| 1. |
- Arvidsson, Ida, et al.
(författare)
-
Comparison of different augmentation techniques for improved generalization performance for gleason grading
- 2019
-
Ingår i: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). - 9781538636411 ; , s. 923-927
-
Konferensbidrag (refereegranskat)abstract
- The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate biopsies, stained with HE, detailed annotated with Gleason grades. We obtained results similar to previous studies, with accuracies of 77% for Gleason grading for images from the same site as the training data and 59% for images from other sites. However, we also found out that the use of traditional augmentation techniques gave better performance compared to when using cycle GANs, either to augment the training data or to normalize the test data.
|
|
| 2. |
- Arvidsson, Ida, et al.
(författare)
-
Generalization of prostate cancer classification for multiple sites using deep learning
- 2018
-
Ingår i: 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. - 9781538636367 ; 2018-April, s. 191-194
-
Konferensbidrag (refereegranskat)abstract
- Deep learning has the potential to drastically increase the accuracy and efficiency of prostate cancer diagnosis, which would be of uttermost use. Today the diagnosis is determined manually from H&E stained specimens using a light microscope. In this paper several different approaches based on convolutional neural networks for prostate cancer classification are presented and compared, using three different datasets with different origins. The issue that algorithms trained on a certain site might not generalize to other sites, due to for example inevitable stain variations, is highlighted. Two different techniques to overcome this complication are compared; by training the networks using color augmentation and by using digital stain separation. Furthermore, the potential of using an autoencoder to get a more efficient downsampling is investigated, which turned out to be the method giving the best generalization. We achieve accuracies of 95% for classification of benign versus malignant tissue and 81% for Gleason grading for data from the same site as the training data. The corresponding accuracies for images from other sites are in average 88% and 52% respectively.
|
|
| 3. |
- Batstone, Kenneth, et al.
(författare)
-
Collaborative merging of radio SLAM maps in view of crowd-sourced data acquisition and big data
- 2019
-
Ingår i: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 807-813
-
Konferensbidrag (refereegranskat)abstract
- Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition.
|
|
| 4. |
- Gummeson, Anna, et al.
(författare)
-
Fast and efficient minimal solvers for quadric based camera pose estimation
- 2022
-
Ingår i: 2022 26th International Conference on Pattern Recognition, ICPR 2022. - 9781665490627 ; , s. 3973-3979
-
Konferensbidrag (refereegranskat)abstract
- In this paper we address absolute camera pose estimation. An efficient (and standard) way to solve this problem, is to use sparse keypoint correspondences. In many cases point features are not available, or are unstable over time and viewing conditions. We propose a framework based on silhouettes of quadric surfaces, with special emphasis on cylinders. We provide mathematical analysis of the problem of projected cylinders in particular, but also general quadrics. We develop a number of minimal solvers for estimating camera pose from silhouette lines of cylinders, given different calibration and cylinder properties. These solvers can be used efficiently in bootstrapping robust estimation schemes, such as RANSAC. Note that even though we have lines as image features, this is a different case than line based pose estimation, since we do not have 2D-line to 3D-line correspondences. We perform synthetic accuracy and robustness tests and evaluate on a number of real case scenarios.
|
|
| 5. |
- Kjellson, Christoffer, et al.
(författare)
-
Accurate Indoor Positioning Based on Learned Absolute and Relative Models
- 2021
-
Ingår i: 2021 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2021. - 9781665404020
-
Konferensbidrag (refereegranskat)abstract
- To improve the accuracy of indoor positioning systems it can be useful to combine different types of sensor data. This paper describes deep learning methods both for estimating absolute positions and for performing pedestrian dead reckoning, and then how to combine the resulting estimates using weighted least squares optimization. The positioning model is based on a custom neural network which uses measurements of received signal strength indication from one instant of time as input. The model for estimating relative positions is on the other hand based on inertial sensors, the accelerometer, magnetometer and gyroscope. The position estimates are then combined using a least squares approach with weights based on the standard deviations of errors in predictions from the used models.
|
|
| 6. |
- Larsson, Martin, et al.
(författare)
-
Optimal Trilateration Is an Eigenvalue Problem
- 2019
-
Ingår i: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. - 9781479981311 ; 2019-May, s. 5586-5590
-
Konferensbidrag (refereegranskat)abstract
- The problem of estimating receiver or sender node positions from measured receiver-sender distances is a key issue in different applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using UWB or using round-trip-time measurements between mobile phones and WiFi-units. In this paper we address the problem of optimally estimating a receiver position given a number of distance measurements to known sender positions, so called trilateration. We show that this problem can be rephrased as an eigenvalue problem. We also address different error models and the multilateration setting where an additional offset is also unknown, and show that these problems can be modeled using the same framework.
|
|
| 7. |
- Larsson, Martin, et al.
(författare)
-
Trilateration Using Motion Models
- 2022
-
Ingår i: 2022 25th International Conference on Information Fusion, FUSION 2022. - 9781737749721 ; , s. 01-07
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.
|
|
| 8. |
- Larsson, Viktor, et al.
(författare)
-
Beyond Gröbner Bases : Basis Selection for Minimal Solvers
- 2018
-
Ingår i: Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. - 9781538664209 ; , s. 3945-3954
-
Konferensbidrag (refereegranskat)abstract
- Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have traditionally been based on a Grobner basis for the polynomial ideal. Here we describe how we can enumerate all such bases in an efficient way. We also show that going beyond Grobner bases leads to more efficient solvers in many cases. We present a novel basis sampling scheme that we evaluate on a number of problems.
|
|
| 9. |
- Moliner, Olivier, et al.
(författare)
-
Bootstrapped Representation Learning for Skeleton-Based Action Recognition
- 2022
-
Ingår i: Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. - 2160-7516 .- 2160-7508. - 9781665487399 ; 2022-June, s. 4153-4163
-
Konferensbidrag (refereegranskat)abstract
- In this work, we study self-supervised representation learning for 3D skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL) for representation learning on skeleton sequence data and propose a new data augmentation strategy including two asymmetric transformation pipelines. We also introduce a multi-viewpoint sampling method that leverages multiple viewing angles of the same action captured by different cameras. In the semi-supervised setting, we show that the performance can be further improved by knowledge distillation from wider networks, leveraging once more the unlabeled samples. We conduct extensive experiments on the NTU-60, NTU-120 and PKU-MMD datasets to demonstrate the performance of our proposed method. Our method consistently outperforms the current state of the art on linear evaluation, semi-supervised and transfer learning benchmarks.
|
|
| 10. |
- Ornhag, Marcus Valtonen, et al.
(författare)
-
Efficient real-time radial distortion correction for UAVs
- 2021
-
Ingår i: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). - 9781665404778 - 9780738142661 - 9781665446402 ; , s. 1750-1759
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs in real-time. This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. By utilizing the IMU data, the cameras can be aligned with the gravity direction. This allows us to work with fewer degrees of freedom, and opens up for further intrinsic calibration. We propose a fast and robust minimal solver for simultaneously estimating the focal length, radial distortion profile and motion parameters from homographies. The proposed solver is tested on both synthetic and real data, and perform better or on par with state-of-the-art methods relying on pre-calibration procedures. Code available at: https://github.com/marcusvaltonen/HomLib.1
|
|
| 11. |
- Persson, Patrik, et al.
(författare)
-
Parameterization of Ambiguity in Monocular Depth Prediction
- 2021
-
Ingår i: Proceedings - 2021 International Conference on 3D Vision, 3DV 2021. - 9781665426886 ; , s. 761-770
-
Konferensbidrag (refereegranskat)abstract
- Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps,the result often has poor metric accuracy. Moreover,the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable monocular predictions. By searching the parameterization we can combine monocular estimation with traditional photoconsistency or geometry based methods to achieve both visually appealing and metrically accurate surface estimations. Since we relax the problem we are able to work with smaller networks than current architectures. In addition we design a self-supervised training scheme,eliminating the need for ground truth image depth-map pairs. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches.
|
|
| 12. |
- Valtonen Ornhag, Marcus, et al.
(författare)
-
Trust Your IMU : Consequences of Ignoring the IMU Drift
- 2022
-
Ingår i: Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. - 2160-7516 .- 2160-7508. - 9781665487399 ; 2022-June, s. 4467-4476
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups.The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://github.com/marcusvaltonen/DronePoseLib.1
|
|
