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Many different visual modalities and feature-processing methods can be used for the same object-tracking task. In this chapter we provide a variety of examples, following the abstraction of 3 and organized in the following way (Fig. 4.1): Global statistics. Reference statistics (e.g., a color distribution) are collected from the visible object surface, and matched to the image statistics under the region of interest. Background model. A reference pixel-wise model of the background can be matched with the current image in order to remove data that should not belong to any object. Blobs. Connected components are clustered from a given saliency map and matched to the model silhouette projected. Contours. Sharp intensity, color, and texture transitions in the image are matched to the model contours projected. Motion. A motion eld detected in the image (scalar or vector) is compared to the expected motion eld generated by the object. Keypoints. Distinctive gray-level patches are detected and matched to those sampled from the model surface. Template. A fully textured model is rendered and matched to the underlying pixel colors.

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When you mix cells with percent and normal formatting in a column, you may prefer to see the nonpercent values indented from the right so the values line up properly. To do so, apply the following number format to nonpercent cells. This format string uses an underscore followed by the percent symbol. The result is a space equal to the width of the percent symbol.

Each principal component represents a weighted combination of the observed variables. Although all variables in a dataset are combined in a speci c principal component, the weights re ect the relative importance of each variable within each principal component. For example, Fig. 4.11 displays a series of weights for ve principal components (PC1 PC5) extracted from a dataset of ve variables [age ( years), weight (lbs), height (inches), abdomen (cm), ankle (cm)]. Each weight, or loading, within a principal component re ects the relative importance of the variable, and these values fall within the range of 1 to 1. For example, in the second principal component, PC2, age ( years) has a strong negative score of 0.781 whereas weight (lbs) is given a score close to 0.

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