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image of Volume 1, Issue 3-4
Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 1, Issue 3-4, December 2007

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Volume 1, Issue 3

December 2007

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    • Design of a computer vision system for a differential spraying operation in precision agriculture using Hebbian learning
      • Author(s): G. Pajares ; A. Tellaeche ; X.-P. BurgosArtizzu ; A. Ribeiro
      • Source: IET Computer Vision, Volume 1, Issue 3, p. 93 –99
      • DOI:  10.1049/iet-cvi:20070028
      • Type: Article
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      • p. 93 –99 (7)
        One objective in precision agriculture is to minimise the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. An automatic computer vision system for the detection and differential spraying of weeds in crop fields is discussed. The system involves an image segmentation approach and a decision maker. The first is designed as a sequence of image processing techniques and the second is a decision maker based on the Hebbian learning paradigm under the self-organising criterion. The combination of both and the use of the second are the main findings. The method is compared favourably with other existing strategies achieving a suitable performance.
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      Evaluation of point localisation and similarity fusion methods for Gabor jet-based face verification
      • Author(s): D. González-Jiménez ; E. Argones-Rúa ; J.L. Alba-Castro ; J. Kittler
      • Source: IET Computer Vision, Volume 1, Issue 3, p. 101 –112
      • DOI:  10.1049/iet-cvi:20070024
      • Type: Article
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      • p. 101 –112 (12)
        A comparative evaluation of two problems addressed in local Gabor feature-based face recognition is presented: localisation of points for feature extraction, and fusion of Gabor-based local similarity measures. For the former problem, three different point configurations are compared: a face-like mesh, a (rigid) rectangular grid and a shape-driven mesh. Regarding the problem of combining local Gabor similarities for better discrimination between subjects, several state-of-the-art techniques are evaluated: support vector machines, boosting of multilayer perceptrons, sequential floating forward search, a variant of the classical linear discriminant analysis, best individual feature selection, and a closely related technique that has been recently proposed. All the experiments were carried out in configurations I and II of the XM2VTS database.
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      Infrared imaging of hand vein patterns for biometric purposes
      • Author(s): L. Wang ; G. Leedham ; S.-Y. Cho
      • Source: IET Computer Vision, Volume 1, Issue 3, p. 113 –122
      • DOI:  10.1049/iet-cvi:20070009
      • Type: Article
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      • p. 113 –122 (10)
        A novel non-invasive imaging technique to image the vein patterns in various parts of the hand for biometric purposes is evaluated. Two imaging methods are investigated: far-infrared (FIR) thermography and near-infrared (NIR) imaging. Experiments involving data acquisition from various parts of the hand, including the back of the hand, palm and wrist, were carried out using both imaging techniques. Analysis of the data collected shows that FIR thermography is less successful at capturing veins in the palm and wrist. FIR thermography can capture the large veins in the back of the hand, but it is sensitive to ambient temperature and humidity conditions as well as human body temperature. NIR imaging produces good quality images when capturing veins in the back of the hand, palm and wrist. NIR imaging is also more tolerant to changes in the environment and body condition but faces the problem of pattern corruption because of visible skin features being mistaken for veins. This corruption is not present in FIR imaging. An initial biometric system is investigated to test both FIR and NIR images for biometric purposes. The results show all the subjects were correctly identified, which indicates vein pattern biometrics with infrared imaging is a potentially useful biometric.
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