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...by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers =-=[6]-=-. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-l...

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...tor by focussing attention on promising regions of the image. The notion behind focus of attention approaches is that it is often possible to rapidly determine where in an image an object might occur =-=[19, 8, 1]-=-. More complex processing is reserved only for these promising regions. The key measure of such an approach is the “false negative” rate of the attentional process. It must be the case that all, or al...

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...aluation of the second dot product is accomplished with four array accesses. 2.2 Feature Discussion Rectangle features are somewhat primitive when compared with alternatives such as steerable filters =-=[5, 7]-=-. Steerable filters, and their relatives, are excellent for the detailed analysis of boundaries, image compression, and texture analysis. In contrast rectangle features, while sensitive to the presenc...

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... boosting process, which selects a new weak classifier, can be viewed as a feature selection process. AdaBoost provides an effective learning algorithm and strong bounds on generalization performance =-=[14, 9, 10]-=-. The third major contribution of this paper is a method for combining successively more complex classifiers in a cascade structure which dramatically increases the speed of the detector by focussing ...

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... boosting process, which selects a new weak classifier, can be viewed as a feature selection process. AdaBoost provides an effective learning algorithm and strong bounds on generalization performance =-=[14, 9, 10]-=-. The third major contribution of this paper is a method for combining successively more complex classifiers in a cascade structure which dramatically increases the speed of the detector by focussing ...

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...entation called an integral image that allows for very fast feature evaluation. Motivated in part by the work of Papageorgiou et al. our detection system does not work directly with image intensities =-=[10]-=-. Like these authors we use a set of features which are reminiscent of Haar Basis functions (though we will also use related filters which are more complex than Haar filters). In order to compute thes...

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...mbination of the hypotheses where the weights are inversely proportional to the training errors. 3.1 Learning Discussion Many general feature selection procedures have been proposed (see chapter 8 of =-=[20]-=- for a review). Our final application demanded a very aggressive process which would discard the vast majority of features. For a similar recognition problem Papageorgiou et al. proposed a scheme for ...

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... to compute these features very rapidly at many scales we introduce the integral image representation for images (the integral image is very similar to the summed area table used in computer graphics =-=[3]-=- for texture mapping). The integral image can be computed from an image using a few operations per pixel. Once computed, any one of these Harr-like features can be computed at any scale or location in...

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...e computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems =-=[18, 13, 16, 12, 1]-=-. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. 1. Introduction This paper brings together new algorithms and insights to construct a framework for robust and...

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... the image. In recent work Fleuret and Geman have presented a face detection technique which relies on a “chain” of tests in order to signify the presence of a face at a particular scale and location =-=[4]-=-. The image properties measured by Fleuret and Geman, disjunctions of fine scale edges, are quite different than rectangle features which are simple, exist at all scales, and are somewhat interpretabl...

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...e computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems =-=[18, 13, 16, 12, 1]-=-. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. 1. Introduction This paper brings together new algorithms and insights to construct a framework for robust and...

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...aluation of the second dot product is accomplished with four array accesses. 2.2 Feature Discussion Rectangle features are somewhat primitive when compared with alternatives such as steerable filters =-=[5, 7]-=-. Steerable filters, and their relatives, are excellent for the detailed analysis of boundaries, image compression, and texture analysis. In contrast rectangle features, while sensitive to the presenc...

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...e computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems =-=[18, 13, 16, 12, 1]-=-. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. 1. Introduction This paper brings together new algorithms and insights to construct a framework for robust and...

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... references, eight in the case of the three-rectangle features, and nine for four-rectangle features. One alternative motivation for the integral image comes from the “boxlets” work of Simard, et al. =-=[17]-=-. The authors point out that in the case of linear operations (e.g. *,+-), any invertible linear operation can be to* or- applied if its inverse is applied to the result. For example in the case of co...