object Tracking in Thermal Infrared Imagery based on Channel Coded Distribution Fields
2017 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. Tracking methods designed for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

Place, publisher, year, edition, pages
Svenska sällskapet för automatiserad bildanalys (SSBA), 2017
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-136743 (URN)
Conference
Swedish Symposium on Image Analysis
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567Swedish Research Council, 2014-6227
Available from: 2017-04-24 Created: 2017-04-24 Last updated: 2019-05-09Bibliographically approved
Berg, A., Felsberg, M., Häger, G. & Ahlberg, J. (2016). An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge. In: : . Paper presented at Swedish Symposium on Image Analysis.
Open this publication in new window or tab >>An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.

Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-127598 (URN)
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2018-01-10Bibliographically approved
Felsberg, M., Kristan, M., Matas, J., Leonardis, A., Pflugfelder, R., Häger, G., . . . He, Z. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results. In: Hua G., Jégou H. (Ed.), Computer Vision – ECCV 2016 Workshops. ECCV 2016.: . Paper presented at 14th European Conference on Computer Vision (ECCV) (pp. 824-849). SPRINGER INT PUBLISHING AG
Open this publication in new window or tab >>The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
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2016 (English)In: Computer Vision – ECCV 2016 Workshops. ECCV 2016. / [ed] Hua G., Jégou H., SPRINGER INT PUBLISHING AG , 2016, p. 824-849Conference paper, Published paper (Refereed)
Abstract [en]

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

Place, publisher, year, edition, pages
SPRINGER INT PUBLISHING AG, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9914
Keywords
Performance evaluation; Object tracking; Thermal IR; VOT
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-133773 (URN)10.1007/978-3-319-48881-3_55 (DOI)000389501700055 ()978-3-319-48881-3 (ISBN)978-3-319-48880-6 (ISBN)
Conference
14th European Conference on Computer Vision (ECCV)
Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2018-10-15
Berg, A., Ahlberg, J. & Felsberg, M. (2015). A thermal infrared dataset for evaluation of short-term tracking methods. In: : . Paper presented at Swedish Symposium on Image Analysis.
Open this publication in new window or tab >>A thermal infrared dataset for evaluation of short-term tracking methods
2015 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

During recent years, thermal cameras have decreased in both size and cost while improving image quality. The area of use for such cameras has expanded with many exciting applications, many of which require tracking of objects. While being subject to extensive research in the visual domain, tracking in thermal imagery has historically been of interest mainly for military purposes. The available thermal infrared datasets for evaluating methods addressing these problems are few and the ones that do are not challenging enough for today’s tracking algorithms. Therefore, we hereby propose a thermal infrared dataset for evaluation of short-term tracking methods. The dataset consists of 20 sequences which have been collected from multiple sources and the data format used is in accordance with the Visual Object Tracking (VOT) Challenge.

Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-127541 (URN)
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2018-01-10Bibliographically approved
Berg, A., Ahlberg, J. & Felsberg, M. (2015). A Thermal Object Tracking Benchmark. In: : . Paper presented at 12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, August 25-28 2015. IEEE
Open this publication in new window or tab >>A Thermal Object Tracking Benchmark
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121001 (URN)10.1109/AVSS.2015.7301772 (DOI)000380619700052 ()978-1-4673-7632-7 (ISBN)
Conference
12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, August 25-28 2015
Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6763-5487

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