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Xiong, Z., Jonnarth, A., Eldesokey, A., Johnander, J., Wandt, B. & Forssén, P.-E. (2024). Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW): . Paper presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 17-18 June 2024 (pp. 3471-3480). IEEE, abs/1803.04765
Open this publication in new window or tab >>Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
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2024 (English)In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE , 2024, Vol. abs/1803.04765, p. 3471-3480Conference paper, Published paper (Refereed)
Abstract [en]

Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:liu:diva-208088 (URN)10.1109/cvprw63382.2024.00351 (DOI)9798350365474 (ISBN)9798350365481 (ISBN)
Conference
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 17-18 June 2024
Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2025-02-07Bibliographically approved
Barberi, E., Cucinotta, F., Forssén, P.-E., Raffaele, M. & Salmeri, F. (2023). A differential entropy-based method for reverse engineering quality assessment. In: : . Paper presented at ADM 2023 International Conference, Florence, Italy 6-8 September 2023.
Open this publication in new window or tab >>A differential entropy-based method for reverse engineering quality assessment
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2023 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The present work proposes the use of point clouds differential entropy as a method for reverse engineering quality assessment. This quality assessment can be used to measure the deviation of objects made with additive manufacturing or CNC techniques. The quality of the execution is intended as a measure of the deviation of the geometry of the obtained object compared to the original CAD. This paper proposes the use of the quality index of the CorAl method to assess the quality of an objects compared to its original CAD. This index, based on the differential entropy, takes on a value the closer to 0 the more they obtained object is close to the original geometry. The advantage of this method is to have a global synthetic index. It is however possible to have entropy maps of the individual points to verify which are the areas with the greatest deviation. The method is robust for comparing point clouds at different densities. Objects obtained by additive manufacturing with different print qualities were used. The quality index evaluated for each object, as defined in the CorAl method, turns out to be gradually closer to 0 as the quality of the piece's construction increases.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-198606 (URN)
Conference
ADM 2023 International Conference, Florence, Italy 6-8 September 2023
Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2023-10-20
Brissman, E., Forssén, P.-E. & Edstedt, J. (2023). Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods. In: Gade, R., Felsberg, M., Kämäräinen, JK (Ed.), : . Paper presented at 22nd Scandinavian Conference, SCIA 2023 Sirkka, Finland, April 18–21, 2023 (pp. 34-49).
Open this publication in new window or tab >>Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods
2023 (English)In: / [ed] Gade, R., Felsberg, M., Kämäräinen, JK, 2023, p. 34-49Conference paper, Published paper (Refereed)
Abstract [en]

A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models’ residual variances—however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13885
National Category
Probability Theory and Statistics Computer graphics and computer vision
Identifiers
urn:nbn:se:liu:diva-198605 (URN)10.1007/978-3-031-31435-3_3 (DOI)978-3-031-31434-6 (ISBN)978-3-031-31435-3 (ISBN)
Conference
22nd Scandinavian Conference, SCIA 2023 Sirkka, Finland, April 18–21, 2023
Note

Funding agencies: This work was partially supported by the Wallenberg AI, Autonomous Systems, and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation; and the computations were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre; and a point cloud of a realistic scene was provided by the Swedish National Forensic Centre (NFC).

Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2025-02-01
Persson, M., Häger, G., Ovrén, H. & Forssén, P.-E. (2021). Practical Pose Trajectory Splines With Explicit Regularization. In: 2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021): . Paper presented at 9th International Conference on 3D Vision (3DV), ELECTR NETWORK, dec 01-03, 2021 (pp. 156-165). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Practical Pose Trajectory Splines With Explicit Regularization
2021 (English)In: 2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021), Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 156-165Conference paper, Published paper (Refereed)
Abstract [en]

We investigate spline-based continuous-time pose trajectory estimation using non-linear explicit motion priors. Current regularization priors either linearize the orientation, rely on the implicit regularization obtained from the used spline basis function, or use sampling based regularization schemes. The latter is a special case of a Riemann sum approximation, and we demonstrate when and why this can fail, and propose a way to avoid these issues. In addition we provide a number of novel practically useful theoretical contributions, including requirements on knot spacing for orientation splines, new basis functions for constant velocity extrapolation, and a generalization of the popular P-Spline penalty to orientation. We analyze the properties of the proposed approach using synthetic data. We validate our system using the standard task of visual-inertial calibration, and apply it to stereo visual odometry where we demonstrate real-time performance on KITTI.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
International Conference on 3D Vision, ISSN 2378-3826, E-ISSN 2475-7888
National Category
Computer graphics and computer vision Computer Sciences
Identifiers
urn:nbn:se:liu:diva-182729 (URN)10.1109/3DV53792.2021.00026 (DOI)000786496000016 ()9781665426886 (ISBN)9781665426893 (ISBN)
Conference
9th International Conference on 3D Vision (3DV), ELECTR NETWORK, dec 01-03, 2021
Funder
Vinnova
Note

Funding: Vinnova through the Visual Sweden networkVinnova [Dnr 2019-02261]

Available from: 2022-02-07 Created: 2022-02-07 Last updated: 2025-02-01Bibliographically approved
Järemo-Lawin, F. & Forssén, P.-E. (2020). Registration Loss Learning for Deep Probabilistic Point Set Registration. In: 2020 International Conference on 3D Vision (3DV): . Paper presented at International Virtual Conference on 3D Vision, November 25-28, 2020 (pp. 563-572). IEEE
Open this publication in new window or tab >>Registration Loss Learning for Deep Probabilistic Point Set Registration
2020 (English)In: 2020 International Conference on 3D Vision (3DV), IEEE, 2020, p. 563-572Conference paper, Published paper (Refereed)
Abstract [en]

Probabilistic methods for point set registration have interesting theoretical properties, such as linear complexity in the number of used points, and they easily generalize to joint registration of multiple point sets. In this work, we improve their recognition performance to match state of the art. This is done by incorporating learned features, by adding a von Mises-Fisher feature model in each mixture component, and by using learned attention weights. We learn these jointly using a registration loss learning strategy (RLL) that directly uses the registration error as a loss, by back-propagating through the registration iterations. This is possible as the probabilistic registration is fully differentiable, and the result is a learning framework that is truly end-to-end. We perform extensive experiments on the 3DMatch and Kitti datasets. The experiments demonstrate that our approach benefits significantly from the integration of the learned features and our learning strategy, outperforming the state-of-the-art on Kitti. Code is available at https://github.com/felja633/RLLReg.

Place, publisher, year, edition, pages
IEEE, 2020
Series
International Conference on 3D Vision, ISSN 2378-3826
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-173539 (URN)10.1109/3DV50981.2020.00066 (DOI)000653085200057 ()978-1-7281-8128-8 (ISBN)978-1-7281-8129-5 (ISBN)
Conference
International Virtual Conference on 3D Vision, November 25-28, 2020
Note

Funding Agencies|ELLIIT Excellence Center; Vinnova through the Visual Sweden networkVinnova [2019-02261]

Available from: 2021-02-22 Created: 2021-02-22 Last updated: 2022-10-06Bibliographically approved
Felsberg, M., Forssén, P.-E., Sintorn, I.-M. & Unger, J. (Eds.). (2019). Image Analysis. Paper presented at 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11-13, 2019. Springer
Open this publication in new window or tab >>Image Analysis
2019 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This volume constitutes the refereed proceedings of the 21st Scandinavian Conference on Image Analysis, SCIA 2019, held in Norrköping, Sweden, in June 2019.

The 40 revised papers presented were carefully reviewed and selected from 63 submissions. The contributions are structured in topical sections on Deep convolutional neural networks; Feature extraction and image analysis; Matching, tracking and geometry; and Medical and biomedical image analysis.

Place, publisher, year, edition, pages
Springer, 2019. p. 600
Series
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11482
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:liu:diva-163196 (URN)10.1007/978-3-030-20205-7 (DOI)9783030202040 (ISBN)9783030202057 (ISBN)
Conference
21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11-13, 2019
Available from: 2020-01-22 Created: 2020-01-22 Last updated: 2025-02-07Bibliographically approved
Järemo Lawin, F., Danelljan, M., Khan, F. S., Forssén, P.-E. & Felsberg, M. (2018). Density Adaptive Point Set Registration. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, United States, 18-22 June, 2018 (pp. 3829-3837). IEEE
Open this publication in new window or tab >>Density Adaptive Point Set Registration
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2018 (English)In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, p. 3829-3837Conference paper, Published paper (Refereed)
Abstract [en]

Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets.    We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Conference on Computer Vision and Pattern Recognition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-149774 (URN)10.1109/CVPR.2018.00403 (DOI)000457843603101 ()978-1-5386-6420-9 (ISBN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, United States, 18-22 June, 2018
Note

Funding Agencies|EUs Horizon 2020 Programme [644839]; CENIIT grant [18.14]; VR grant: EMC2 [2014-6227]; VR grant [2016-05543]; VR grant: LCMM [2014-5928]

Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2023-04-03Bibliographically approved
Ovrén, H. & Forssén, P.-E. (2018). Spline Error Weighting for Robust Visual-Inertial Fusion. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2018, Salt Lake City, USA (pp. 321-329).
Open this publication in new window or tab >>Spline Error Weighting for Robust Visual-Inertial Fusion
2018 (English)In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p. 321-329Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we derive and test a probability-based weighting that can balance residuals of different types in spline fitting. In contrast to previous formulations, the proposed spline error weighting scheme also incorporates a prediction of the approximation error of the spline fit. We demonstrate the effectiveness of the prediction in a synthetic experiment, and apply it to visual-inertial fusion on rolling shutter cameras. This results in a method that can estimate 3D structure with metric scale on generic first-person videos. We also propose a quality measure for spline fitting, that can be used to automatically select the knot spacing. Experiments verify that the obtained trajectory quality corresponds well with the requested quality. Finally, by linearly scaling the weights, we show that the proposed spline error weighting minimizes the estimation errors on real sequences, in terms of scale and end-point errors.

Series
Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-149495 (URN)10.1109/CVPR.2018.00041 (DOI)000457843600034 ()978-1-5386-6420-9 (ISBN)978-1-5386-6421-6 (ISBN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2018, Salt Lake City, USA
Funder
Swedish Research Council, 2014-5928Swedish Research Council, 2014-6227
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2019-02-26Bibliographically approved
Wallenberg, M. & Forssen, P.-E. (2017). Attentional Masking for Pre-trained Deep Networks. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS17): . Paper presented at The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), September 24–28, Vancouver, Canada (pp. 6149-6154). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Attentional Masking for Pre-trained Deep Networks
2017 (English)In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS17), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 6149-6154Conference paper, Published paper (Refereed)
Abstract [en]

The ability to direct visual attention is a fundamental skill for seeing robots. Attention comes in two flavours: the gaze direction (overt attention) and attention to a specific part of the current field of view (covert attention), of which the latter is the focus of the present study. Specifically, we study the effects of attentional masking within pre-trained deep neural networks for the purpose of handling ambiguous scenes containing multiple objects. We investigate several variants of attentional masking on partially pre-trained deep neural networks and evaluate the effects on classification performance and sensitivity to attention mask errors in multi-object scenes. We find that a combined scheme consisting of multi-level masking and blending provides the best trade-off between classification accuracy and insensitivity to masking errors. This proposed approach is denoted multilayer continuous-valued convolutional feature masking (MC-CFM). For reasonably accurate masks it can suppress the influence of distracting objects and reach comparable classification performance to unmasked recognition in cases without distractors.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
National Category
Computer graphics and computer vision Computer Systems
Identifiers
urn:nbn:se:liu:diva-142061 (URN)10.1109/IROS.2017.8206516 (DOI)000426978205110 ()978-1-5386-2682-5 (ISBN)
Conference
The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), September 24–28, Vancouver, Canada
Note

Funding agencies: Swedish Research Council [2014-5928]; Linkoping University

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2025-02-01Bibliographically approved
Eilertsen, G., Forssén, P.-E. & Unger, J. (2017). BriefMatch: Dense binary feature matching for real-time optical flow estimation. In: Puneet Sharma, Filippo Maria Bianchi (Ed.), Proceedings of the Scandinavian Conference on Image Analysis (SCIA17): . Paper presented at Scandinavian Conference on Image Analysis (SCIA17), Tromsø, Norway, 12-4 June, 2017 (pp. 221-233). Springer, 10269
Open this publication in new window or tab >>BriefMatch: Dense binary feature matching for real-time optical flow estimation
2017 (English)In: Proceedings of the Scandinavian Conference on Image Analysis (SCIA17) / [ed] Puneet Sharma, Filippo Maria Bianchi, Springer, 2017, Vol. 10269, p. 221-233Conference paper, Published paper (Refereed)
Abstract [en]

Research in optical flow estimation has to a large extent focused on achieving the best possible quality with no regards to running time. Nevertheless, in a number of important applications the speed is crucial. To address this problem we present BriefMatch, a real-time optical flow method that is suitable for live applications. The method combines binary features with the search strategy from PatchMatch in order to efficiently find a dense correspondence field between images. We show that the BRIEF descriptor provides better candidates (less outlier-prone) in shorter time, when compared to direct pixel comparisons and the Census transform. This allows us to achieve high quality results from a simple filtering of the initially matched candidates. Currently, BriefMatch has the fastest running time on the Middlebury benchmark, while placing highest of all the methods that run in shorter than 0.5 seconds.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
computer vision, optical flow, feature matching, real-time computation
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:liu:diva-149418 (URN)10.1007/978-3-319-59126-1_19 (DOI)000454359300019 ()2-s2.0-85020383306 (Scopus ID)978-3-319-59125-4 (ISBN)
Conference
Scandinavian Conference on Image Analysis (SCIA17), Tromsø, Norway, 12-4 June, 2017
Available from: 2018-06-28 Created: 2018-06-28 Last updated: 2025-02-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5698-5983

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