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

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

This quantity goals at supplying engineers, researchers, educators, and complex scholars within the box of Multisensor Fusion and Integration for clever platforms with the hot growth in conception and functions. This used to be performed via identifying top of the range papers with applicable subject matters from the IEEE foreign convention on of Multisensor Fusion and Integration for clever structures (MFI 2008) held in Seoul, Korea, August 20-22, 2008, and through enhancing them for a constant circulate of the selected topic. The ebook has the next detailed positive aspects: 1) The booklet addresses the growing to be curiosity in our society concerning the box of Multisensor Fusion and Integration for clever platforms and a couple of) the publication includes an advent for every chosen subject matter and integrated the editorial method to gather ultimate manuscripts in significant revision. the next subject matters are coated by way of the ebook: Sensor community, info Fusion, Robotics, Surveillance, imaginative and prescient, HCI, SLAM, functions (Underwater, ITS, etc.).

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Extra info for Multisensor Fusion and Integration for Intelligent Systems: An Edition of the Selected Papers from the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2008

Sample text

4 (a) Cmap image, (b) Cmap negative image, (c) Gray-scale image After the color transformation, the C squared image may still show low contrast in the upper lip region. This problem can be resolved by using the intensity information I. The upper lip region typically consists of lower intensity values. So by combining the C squared image (which is well separable in the lower lip) and intensity image (which has a stronger boundary in the upper lip), we can obtain an enhanced version of the lip color map Cmap as follows.

These parameters are calculated from a cost function minimization process of fitting the model onto a captured image of the lip. The active contour model, the deformable geometry model, and the active shape model are examples of such methods widely used in lip tracking and feature extraction. The advantage of this approach is that lip shapes can be easily described by low order dimensions and it is invariant under rotation, translation, or scaling. However, this method requires an accurate model initialization to ensure that the model updating process converges.

The contour point normalization process is applied to reduce processing time and to simplify the curve fitting process. The left corner point is fixed as the origin. Rotation and scaling transformations are employed to normalized all points so that p1 is at (0, 0) and p9 is at (1, 0). Reverse normalization is applied after curve fitting for obtaining the original coordinates. 4 Model Optimization The optimization procedure is an iterative process and the lip points are adjusted in order to reduce the cost function in each iteration process.

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