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| 2. |
- Andersson, Pontus, et al.
(författare)
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FLIP: A Difference Evaluator for Alternating Images
- 2020
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Ingår i: Proceedings of the ACM in Computer Graphics and Interactive Techniques. - : Association for Computing Machinery (ACM). - 2577-6193. ; 3:2, s. 1-23
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Tidskriftsartikel (refereegranskat)abstract
- Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have visually inspected over a thousand image pairs that were either retrieved from image databases or generated in-house. We also present results of a user study which indicate that our method performs substantially better, on average, than the other algorithms. To facilitate the use of FLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch.
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| 3. |
- Arvidsson, Ida, et al.
(författare)
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Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera
- 2023
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Ingår i: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 30:1, s. 116-126
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. Results: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). Conclusion: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.
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| 4. |
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| 5. |
- Berg, Axel, et al.
(författare)
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Extending GCC-PHAT using Shift Equivariant Neural Networks
- 2022
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Ingår i: Proceedings of the Annual Conference of the International Speech Communication Association 2022. ; , s. 1791-1795
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Konferensbidrag (refereegranskat)abstract
- Speaker localization using microphone arrays depends on accurate time delay estimation techniques. For decades, methods based on the generalized cross correlation with phase transform (GCC-PHAT) have been widely adopted for this purpose. Recently, the GCC-PHAT has also been used to provide input features to neural networks in order to remove the effects of noise and reverberation, but at the cost of losing theoretical guarantees in noise-free conditions. We propose a novel approach to extending the GCC-PHAT, where the received signals are filtered using a shift equivariant neural network that preserves the timing information contained in the signals. By extensive experiments we show that our model consistently reduces the error of the GCC-PHAT in adverse environments, with guarantees of exact time delay recovery in ideal conditions.
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| 6. |
- Blomqvist, Christopher, et al.
(författare)
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Joint Handwritten Text Recognition and Word Classification for Tabular Information Extraction
- 2022
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Ingår i: 2022 26th International Conference on Pattern Recognition (ICPR). - 9781665490634 - 9781665490627 ; , s. 1564-1570
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a system for extracting tabular information from loosely structured handwritten documents. The system consists of three parts, (i) a u-net like CNN-based method for text detection and segmentation, (ii) a new attention-based method for simultaneous text recognition and classification of word-parts, and (iii) a method for matching the word parts into a tabular structure for each entry. A key contribution is the observation that the new attention-based recognition and classification module makes it possible for improved spatial analysis of the tabular information. The method is evaluated on a unique historical document: The Swedish Wealth Tax of 1571, consisting of 11,453 pages of hand-written tax records. The evaluation shows that the system provides a significant improvement to the state-of-the-art to the problem of tabular extraction from loosely structured historical documents.
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| 7. |
- Blomqvist, Christopher, et al.
(författare)
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Reading the ransom: Methodological advancements in extracting the Swedish Wealth Tax of 1571
- 2023
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Ingår i: Explorations in Economic History. - : Elsevier BV. - 0014-4983. ; 87
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Tidskriftsartikel (refereegranskat)abstract
- We describe a deep learning method to read hand-written records from the 16th century. The method consists of a combination of a segmentation module and a Handwritten Text Recognition (HTR) module. The transformer-based HTR module exploits both language and image features in reading, classifying and extracting the position of each word on the page. The method is demonstrated on a unique historical document: The Swedish Wealth Tax of 1571. Results suggest that the segmentation module performs significantly better than the lay-out analysis implemented in state-of-the art programs, enabling us to trace many more text blocks correctly on each page. The HTR module has a low character error rate (CER), in addition to being able to classify words and help organize them into tabular formats. By demonstrating an automated process to transform loosely structured handwritten information from the 16th century into organized tables, our method should interest economic historians seeking to digitize and organize quantitative material from pre-industrial periods.
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| 8. |
- Ding, Yaqing, et al.
(författare)
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Revisiting the P3P Problem
- 2023
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Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 9798350301298 ; , s. 4872-4880
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Konferensbidrag (refereegranskat)abstract
- One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2D-to-3D correspondences. Since these solvers form a critical component of many vision systems (e.g. in localization and Structure-from-Motion), there have been significant effort in developing faster and more stable algorithms. While the current state-of-the-art solvers are both extremely fast and stable, there still exist configurations where they break down. In this paper we algebraically formulate the problem as finding the intersection of two conics. With this formulation we are able to analytically characterize the real roots of the polynomial system and employ a tailored solution strategy for each problem instance. The result is a fast and stable solver, that is able to correctly solve cases where competing methods might fail. Our experimental evaluation shows that we outperform the current state-of-the-art methods both in terms of speed and success rate.
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| 9. |
- Ebelin, Pontus, et al.
(författare)
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Estimates of Temporal Edge Detection Filters in Human Vision
- 2024
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Ingår i: ACM Transactions on Applied Perception. - 1544-3558. ; 21:2
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Tidskriftsartikel (refereegranskat)abstract
- Edge detection is an important process in human visual processing. However, as far as we know, few attempts have been made to map the temporal edge detection filters in human vision. To that end, we devised a user study and collected data from which we derived estimates of human temporal edge detection filters based on three different models, including the derivative of the infinite symmetric exponential function and temporal contrast sensitivity function. We analyze our findings using several different methods, including extending the filter to higher frequencies than were shown during the experiment. In addition, we show a proof of concept that our filter may be used in spatiotemporal image quality metrics by incorporating it into a flicker detection pipeline.
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| 10. |
- Ebelin, Pontus, et al.
(författare)
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Luminance-Preserving and Temporally Stable Daltonization
- 2023
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Ingår i: Eurographics 2023 - Short Papers. - 9783038682097 ; , s. 45-49
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Konferensbidrag (refereegranskat)abstract
- We propose a novel, real-time algorithm for recoloring images to improve the experience for a color vision deficient observer. The output is temporally stable and preserves luminance, the most important visual cue. It runs in 0.2 ms per frame on a GPU.
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