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Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 1, Issue 1, March 2007

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Volume 1, Issue 1

March 2007

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Editorial: IET Computer Vision

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    • Extracting characters from real vehicle licence plates out-of-doors
      • Author(s): B.-F. Wu ; S.-P. Lin ; C.-C. Chiu
      • Source: IET Computer Vision, Volume 1, Issue 1, p. 2 –10
      • DOI:  10.1049/iet-cvi:20050132
      • Type: Article
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      • p. 2 –10 (9)
        Most applications of automatic vehicle identification systems are outdoors and are consumer-orientated. The recognition rate, the system reliability and the processing speed are very important. A system for extracting characters from licence plates has been presented. The system can successfully extract characters from licence plates in any part of a captured image without the need to consider the luminance of the surroundings or the size and inclination of the licence plate or the colour of the vehicle. Moreover, the processing time of this system is satisfactory for several applications. The proposed system depends only on the completeness of a character and so is tolerant of skewed plates and complex backgrounds. In a real parking lot, an extraction rate of 95.6% was obtained by applying the system to 228 vehicles.
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      Fast numerically stable computation of orthogonal Fourier–Mellin moments
      • Author(s): G.A. Papakostas ; Y.S. Boutalis ; D.A. Karras ; B.G. Mertzios
      • Source: IET Computer Vision, Volume 1, Issue 1, p. 11 –16
      • DOI:  10.1049/iet-cvi:20060130
      • Type: Article
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      • p. 11 –16 (6)
        An efficient algorithm for the computation of the orthogonal Fourier–Mellin moments (OFMMs) is presented. The proposed method computes the fractional parts of the orthogonal polynomials, which consist of fractional terms, recursively, by eliminating the number of factorial calculations. The recursive computation of the fractional terms makes the overall computation of the OFMMs a very fast procedure in comparison with the conventional direct method. Actually, the computational complexity of the proposed method is linear O(p) in multiplications, with p being the moment order, while the corresponding complexity of the direct method is O(p2). Moreover, this recursive algorithm has better numerical behaviour, as it arrives at an overflow situation much later than the original one and does not introduce any finite precision errors. These are the two major advantages of the algorithm introduced in the current work, establishing the computation of the OFMMs to a very high order as a quite easy and achievable task. Appropriate simulations on images of different sizes justify the superiority of the proposed algorithm over the conventional algorithm currently used.
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      Hierarchical active shape model with motion prediction for real-time tracking of non-rigid objects
      • Author(s): S.-W. Lee ; J. Kang ; J. Shin ; J. Paik
      • Source: IET Computer Vision, Volume 1, Issue 1, p. 17 –24
      • DOI:  10.1049/iet-cvi:20045243
      • Type: Article
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      • p. 17 –24 (8)
        Video tracking systems generally deal with non-rigid objects with various shapes and sizes. This often results in a poor match of an initial model with the actual input shape, and consequently causes the failure of tracking. The robustness of the active shape model (ASM) enables video tracking systems to deal with such unpredictable inputs. The iterative nature of the ASM, however, makes real-time implementation difficult. A novel ASM-based real-time tracking method with particular relevance to non-rigid objects is proposed. The proposed tracking system adopts a hierarchical approach to reduce computational loads and increase immunity to noise. In order to make the system operate in real-time, a novel prediction approach is proposed that significantly reduces the number of iterations. In the sequential images, the initial feature points have been estimated using a block-matching algorithm. Kalman filtering has also been applied for increasing accuracy of the motion estimation. The proposed hierarchical, prediction-based approach is proven to outperform the existing methods in the sense of both tracking performance and convergence speed.
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      3D model-based pose invariant face recognition from multiple views
      • Author(s): Q. Chen ; J. Yao ; W.K. Cham
      • Source: IET Computer Vision, Volume 1, Issue 1, p. 25 –34
      • DOI:  10.1049/iet-cvi:20060014
      • Type: Article
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      • p. 25 –34 (10)
        A 3D model-based pose invariant face recognition method that can recognise a human face from its multiple views is proposed. First, pose estimation and 3D face model adaptation are achieved by means of a three-layer linear iterative process. Frontal view face images are synthesised using the estimated 3D models and poses. Then the discriminant ‘waveletfaces’ are extracted from these synthesised frontal view images. Finally, corresponding nearest feature space classifier is implemented. Experimental results show that the proposed method can recognise faces under variable poses with good accuracy.
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      Consumer electronics control system based on hand gesture moment invariants
      • Author(s): P. Premaratne  and  Q. Nguyen
      • Source: IET Computer Vision, Volume 1, Issue 1, p. 35 –41
      • DOI:  10.1049/iet-cvi:20060198
      • Type: Article
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      • p. 35 –41 (7)
        Almost all consumer electronic equipment today uses remote controls for user interfaces. However, the variety of physical shapes and functional commands that each remote control features also raises numerous problems: the difficulties in locating the required remote control, the confusion with the button layout, the replacement issue and so on. The consumer electronics control system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for domestic appliances. Based on one unified set of hand gestures, this system interprets the user hand gestures into pre-defined commands to control one or many devices simultaneously. The system has been tested and verified under both incandescent and fluorescent lighting conditions. The experimental results are very encouraging as the system produces real-time responses and highly accurate recognition towards various gestures.
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