Title : Rapid Object Detection using a Boosted Cascade of Simple Features
Author : P. Viola and M. Jones
summarization:
Three major contributions:
1. introduce a new image representation call "integral image".
there 2 reasons why using features rather than pixels dirctly:
(1) can encode ad-hoc domain knowledge from training data.
(2) feature based system much faster.
this feature compute the sum of pixel values with a rectangular regions,
and use the difference between 2, 3, or 4 regions as the feature.
The best advantage is this paper use a dynamic programming way to compute the
difference between regions efficiently.
2. learning algorithm based on AdaBoost.
the object of AdaBoost is to select small set form all feature set and train the classify.
It selects the feature which is significant and achieve fewer than 1% false negatives
and 40% false positives by the experiment.
3. the method for combining increasingly more complex classifiers in a "cascade",
which like a filter and quickly discarded the noise region. This method could reject
many of negative regions and nearly detect all of positive regions.
critique : I think two methods in this paper is attractive. one is the efficiently feature computing.
the dynamic technique reduce the total computation needed. second is the cascade
method, reject most non-target at earlier stage but still guarantee the detection of
positive region.
and there are discussion after each issue, I think it is more readable for readers.
