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| 2. |
- Batstone, Kenneth John, et al.
(författare)
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Robust Time-of-Arrival Self Calibration with Missing Data and Outliers
- 2016
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Ingår i: 2016 24th European Signal Processing Conference (EUSIPCO). - 9780992862657 ; , s. 2370-2374
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating receiver-sender node positionsfrom measured receiver-sender distances is a key issue indifferent applications such as microphone array calibration, radioantenna array calibration, mapping and positioning using ultrawidebandand mapping and positioning using round-trip-timemeasurements between mobile phones and Wi-Fi-units. Thanks torecent research in this area we have an increased understandingof the geometry of this problem. In this paper, we study theproblem of missing information and the presence of outliers inthe data. We propose a novel hypothesis and test frameworkthat efficiently finds initial estimates of the unknown parametersand combine such methods with optimization techniques toobtain accurate and robust systems. The proposed systems areevaluated against current state-of-the-art methods on a large setof benchmark tests. This is evaluated further on Wi-Fi roundtriptime and ultra-wideband measurements to give a realisticexample of self calibration for indoor localization.
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| 3. |
- Batstone, Kenneth John, et al.
(författare)
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Towards Real-time Time-of-Arrival Self-Calibration using Ultra-Wideband Anchors
- 2017
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Ingår i: International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017.
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Konferensbidrag (refereegranskat)abstract
- Indoor localisation is a currently a key issue, from robotics to the Internet of Things. With hardware advancements making Ultra-Wideband devices more accurate and low powered (potentially even passive), this unlocks the potential of having such devices in common place around factories and homes, enabling an alternative method of navigation. Therefore, anchor calibration indoors becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we study the possibility for sequentially gathering Ultra-Wideband Time-of-Arrival measurements and using previously studied robust solvers, merge solutions together in order to calculate anchor positions in 3D in real-time. Here it is assumed that there is no prior knowledge of the anchor positions. This is then validated using Ultra-Wideband Time-of-Arrival data gathered by a Bitcraze Crazyflie quadcopter in 2D motion, 3D motion and full flight.
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| 4. |
- Berg, Axel, et al.
(författare)
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THE LU SYSTEM FOR DCASE 2024 SOUND EVENT LOCALIZATION AND DETECTION CHALLENGE
- 2024
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This technical report gives an overview of our submission to task 3 of the DCASE 2024 challenge. We present a sound event localization and detection (SELD) system using input features based on trainable neural generalized cross-correlations with phase transform (NGCC-PHAT). With these features together with spectrograms as input to a Transformer-based network, we achieve significant improvements over the baseline method. In addition, we also present an audio-visual version of our system, where distance predictions are updated using depth maps from the panorama video frames.
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| 5. |
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| 6. |
- Jiang, Fangyuan, et al.
(författare)
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On the Minimal Problems of Low-Rank Matrix Factorization
- 2015
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Ingår i: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. - 9781467369633 ; , s. 2549-2557
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Konferensbidrag (refereegranskat)abstract
- Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. However, very little attention has been drawn to minimal cases for this problem or to using the minimal configuration of observations to find the solution. Minimal problems are useful when either outliers are present or the observation matrix is sparse. In this paper, we first give some theoretical insights on how to generate all the minimal problems of a given size using Laman graph theory. We then propose a new parametrization and a building-block scheme to solve these minimal problems by extending the solution from a small sized minimal problem. We test our solvers on synthetic data as well as real data with outliers or a large portion of missing data and show that our method can handle the cases when other iterative methods, based on convex relaxation, fail.
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| 7. |
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| 8. |
- Kjellberg, Tobias, et al.
(författare)
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Tracking the Motion of Box Jellyfish
- 2014
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Konferensbidrag (refereegranskat)abstract
- In this paper we investigate a system for tracking the motion of box jellyfish tripedalia cystophora in a special test setup. The goal is to measure the motor response of the animal given certain visual stimuli. The approach is based on tracking the special sensory structures – the rhopalia – of the box jellyfish from high-speed video sequences. We have focused on a realtime system with simple building blocks in our system. However, using a combination of simple intensity based detection and model based tracking we achieve promising tracking results with up to 95% accuracy.
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| 9. |
- Kuang, Yubin, et al.
(författare)
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Optimizing Visual Vocabularies Using Soft Assignment Entropies
- 2011
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Ingår i: Lecture Notes in Computer Science. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 0302-9743 .- 1611-3349. - 9783642192814 - 9783642192821 ; 6495, s. 255-268
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Konferensbidrag (refereegranskat)abstract
- The state of the art for large database object retrieval in images is based on quantizing descriptors of interest points into visual words. High similarity between matching image representations (as bags of words) is based upon the assumption that matched points in the two images end up in similar words in hard assignment or in similar representations in soft assignment techniques. In this paper we study how ground truth correspondences can be used to generate better visual vocabularies. Matching of image patches can be done e.g. using deformable models or from estimating 3D geometry. For optimization of the vocabulary, we propose minimizing the entropies of soft assignment of points. We base our clustering on hierarchical k-splits. The results from our entropy based clustering are compared with hierarchical k-means. The vocabularies have been tested on real data with decreased entropy and increased true positive rate, as well as better retrieval performance.
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| 10. |
- Kuang, Yubin, et al.
(författare)
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Revisiting Trifocal Tensor Estimation Using Lines
- 2014
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Ingår i: Pattern Recognition (ICPR), 2014 22nd International Conference on. - 1051-4651. ; , s. 2419-2423
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Konferensbidrag (refereegranskat)abstract
- In this paper, we revisit the problem of estimating the trifocal tensor from image line measurements. With measurements of corresponding lines in three views, a linear method [1] requiring 13 lines was developed to estimate the trifocal tensor from which projective reconstruction of the scene is made possible. By further utilizing the nonlinear constraints on the trifocal tensor, we propose several new linear solvers that require fewer number of lines (10,11,12) than the previous linear method. We use methods based on algebraic geometry to incorporate the non-linear constraints in the estimation. We demonstrate the performance of the proposed solvers on synthetic data. We also test the solvers on real images and obtain promising results.
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