Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to recognize three-dimensional objects
Neural Computation
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Hausdorff Kernel for 3D Object Acquisition and Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Face detection using a first-order RCE classifier
EURASIP Journal on Applied Signal Processing
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
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This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition (Heeger and Bergen 1995, De Bonet and Viola 1998, Zhu et al. 1998, Simoncelli and Portilla 1998). Our method follows the texture recognition work of De Bonet and Viola. We use feature vectors which capture the joint occurrence of local features at multiple resolutions. The distribution of feature vectors for a set of training images of an object class is estimated by clustering the data and then forming a mixture of gaussian model. The mixture model is further refined by determining which clusters are the most discriminative for the class and retaining only those clusters. After the model is learned, test images are classified by computing the likelihood of their feature vectors with respect to the model. We present promising results in applying our technique to face detection and car detection.