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Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 1, Issue 2, June 2007

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Volume 1, Issue 2

June 2007

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    • Catadioptric single-shot rangefinder for textured map building in robot navigation
      • Author(s): R. Orghidan ; E.M. Mouaddib ; J. Salvi ; J.J. Serrano
      • Source: IET Computer Vision, Volume 1, Issue 2, p. 43 –53
      • DOI:  10.1049/iet-cvi:20065003
      • Type: Article
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      • p. 43 –53 (11)
        The paper focuses on combining the advantages of both omnidirectional vision and structured light in order to obtain panoramic depth information for generating a complete 3D visual model of the surroundings of a mobile robot. The model is completed by mapping the imaged texture onto the objects through the central view point. This process is called single-shot scene reconstruction because a visual model of the scene is obtained from a single omnidirectional image. Several such reconstructions are then registered to build a 3D map of the scene while the robot is moving. The authors call this process range scan registration modelling. A mobile platform provided with a catadioptric camera coupled with an omnidirectional structured light projector was used for this purpose. Sensor calibration, image processing and experimental results are included.
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      Integrating CFD modelling, neural networks and remote sensing: controlled prediction of chlorophyll-a concentration in the Mejillones of South Bay
      • Author(s): G.A. Alvarez ; R.A. Salinas ; T.J. Malthus
      • Source: IET Computer Vision, Volume 1, Issue 2, p. 55 –65
      • DOI:  10.1049/iet-cvi:20060168
      • Type: Article
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      • p. 55 –65 (11)
        The geomorphologic characteristics of the Mejillones of South Bay (The Bay) in northern Chile influence the oceanic dynamics inside the Bay. The presence of the Günther tropical undercurrent brings tropical water to the region, whereas the Humboldt polar current brings cool water to the same area. The combination of geomorphology and currents generates conditions responsible for producing a powerful upwelling phenomenon in the Bay. A novel approach that integrates both remotely sensed data and in situ information, including oceanic variables and bathymetry, is proposed, which when combined with a computational fluid dynamic (CFD) model predicts patterns of surface distributions of variables such as chlorophyll-a concentration and temperature. The proposed system uniquely generates a controlled map via the application of digital image processing and an artificial neural network.
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      Derivation of blur-invariant features using orthogonal Legendre moments
      • Author(s): C.-Y. Wee  and  R. Paramesran
      • Source: IET Computer Vision, Volume 1, Issue 2, p. 66 –77
      • DOI:  10.1049/iet-cvi:20070016
      • Type: Article
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      • p. 66 –77 (12)
        A novel solution based on the orthogonal Legendre moments to derive a blur-invariant feature set for degraded image recognition systems is proposed. The image is degraded because of the introduction of the point spread function (PSF), which is caused by the imperfect imaging system and the environment. The moment relation between the PSF and discrete-space image has been developed using a discrete, finite-extent 2D convolution model. The derivation of a blur-invariant feature set is made possible because the Legendre central moments are not equally affected by the PSF during the imaging process. Hence, the formulation of a novel technique to derive the blur-invariant feature set using the least-affected Legendre central moments has been proposed. It is made less vulnerable to the additive random noise by setting the order (p+q) of the invariant features to be small. The advantages of the proposed blur-invariant feature set in terms of invariant to PSF, discriminability and noise stability are validated through experiments.
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      Statistical multiple light source detection
      • Author(s): C.-S. Bouganis  and  M. Brookes
      • Source: IET Computer Vision, Volume 1, Issue 2, p. 79 –91
      • DOI:  10.1049/iet-cvi:20065001
      • Type: Article
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      • p. 79 –91 (13)
        Multiple light source detection has many applications in image synthesis and augmented reality. Current techniques can provide accurate results but have limited applicability in real-life scenarios where interaction with the scene is not possible. The authors provide a statistical framework for multiple light source detection that relies on the common features of objects belonging to a particular class and illustrate it using the class of human faces. Experiments with real data demonstrate that a light distribution with up to three light sources can be detected within 13° mean error. Application of the proposed framework to the problem of 3D reconstruction from multiple images under arbitrary lighting demonstrates the effectiveness of the framework compared with current techniques.
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