Advanced Concepts for Intelligent Vision Systems: 11th by Chrysi Papalazarou, Peter M. J. Rongen, Peter H. N. de With

By Chrysi Papalazarou, Peter M. J. Rongen, Peter H. N. de With (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)

This e-book constitutes the refereed complaints of the eleventh foreign convention on complicated techniques for clever imaginative and prescient platforms, ACIVS 2009, held in Bordeaux, France in September/October 2009.

The forty three revised complete papers and 25 posters provided have been rigorously reviewed and chosen from a hundred and fifteen submissions. The papers are geared up in topical sections on technovision, primary mathematical suggestions, photograph processing, coding and filtering, photograph and video research, machine imaginative and prescient, monitoring, colour, multispectral and special-purpose imaging, scientific imaging, and biometrics.

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Extra resources for Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings

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The log term allows to more penalize a presence of slight under-segmentation in comparison with a presence of slight over-segmentation. The final evaluation criterion H is then given by the following equation: MI + m × η H= (11) 1+m where m is a weighting coefficient which controls the importance of the over- or under-segmentation errors in the judgment. This evaluation method has been compared to criteria from the literature: the Vinet’s measure [18], the Hamming’s criterion [19], the three Yasnoff’s criteria [20] and the two Martins’s criteria [21].

Rudz et al. 3 and method 1. In such kind of fire images, the separation of fire regions can be easily performed in RGB color space. Therefore, like for the tunnel scale, the three rules of method 2 are verified by most fire pixels. 6 Conclusion Over the last decades, several segmentation methods have been developed to detect the flame region in a fire scene. In this paper, we have attempted to evaluate four segmentation methods available from the wildland and urban fires literature using supervised evaluation.

IEEE AutoID 1999, pp. es Abstract. Moving object detection is a critical task for many computer vision applications: the objective is the classification of the pixels in the video sequence into either foreground or background. A commonly used technique to achieve it in scenes captured by a static camera is Background Subtraction (BGS). Several BGS techniques have been proposed in the literature but a rigorous comparison that analyzes the different parameter configuration for each technique in different scenarios with precise ground-truth data is still lacking.

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