561 research outputs found

    Affine Subspace Representation for Feature Description

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    This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio

    Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

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    Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in Biomedical Image Analysis. MICCAI 2017. Quebec Cit

    Analysis of brain activation patterns using a 3-D scale-space primal sketch

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    http://www.rosenland-lippe.de/Rosenland-13.pdf (2012) Jürgen Hartmann hat den ergiebigen Quellenbestand für die lippische Regionalgeschichte ausgewertet

    Segmentation of Loops from Coronal EUV Images

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    We present a procedure which extracts bright loop features from solar EUV images. In terms of image intensities, these features are elongated ridge-like intensity maxima. To discriminate the maxima, we need information about the spatial derivatives of the image intensity. Commonly, the derivative estimates are strongly affected by image noise. We therefore use a regularized estimation of the derivative which is then used to interpolate a discrete vector field of ridge points ``ridgels'' which are positioned on the ridge center and have the intrinsic orientation of the local ridge direction. A scheme is proposed to connect ridgels to smooth, spline-represented curves which fit the observed loops. Finally, a half-automated user interface allows one to merge or split, eliminate or select loop fits obtained form the above procedure. In this paper we apply our tool to one of the first EUV images observed by the SECCHI instrument onboard the recently launched STEREO spacecraft. We compare the extracted loops with projected field lines computed from almost-simultaneously-taken magnetograms measured by the SOHO/MDI Doppler imager. The field lines were calculated using a linear force-free field model. This comparison allows one to verify faint and spurious loop connections produced by our segmentation tool and it also helps to prove the quality of the magnetic-field model where well-identified loop structures comply with field-line projections. We also discuss further potential applications of our tool such as loop oscillations and stereoscopy.Comment: 13 pages, 9 figures, Solar Physics, online firs

    A ‘quiet revolution’? The impact of Training Schools on initial teacher training partnerships

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    This paper discusses the impact on initial teacher training of a new policy initiative in England: the introduction of Training Schools. First, the Training School project is set in context by exploring the evolution of a partnership approach to initial teacher training in England. Ways in which Training Schools represent a break with established practice are considered together with their implications for the dominant mode of partnership led by higher education institutions (HEIs). The capacity of Training Schools to achieve their own policy objectives is examined, especially their efficacy as a strategy for managing innovation and the dissemination of innovation. The paper ends by focusing on a particular Training School project which has adopted an unusual approach to its work and enquires whether this alternative approach could offer a more profitable way forward. During the course of the paper, five different models of partnership are considered: collaborative, complementary, HEI-led, school-led and partnership within a partnership

    MDL-Based Analysis of Time Series at Multiple Time-Scales

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