| 1. |
- Källén, Hanna, et al.
(författare)
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Towards Grading Gleason Score using Generically Trained Deep convolutional Neural Networks
- 2016
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Ingår i: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479923496 - 9781479923502 ; 2016-June, s. 1163-1167
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Konferensbidrag (refereegranskat)abstract
- We developed an automatic algorithm with the purpose to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen. In order to detect and classify the cancerous tissue, a deep convolutional neural network that had been pre-trained on a large set of photographic images was used. A specific aim was to support intuitive interaction with the result, to let pathologists adjust and correct the output. Therefore, we have designed an algorithm that makes a spatial classification of the whole slide into the same growth patterns as pathologists do. The 22-layer network was cut at an earlier layer and the output from that layer was used to train both a random forest classifier and a support vector machines classifier. At a specific layer a small patch of the image was used to calculate a feature vector and an image is represented by a number of those vectors. We have classified both the individual patches and the entire images. The classification results were compared for different scales of the images and feature vectors from two different layers from the network. Testing was made on a dataset consisting of 213 images, all containing a single class, benign tissue or Gleason score 3-5. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.
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| 2. |
- Weegar, Rebecka, et al.
(författare)
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Linking Entities Across Images and Text
- 2015
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Ingår i: Proceedings of the 19th Conference on Computational Language Learning. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781941643778 ; , s. 185-193
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Konferensbidrag (refereegranskat)abstract
- This paper describes a set of methods to link entities across images and text. Asa corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category. We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels. We also measured the statistical associations between these mentions and the labels and we combined them with the semantic similarity to produce mappings in the form of pairs consisting of a region label and a caption entity. In a second step, we used the syntactic relationships between the mentions and the spatial relationships between the regions to rerank the lists of candidate mappings. To evaluate our methods, we annotated a test set of 200 images, where we manually linked the image regions to their corresponding mentions in the captions. Eventually, we could match objects in pictures to their correct mentions for nearly 89 percent of the segments, when such a matching exists.
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| 3. |
- Batstone, Kenneth, et al.
(författare)
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Robust time-of-arrival self calibration and indoor localization using Wi-Fi round-trip time measurements
- 2016
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Ingår i: Communications Workshops (ICC), 2016 IEEE International Conference on. - 9781509004485 ; , s. 26-31
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating receiver-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 and mapping and positioning using round-trip-time measurements between mobile phones and Wi-Fi-units. Thanks to recent research in this area we have an increased understanding of the geometry of this problem. In this paper, we study the problem of missing information and the presence of outliers in the given data. We propose a novel hypothesis and test framework that efficiently finds initial estimates of the unknown parameters and combine such methods with optimization techniques to obtain accurate and robust systems. The proposed systems are evaluated using Wi-Fi round-trip time measurements to give a realistic example of indoor localization. The resulting map of the anchor points is validated against ground truth measurements with promising results.
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| 4. |
- Flood, Gabrielle, et al.
(författare)
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Efficient Merging of Maps and Detection of Changes
- 2019
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Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 348-360
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Konferensbidrag (refereegranskat)abstract
- With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.
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| 5. |
- Flood, Gabrielle, et al.
(författare)
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Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals
- 2019
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Ingår i: Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030054984 ; 11351 LNCS, s. 116-138
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Konferensbidrag (refereegranskat)abstract
- Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinements that are more sensitive to noise, but also give higher precision when they are initialized close to the real translation. In this paper, we present stochastic models that can explain the precision limits of such sub-sample time-difference estimates. Using these models new methods are provided for precise estimates of time-differences as well as Doppler effects. The developed methods are evaluated and verified on both synthetic and real data.
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| 6. |
- Grundström, Jakob, et al.
(författare)
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Transferring and compressing convolutional neural networks for face representations
- 2016
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319415017 - 9783319415000 ; 9730, s. 20-29
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Konferensbidrag (refereegranskat)abstract
- In this work we have investigated face verification based on deep representations from Convolutional Neural Networks (CNNs) to find an accurate and compact face descriptor trained only on a restricted amount of face image data. Transfer learning by fine-tuning CNNs pre-trained on large-scale object recognition has been shown to be a suitable approach to counter a limited amount of target domain data. Using model compression we reduced the model complexity without significant loss in accuracy and made the feature extraction more feasible for real-time use and deployment on embedded systems and mobile devices. The compression resulted in a 9-fold reduction in number of parameters and a 5-fold speed-up in the average feature extraction time running on a desktop CPU. With continued training of the compressed model using a Siamese Network setup, it outperformed the larger model.
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| 7. |
- Haner, Sebastian, et al.
(författare)
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Absolute pose for cameras under flat refractive interfaces
- 2015
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Ingår i: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - 9781467369640 ; , s. 1428-1436
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Konferensbidrag (refereegranskat)abstract
- This paper studies the problem of determining the absolute pose of a perspective camera observing a scene through a known refractive plane, the flat boundary between transparent media with different refractive indices. Efficient minimal solvers are developed for the 2D, known orientation and known rotation axis cases, and near-minimal solvers for the general calibrated and unknown focal length cases. We show that ambiguities in the equations of Snell's law give rise to a large number of false solutions, increasing the complexity of the problem. Evaluation of the solvers on both synthetic and real data show excellent numerical performance, and the necessity of explicitly modelling refraction to obtain accurate pose estimates.
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| 8. |
- Isaksson, Johan, et al.
(författare)
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Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN
- 2017
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Ingår i: 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. - 9781509061815 ; 2017-May, s. 1252-1256
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Konferensbidrag (refereegranskat)abstract
- There is a need for an automatic Gleason scoring system that can be used for prostate cancer diagnosis. Today the diagnoses are determined by pathologists manually, which is both a complex and a time-consuming task. To reduce the pathologists' workload, but also to reduce variations between different pathologists, an automatic classification system would be of great use. Some previous works have aimed for this, but still more work needs to be done. It is probable that such a tool would benefit from having access to individually segmented, pathologically relevant objects from the images. Therefore, we have developed an algorithm for semantic segmentation of the microscopic images of H&E stained prostate tissue into Background, Stroma, Epithelial Cytoplasm and Nuclei. This algorithm is based on deep learning, or more specifically a convolutional neural network. The network design is inspired by architectures that previously have been proved successful in different applications. It consists of a contracting and an expanding part, which are symmetrical. We have reached an accuracy of 80 %, as measured by the mean of the intersection over union, for segmentation into four classes. Previous works have only investigated nuclei segmentation, and our network performed similar but for the more challenging task of four class segmentation.
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| 9. |
- Larsson, Martin, et al.
(författare)
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Registration and Merging Maps with Uncertainties
- 2018
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Ingår i: IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation. - 9781538656358
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Konferensbidrag (refereegranskat)abstract
- In this paper we address the problem of registering and merging two maps in two dimensions, given covariance estimates of the two maps. We show that if two maps are given in the same coordinate system, then the problem of merging them in a statistically optimal way can be formulated as a linear least squares problem, but if they are given in different coordinate systems as well the problem becomes highly non-linear and nonconvex. We show how we can relax the problem slightly in order to optimize over the registration (i.e. putting the two maps in the same coordinate system) and at the same time optimize over the merged map. The approach is based on finding all stationary points of the optimization problem and evaluating these to choose the global optimum. We show on synthetic data that in many cases the proposed approach gives better results than naively registering and merging the maps. We also show results on real data, where we merge maps given by time-of-arrival measurements, and in these cases simpler linear methods perform just a good as the proposed method.
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| 10. |
- Larsson, Viktor, et al.
(författare)
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Uncovering symmetries in polynomial systems
- 2016
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Ingår i: Computer Vision – ECCV 2016 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319464862 - 9783319464879 ; 9907, s. 252-267
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Konferensbidrag (refereegranskat)abstract
- In this paper we study symmetries in polynomial equation systems and how they can be integrated into the action matrix method. The main contribution is a generalization of the partial p-fold symmetry and we provide new theoretical insights as to why these methods work. We show several examples of how to use this symmetry to construct more compact polynomial solvers. As a second contribution we present a simple and automatic method for finding these symmetries for a given problem. Finally we show two examples where these symmetries occur in real applications.
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