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IET Computer Vision

Issue 4 • Date Dec. 2010

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Displaying Results 1 - 7 of 7
  • New multi-resolution image stitching with local and global alignment

    Publication Year: 2010, Page(s):231 - 246
    Cited by:  Papers (3)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1125 KB)

    Three main problems affect the alignment quality of existing studies on multi-resolution image stitching: (i) the initial motion obtained is sometimes incorrect; (ii) the local motion is hard to be estimated and (iii) the widely used global bundle adjustment is difficult to converge. The authors propose a new multi-resolution image mosaic method that combines three corresponding tactics to solve t... View full abstract»

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  • Automatic identification of landmarks in digital images

    Publication Year: 2010, Page(s):247 - 260
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (635 KB)

    The authors present an automated system for feature recognition in digital images. Morphometric landmarks are points that can be defined in all specimens and located precisely. They are widely used in shape analysis and a typical shape analysis study involves several hundred digital images. Presently, the extraction of landmarks is usually done manually and the process of identifying the landmarks... View full abstract»

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  • Efficient eye detection method based on grey intensity variance and independent components analysis

    Publication Year: 2010, Page(s):261 - 271
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (792 KB)

    Detection of facial features such as eye, nose and mouth in the human face images is important for many applications like face identification or recognition systems. Independent components analysis (ICA) is an unsupervised learning method which decorrelates the higher-order statistics in addition to the second-order moments. Recently, it is used as a technique for face recognition. In this study, ... View full abstract»

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  • Study on the performance of moments as invariant descriptors for practical face recognition systems

    Publication Year: 2010, Page(s):272 - 285
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (742 KB)

    The performance of pattern recognition systems that use statistical features depends on a specific feature extraction technique. This technique is used to represent an image by a set of features and to reduce the dimension of the image space by removing redundant data. This study investigates a variety of moment-based feature extraction techniques, including Zernike, pseudo Zernike and orthogonal ... View full abstract»

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  • Why not use the levenberg-marquardt method for fundamental matrix estimation?

    Publication Year: 2010, Page(s):286 - 294
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (356 KB)

    The estimation of a fundamental matrix between two views is of great interest for a number of computer vision and robotics tasks. There exist well-known algorithms for this problem: such as normalised eight-point algorithm, fundamental numerical scheme (FNS), extended FNS (EFNS), and heteroscedastic errors-in-variable (HEIV). The Levenberg-Marquardt (LM) method can also be employed to estimate a f... View full abstract»

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  • Object tracking using AM-FM image features

    Publication Year: 2010, Page(s):295 - 305
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1117 KB)

    AM-FM models analyse an image in terms of amplitude (AM) and frequency modulated (FM) sinusoids. In this study, the authors present detection and tracking of single and multiple objects in video sequences using AM-FM features. The authors use the particle filtering framework for estimating the motion parameters. The single object tracking algorithm uses an affine motion model and a subspace-based ... View full abstract»

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  • Nearest-neighbour ensembles in lasso feature subspaces

    Publication Year: 2010, Page(s):306 - 319
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1000 KB)

    The least absolute shrinkage and selection operator (lasso) is a promising feature selection technique. However, it has traditionally not been a focus of research in ensemble classification methods. In this study, the authors propose a robust classification algorithm that makes use of an ensemble of classifiers in lasso feature subspaces. The algorithm consists of two stages: the first is a lasso-... 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

Meet Our Editors

Editor-in-Chief
Majid Mirmehdi
University of Bristol
UK

Publisher
Editorial Assistant
IET Research Journals
Michael Faraday House
Six Hills Way
Stevenage  SG1 2AY  SG1 2AY  United Kingdom
iet_cvi@theiet.org