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Computer Vision, IET

Issue 4 • Date December 2008

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Displaying Results 1 - 6 of 6
  • Editorial Selected papers from the Digital Image Computing Technology and Applications conference 2007 (DICTA 2007)

    Page(s): 191 - 192
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (74 KB)  

    This editorial is an introduction to the special issue. A brief overview is given of the papers presented. View full abstract»

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  • A quadratic programming approach to image labelling

    Page(s): 193 - 207
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (800 KB)  

    Image labelling tasks are usually formulated within the framework of discrete Markov random fields where the optimal labels are recovered by extremising a discrete energy function. The authors present an alternative continuous relaxation approach to image labelling, which makes use of a quadratic cost function over the class labels. The cost function to be minimised is convex and its discrete version is equivalent up to a constant additive factor to the target function used in discrete MRF approaches. Moreover, its corresponding Hessian matrix is given by the graph Laplacian of the adjacency matrix. Therefore the optimisation of the cost function is governed by the pairwise interactions between pixels in the local neighbourhood. This leads to a sparse Hessian matrix for which the global minimum of the continuous relaxation problem can be efficiently found by solving a system of linear equations using the Cholesky factorisation. The authors elaborate on the links between the method and other techniques elsewhere in the literature and provide results on synthetic and real-world imagery. The authors also provide a comparison with competing approaches. View full abstract»

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  • Local 3D structure recognition in range images

    Page(s): 208 - 217
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (929 KB)  

    A feature detector and a feature descriptor are presented, which are applicable to 3D range data. The feature detector is used to identify locations in the range data at which the feature descriptor is applied. The feature descriptor, or feature transform, calculates a signature for each identified location on the basis of local shape information. The approach used in both the feature detector and the descriptor is motivated by the success of the scale invariant feature transform and speeded up robust features approaches in the 2D case. Using synthetic data, the authors evaluate the repeatability of the detector and robustness of the descriptor to global transformations and image noise. The complete system is then applied to the problem of automatic detection of repeated structure in real range images. View full abstract»

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  • Dimensionality reduction for more stable vision parameter estimation

    Page(s): 218 - 227
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (371 KB)  

    The problem of estimating parameters from data is considered for a class of multi-objective models of importance in computer vision. One previous approach for solving the problem is via the fundamental numerical scheme (FNS). Here, a more stable version of FNS is developed, with better convergence properties than the original version. The improvement in performance is achieved by reducing the original estimation problem to a couple of problems of lower dimension. By way of example, the new algorithm is applied to the problem of estimating the trifocal tensor relating three views of a scene. Experiments carried out with both synthetic and real images reveal the new estimator to be more stable compared to the original FNS method, and commensurate in accuracy with, but faster than, the gold standard maximum likelihood estimator. View full abstract»

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  • In vivo assessment of alveolar morphology using a flexible catheter-based confocal microscope

    Page(s): 228 - 235
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1325 KB)  

    Investigating the structure and function of alveoli in vivo is crucial for understanding the normal and diseased lungs. The authors image the alveoli of mice in vivo using a custom fibre optic catheter-based laser scanning confocal microscope. Images obtained using this system are analysed with an automated software application for alveolar size, wall thickness and number. Results show that direct dynamic visualisation of alveoli and surrounding structures is possible in vivo, with high resolution. Early results indicate high heterogeneity in alveolar structure in vivo, as opposed to an ordered uniform structure. Using the techniques presented here, there is great promise for advancing our knowledge of the functional unit of the lung, the alveolus, for alveolar mechanics, cell traffic and three-dimensional structural visualisation. View full abstract»

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  • Performance evaluation of local features in human classification and detection

    Page(s): 236 - 246
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1173 KB)  

    Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and 'Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection. View full abstract»

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Aims & Scope

IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in Computer Vision.

Full Aims & Scope