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image of Volume 4, Issue 4
Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 4, Issue 4, December 2010

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Volume 4, Issue 4

December 2010

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    • New multi-resolution image stitching with local and global alignment
      • Author(s): X. Fang ; B. Luo ; H. Zhao ; J. Tang ; S. Zhai
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 231 –246
      • DOI:  10.1049/iet-cvi.2009.0025
      • Type: Article
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      • p. 231 –246 (16)
        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 these problems. The first problem is solved by introducing an additional motion refinement strategy, which consists of the low-contrast filter and RANSAC. The former removes flatly textured surface pixels and thus eliminates the falsely matched features. The latter removes outliers and finds a robust initial motion for the next layer. The second problem is resolved by a new iteratively local registration method, which calibrates the current camera parameters based on those from previous image with robust non-linear optimisation methods. It improves the convergence efficiency and eliminates error minimisation. For the last problem, the authors introduce a five-parameter bundle adjustment method based on the axis-angle decomposition of the rotation matrix. Comparing with existing bundle adjustment methods, this method is more stable because of an accurate and simple rotation decomposition. The authors show the efficiency of the method with qualitative and quantitative experiments.
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      Automatic identification of landmarks in digital images
      • Author(s): S. Palaniswamy ; N.A. Thacker ; C.P. Klingenberg
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 247 –260
      • DOI:  10.1049/iet-cvi.2009.0014
      • Type: Article
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      • p. 247 –260 (14)
        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 is an important and labour-intensive part of any such analysis. This process is time-consuming, and quite often the research questions are dependent on the duration of obtaining these data. The authors show that a single training image with its landmark co-ordinates is enough to independently estimate the landmarks of any individual within a particular data set. The reliability and accuracy of the method can be further enhanced by using multiple training images. The precision, repeatability and robustness of the algorithm have been evaluated. It is shown in this study that the method is sufficiently accurate to replace the manual identification of landmarks. The generic nature and intrinsic capability of the feature recognition process enables this method to be easily incorporated into other recognition tasks.
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      Efficient eye detection method based on grey intensity variance and independent components analysis
      • Author(s): M. Hassaballah ; T. Kanazawa ; S. Ido ; S. Ido
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 261 –271
      • DOI:  10.1049/iet-cvi.2009.0097
      • Type: Article
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      • p. 261 –271 (11)
        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, ICA applied on a patch image is used as a method to extract the eye which is the most salient and stable feature among all the facial features. The variance of grey intensity in the eye region and ICA are combined together to detect rough eye window. The ICA basis images are computed using the FastICA algorithm; that computes independent components by maximising non-Gaussianity of the whitened data distribution using a kurtosis maximisation process. After detecting rough eye window, intensity information is used to localise eye centre point. The proposed method is evaluated on different databases XM2VTS, BioID and FERET and experimental results demonstrate improved performance over the existing methods. In addition, a high detection rate of 93.3% can be achieved on 600 images with glasses. A comparison between the proposed method and the most recent published methods which focus on eye window detection is also reported.
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      Study on the performance of moments as invariant descriptors for practical face recognition systems
      • Author(s): N. Farajzadeh ; K. Faez ; G. Pan
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 272 –285
      • DOI:  10.1049/iet-cvi.2009.0140
      • Type: Article
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      • p. 272 –285 (14)
        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 Fourier–Mellin, for the recognition of human faces. In this study, the authors have concerned with both values and orders of moments in the sense of accuracy and efficiency. Two public large face databases, FERET and CAS-PEAL-R1, have been exploited in the experiments. The authors have also employed two typical classifiers, radial basis function neural network and support vector machine, in order to ensure the reliability and consistency of the results from the classification point of view. The extensive experiments in variations of illumination, expression, aging and different accessories have shown that Zernike moments achieve the best overall performance in terms of both classification accuracy and execution time.
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      Why not use the Levenberg–Marquardt method for fundamental matrix estimation?
      • Author(s): P. Chen
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 286 –294
      • DOI:  10.1049/iet-cvi.2009.0146
      • Type: Article
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      • p. 286 –294 (9)
        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 fundamental matrix; however, for some unknown reason, it was unfairly treated in the literature so that it was reported to have inferior performance. In this study, the authors concentrate on the application of the LM method for fundamental matrix estimation. Particularly, a new Gauss–Newton approximation of the Hessian matrix is presented, when the Sampson error is minimised; and the rank-two constraint of a fundamental matrix is automatically enforced by revitalising a particular parameterisation. An evaluation of algorithms is presented, showing the advantage of these two techniques.
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      Object tracking using AM-FM image features
      • Author(s): R. Senthil Prakash  and  R. Aravind
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 295 –305
      • DOI:  10.1049/iet-cvi.2009.0027
      • Type: Article
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      • p. 295 –305 (11)
        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 appearance model. The multiple object tracking algorithm, which is a logical extension of single object tracking, can handle varying number of interacting objects. The performance of both single and multiple object tracking is illustrated on real-world videos.
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      Nearest-neighbour ensembles in lasso feature subspaces
      • Author(s): X. He ; P. Beauseroy ; A. Smolarz
      • Source: IET Computer Vision, Volume 4, Issue 4, p. 306 –319
      • DOI:  10.1049/iet-cvi.2009.0056
      • Type: Article
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      • p. 306 –319 (14)
        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-based multiple feature subsets selection cycle, which tries to find a number of relevant and diverse feature subspaces; the second is an ensemble-based decision system that intends to preserve the classification performance in case of abrupt changes in the representation space. Experimental results on the two-class textured image segmentation problem prove the effectiveness of the proposed classification method.
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