| 1. |
- Almgren, Karin, et al.
(författare)
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Role of fibre-fibre and fibre-matrix adhesion in stress transfer in composites made from resin-impregnated paper sheets.
- 2009
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Ingår i: International Journal of Adhesion and Adhesives. - : Elsevier BV. - 0143-7496 .- 1879-0127. ; 29:5, s. 551-557
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Tidskriftsartikel (refereegranskat)abstract
- Paper-reinforced plastics are gaining increased interest as packaging materials, where mechanical properties are of great importance. Strength and stress transfer in paper sheets are controlled by fibre-fibre bonds. In paper-reinforced plastics, where the sheet is impregnated with a polymer resin, other stress-transfer mechanisms may be more important. The influence of fibre-fibre bonds on the strength of paper-reinforced plastics was therefore investigated. Paper sheets with different degrees of fibre-fibre bonding were manufactured and used as reinforcement in a polymeric matrix. Image analysis tools were used to verify that the difference in the degree of fibre-fibre bonding had been preserved in the composite materials. Strength and stiffness of the composites were experimentally determined and showed no correlation to the degree of fibre-fibre bonding, in contrast to the behaviour of unimpregnated paper sheets. The degree of fibre-fibre bonding is therefore believed to have little importance in this type of material, where stress is mainly transferred through the fibre-matrix interface.
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| 2. |
- Andersson, Axel, et al.
(författare)
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End-to-end Multiple Instance Learning with Gradient Accumulation
- 2022
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Ingår i: 2022 IEEE International Conference on Big Data (Big Data). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665480451 - 9781665480468 ; , s. 2742-2746
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Konferensbidrag (refereegranskat)abstract
- Being able to learn on weakly labeled data and provide interpretability are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have become particularly popular for classification of histopathological images. Such image data usually come in the form of gigapixel-sized whole-slide-images (WSI) that are cropped into smaller patches (instances). However, the sheer volume of the data poses a practical big data challenge: All the instances from one WSI cannot fit the GPU memory of conventional deep-learning models. Existing solutions compromise training by relying on pre-trained models, strategic selection of instances, sub-sampling, or self-supervised pre-training. We propose a training strategy based on gradient accumulation that enables direct end-to-end training of ABMIL models without being limited by GPU memory. We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time and compare with the conventional memory-expensive baseline as well as a recent sampled-based approach. This memory-efficient approach, although slower, reaches performance indistinguishable from the memory-expensive baseline.
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| 6. |
- Bajic, Buda, et al.
(författare)
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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
- 2016
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Ingår i: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI). - : IEEE. - 9781479923496 - 9781479923502 ; , s. 123-127
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Konferensbidrag (refereegranskat)abstract
- Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.
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| 7. |
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| 8. |
- Bajic, Buda, et al.
(författare)
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Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps
- 2020
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Ingår i: IET Image Processing. - : INST ENGINEERING TECHNOLOGY-IET. - 1751-9659 .- 1751-9667. ; 14:6, s. 1201-1208
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Tidskriftsartikel (refereegranskat)abstract
- The authors propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state-of-the-art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, $\alpha $alpha LBP-maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.
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| 9. |
- Bajic, Buda, et al.
(författare)
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Restoration of images degraded by signal-dependent noise based on energy minimization : an empirical study
- 2016
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Ingår i: Journal of Electronic Imaging (JEI). - 1017-9909 .- 1560-229X. ; 25:4
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Tidskriftsartikel (refereegranskat)abstract
- Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson–Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.
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| 10. |
- Bajic, Buda, et al.
(författare)
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Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise
- 2016
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Ingår i: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA). - : IEEE. - 9781467389105
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Konferensbidrag (refereegranskat)abstract
- Single image super-resolution (SR) reconstructionaims to estimate a noise-free and blur-free high resolution imagefrom a single blurred and noisy lower resolution observation.Most existing SR reconstruction methods assume that noise in theimage is white Gaussian. Noise resulting from photon countingdevices, as commonly used in image acquisition, is, however,better modelled with a mixed Poisson-Gaussian distribution. Inthis study we propose a single image SR reconstruction methodbased on energy minimization for images degraded by mixedPoisson-Gaussian noise.We evaluate performance of the proposedmethod on synthetic images, for different levels of blur andnoise, and compare it with recent methods for non-Gaussiannoise. Analysis shows that the appropriate treatment of signaldependentnoise, provided by our proposed method, leads tosignificant improvement in reconstruction performance.
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