Thursday, November 3, 2016

Introduction to Haar Cascades

Now that we're starting to build our extraction tool we needed to gain more background information to acquire a better understanding of how haarcascades work.

Background Info:

A HaarCascasde is used to detect objects within images. This feature based classifier was first introduced in the Viola Jones Algorithm explained in the paper "Rapid Object Detection using a Boosted Cascade of Simple Features" by Paula Viola and Michael Jones. The detection method is based off of machine learning and applying a cascade function that is trained from negative and positive images. After the cascade function is trained it can be used to detect the desired object within sample images.

Viola Jones Algorithm

The Viola Jones algorithm detection algorithm depends on "Haar features" to detect the presence of a desired image in a sample, an "Integral Image" which is a representation of the original image. The integral image allows a detector to evaluate features quickly, several operation are performed per pixel from an image. After each pixel is computed any Haar feature can be detected in current time regardless of the position in the image or scale of the image. "AdaBoost" is another vital part of the algorithm and is used for feature feature selection. Adaboost increases the speed of classification by excluding irrelevant features by focusing on a subset of Haar-like features. Cascading as previously mentioned is one of the major contributions to object detection from the algorithm. Cascading increases the speed of the classifier by focusing on the critical portions of the image. Non-promising regions of a sample are disregarded. Increasingly complex processing is applied only once a feature of interest is found.

Explored Sources:
"Rapid Object Detection using a Boosted Cascade of Simple Features"
Face Detection using Haar Cascades

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