Institute of Natural and Mathematical Sciences

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Subject: search.filters.subject.Haar-like features

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    Accelerated face detector training using the PSL framework
    (Massey University, 2009) Susnjak, T.; Barczak, A.L.C.; Hawick, K.A.
    We train a face detection system using the PSL framework [1] which combines the AdaBoost learning algorithm and Haar-like features. We demonstrate the ability of this framework to overcome some of the challenges inherent in training classifiers that are structured in cascades of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas- caded classifiers. We establish the ability of the PSL framework to produce classifiers in a complex domain in significantly reduced time frame. They also comprise of fewer boosted en- sembles albeit at a price of increased false detection rates on our test dataset. We also report on results from a more diverse number of experiments carried out on the PSL framework in order to shed more insight into the effects of variations in its adjustable training parameters.
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    Real-time computation of Haar-like features at generic angles for detection algorithms
    (Massey University, 2006) Barczak, A.L.C.; Johnson, M.J.; Messom, C.H.
    This paper proposes a new approach to detect rotated objects at distinct angles using the Viola-Jones detector. The use of additional Integral Images makes an approximation the Haar-like features for any given angle. The proposed approach uses di erent types of Haar-like features, including features that compute areas at 45o, 26.5o and 63.5o of rotation. Given a trained classi er (using normal features) a conversion is made using a pair of features so an equivalent value is computed for any angle. This conversion is only an approximation, but the errors are constrained and they would have limited impact on the nal accuracy of the classi er. We discuss the sources of errors in the computation of the Haar-like features and show through experiments that in natural images the errors are often negligible.
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    Real-time hand tracking using a set of cooperative classifiers based on Haar-like features
    (Massey University, 2005) Barczak, Andre L.C.; Dadgostar, Farhad
    In this paper we discuss the importance of the choice of features in digital image object recognition. The features can be classified as invariants or noninvariants. Invariant features are robust against one or more modifications such as rotations, translations, scaling and different light (illumination) conditions. Non-invariant features are usually very sensitive to any of these modifiers. On the other hand, non-invariant features can be used even in the event of translation, scaling and rotation, but the feature choice is in some cases more important than the training method. If the feature space is adequate then the training process can be straightforward and good classifiers can be obtained. In the last few years good algorithms have been developed relying on noninvariant features. In this article, we show how non-invariant features can cope with changes even though this requires additional computation at the detection phase. We also show preliminary results for a hand detector based on a set of cooperative Haar-like feature detectors. The results show the good potential of the method as well as the challenges to achieve real-time detection.