| 1. |
- Roland, P, et al.
(författare)
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A database generator for human brain imaging
- 2001
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Ingår i: TINS - Trends in Neurosciences. - 0166-2236 .- 1878-108X. ; 24:10, s. 562-564
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Tidskriftsartikel (refereegranskat)abstract
- Sharing scientific data containing complex information requires new concepts and new technology. NEUROGENERATOR is a database generator for the neuroimaging community. A database generator is a database that generates new databases. The scientists submit raw PET and fMRI data to NEUROGENERATOR, which then processes the data in a uniform way to create databases of homogenous data suitable for data sharing, met-analysis and modelling the human brain at the systems level. These databases are then distributed to the scientists.
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| 2. |
- Thottappillil, Rajeev, et al.
(författare)
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Response of Civilian Facilities to Intentional Electromagnetic Interference (IEMI), with Emphasis on the Swedish Railway Network
- 2005
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Konferensbidrag (refereegranskat)abstract
- Recently a research program was initiated in Sweden to investigate the possible susceptibility of IEMI to civilian networks, with emphasis on the Swedish railway network. The objective is to develop a methodology for investigating and protecting sensitive railway facilities against IEMI and to recommend ways in which EMI control in the railways could be improved for better defense against IEMI. Details of the research program and some preliminary results are presented in this paper.
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| 3. |
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| 4. |
- Almansa, Andrés, et al.
(författare)
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Enhancement of Fingerprint Images by Shape-Adapted Scale-Space Operators
- 1996
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Ingår i: Gaussian Scale-Space Theory. Part I. - Dordrecht : Springer Science+Business Media B.V.. - 9789401588027 ; , s. 21-30
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Bokkapitel (refereegranskat)abstract
- This work presents a novel technique for preprocessing fingerprint images. The method is based on the measurements of second moment descriptors and shape adaptation of scale-space operators with automatic scale selection (Lindeberg 1994). This procedure, which has been successfully used in the context of shape-from-texture and shape from disparity gradients, has several advantages when applied to fingerprint image enhancement, as observed by (Weickert 1995). For example, it is capable of joining interrupted ridges, and enforces continuity of their directional fields.In this work, these abovementioned general ideas are applied and extended in the following ways: Two methods for estimating local ridge width are explored and tuned to the problem of fingerprint enhancement. A ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model. This information is used for guiding a scale-selection mechanism, and for spreading the results of shape adaptation into noisy areas.The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. To a large extent, the scheme has the desirable property of joining interrupted lines without destroying essential singularities such as branching points. Thus, the result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.A detailed experimental evaluation is presented, including a comparison with other techniques. We propose that the techniques presented provide mechanisms of interest to developers of automatic fingerprint identification systems.
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| 5. |
- Almansa, A., et al.
(författare)
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Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection
- 2000
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Ingår i: IEEE Transactions on Image Processing. - : IEEE Signal Processing Society. - 1057-7149 .- 1941-0042. ; 9:12, s. 2027-2042
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Tidskriftsartikel (refereegranskat)abstract
- This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The Scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a Smoothed grey-level version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.
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| 6. |
- Auffarth, Benjamin, 1979-
(författare)
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Machine Learning Techniques with Specific Application to the Early Olfactory System
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below.We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches.We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results.The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space.In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful.In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant.
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| 7. |
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| 8. |
- Björkman, Eva, et al.
(författare)
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Evaluation of design options for the scale-space primal sketch analysis of brain activation images
- 2000
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Konferensbidrag (refereegranskat)abstract
- A key issue in brain imaging concerns how to detect the functionally activated regions from PET and fMRI images. In earlier work, it has been shown that the scale-space primal sketch provides a useful tool for such analysis [1]. The method includes presmoothing with different filter widths and automatic estimation of the spatial extent of the activated regions (blobs).The purpose is to present two modifications of the scale-space primal sketch, as well as a quantitative evaluation which shows that these modifications improve the performance, measured as the separation between blob descriptors extracted from PET images and from noise images. This separation is essential for future work of associating a statistical p-value with the scale-space blob descriptors.
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| 9. |
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| 10. |
- Bretzner, Lars, et al.
(författare)
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Feature tracking with automatic selection of spatial scales
- 1998
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- When observing a dynamic world, the size of image structures may vary over nada. This article emphasizes the need for including explicit mechanisms for automatic scale selection in feature tracking algorithms in order to: (i) adapt the local scale of processing to the local image structure, and (ii) adapt to the size variations that may occur over time. The problems of corner detection and blob detection are treated in detail, and a combined framework for feature tracking is presented in which the image features at every time moment are detected at locally determined and automatically selected nadaes. A useful property of the scale selection method is that the scale levels selected in the feature detection step reflect the spatial extent of the image structures. Thereby, the integrated tracking algorithm has the ability to adapt to spatial as well as temporal size variations, and can in this way overcome some of the inherent limitations of exposing fixed-scale tracking methods to image sequences in which the size variations are large. In the composed tracking procedure, the scale information is used for two additional major purposes: (i) for defining local regions of interest for searching for matching candidates as well as setting the window size for correlation when evaluating matching candidates, and (ii) stability over time of the scale and significance descriptors produced by the scale selection procedure are used for formulating a multi-cue similarity measure for matching. Experiments on real-world sequences are presented showing the performance of the algorithm when applied to (individual) tracking of corners and blobs. Specifically, comparisons with fixed-scale tracking methods are included as well as illustrations of the increase in performance obtained by using multiple cues in the feature matching step.
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