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Computer Vision Metrics: Chapter Six (Part E)

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For Part D of Chapter Six, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


HAAR Features

HAAR-like features [4,9] were popularized in the field of computer vision by the Viola Jones [186] algorithm. HAAR features are based on specific sets of rectangle patterns, as shown in Figure 6-21, which approximate the basic HAAR wavelets, where each HAAR feature is composed of the average pixel value of pixels within the rectangle. This is efficiently computed using integral images.


Figure 6-21. Example HAAR-like features

By using the average pixel value in the rectangular feature, the intent is to find a set of small patterns in adjacent areas where brighter or darker region adjacency may reveal a feature—for example, a bright cheek next to a darker eye socket. However, HAAR features have drawbacks, since rectangles by nature are not rotation invariant much beyond 15 degrees. Also, the integration of pixel values within the rectangle destroys fine detail.

Depending on the type of feature to be detected, such as eyes, a specific set of HAAR feature is chosen to reveal eye/cheek details and eye/nose details. For example, HAAR patterns with two rectangles are useful for detecting edges, while patterns with three rectangles can be used for lines, and patterns with an inset rectangle or four rectangles can be used for single-object features. Note that HAAR features may be a rotated set.

Of course, the scale of the HAAR patterns is an issue, and since a given HAAR feature only works with an image of appropriate scale. Image pyramids are used for HAAR feature detection, along with other techniques for stepping the search window across the image in optimal grid sizes for a given application. Another method to address feature scale is to use a wider set of scaled HAAR features to perform the pyramiding in the feature space rather than the image space. One method to address HAAR feature granularity and rectangular shape is to use overlapping HAAR features to approximate octagons and hexagons; see the CenSurE and STAR methods in Figure 6-19.

HAAR features are closely related to wavelets [227,334]. Wavelets can be considered as an extension of the earlier concept of Gabor functions [333,187]. We provide only a short discussion of wavelets and Gabor...