Most objects' detection problems, such as face/person detection or lesion detection in medicine, require searching for the object in many image patches. However, examining all image zones and computing the feature set for each zone are time-consuming tasks. Cascade detectors are widely used because of their high efficiency in doing this.

Cascade detectors consist of various boosting stages. The boosting algorithm selects the best feature set to create and combine a number of weak tree classifiers. Thus, boosting is not only a detector but also a feature selection method. Each stage is usually trained to detect nearly 100 percent of objects correctly and discard at least 50 percent of the background images. Therefore, background images, which represent a larger number of images, need less processing time as they are discarded at the early stages of the cascade. Moreover, the concluding cascade stages use more features than earlier stages, and even then only objects and...