Multisensor Fusion and Integration for Intelligent Systems: by Khalid Touil, Mourad Zribi, Mohammed Benjelloun (auth.),

By Khalid Touil, Mourad Zribi, Mohammed Benjelloun (auth.), Hernsoo Hahn, Hanseok Ko, Sukhan Lee (eds.)

This quantity goals at delivering engineers, researchers, educators, and complicated scholars within the box of Multisensor Fusion and Integration for clever platforms with the new development in thought and functions. This used to be performed by way of opting for prime quality papers with acceptable issues from the IEEE overseas convention on of Multisensor Fusion and Integration for clever platforms (MFI 2008) held in Seoul, Korea, August 20-22, 2008, and by way of modifying them for a constant circulate of the selected subject. The ebook has the subsequent specific gains: 1) The e-book addresses the becoming curiosity in our society concerning the box of Multisensor Fusion and Integration for clever platforms and a pair of) the ebook includes an creation for every chosen subject matter and integrated the editorial strategy to gather ultimate manuscripts in significant revision. the subsequent subject matters are lined by way of the ebook: Sensor community, info Fusion, Robotics, Surveillance, imaginative and prescient, HCI, SLAM, purposes (Underwater, ITS, etc.).

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Lee et al. C. Ooi et al. Historically, there have been two main approaches [1] in extracting lip features from image sequences. The first method is called the Image-based approach. g. intensity values) around the lip region are used as features for recognition. For instance, these approaches are based on a DCT or a PCA method. The projected low dimensional features are used for speech recognition. The extracted features not only consist of lip features but also of other facial features such as tongue and jaw movement depending on ROI size.

Since no ground truth is available, we manually draw the boundaries of 25 lip images. The first measurement method is the degree of overlap (DOL) between the lip and the non-lip histograms. DOL [9] is used to measure discriminability of the transformed color spaces for differentiating the lip and the non-lip colors. A lower percentage of DOL means a higher contrast between the lip and the non-lip regions. DOL = 1 min(Plip (i), Pnonlip (i)) i=0 where Plip (i) = Num lip (i)/Total lipPixel Pnonlip (i) = Num nonlip (i)/Total nonlipPixel , {0 ≤ i ≤ 1} (10) The second method is Classification Error (CE) which is the average of the False Positive (FP) rate and the False Negative (FN) rate FP is error rate of classifying a non-lip as a lip pixel.

3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. , and Cherfi Z-L. Distributed data fusion: application to the confidence management in vehicular networks. Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany, pp. 846–853 , July 2008. C. and Lages U. Pedestrian detection and classification by Laser scanners. 9th EAEC International Congress, Paris, France, June 2003. Suard F. et al. Pedestrian detection using stereo-vision and graph kernels. Proceedings of the IEEE Intelligent Vehicles Symposium IV’2005, pp.

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