A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to wavelets
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
An Experimental Comparison of Appearance and Geometric Model Based Recognition
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Appearance-Based Face Recognition and Light-Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image classification using a neural network
Pattern Recognition Letters
Model-based and image-based methods for facial image synthesis, analysis and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Dense statistic versus sparse feature-based approach for 3D object recognition
Pattern Recognition and Image Analysis
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In this paper an appearance-based statistical approach for localization and classification of 3-D objects in 2-D color images with real heterogeneous backgrounds is presented. The object feature extraction is done separately for the red, green, and blue channel. We compute six dimensional local feature vectors directly from pixel values in the images using wavelet multiresolution analysis. The first and second component of the feature vectors depend on the pixel values in the red channel, the third and fourth in the green channel, and fifth and sixth in the blue channel. Then we define an object area as a function of 3-D transformations and represent the feature vectors as probability density functions. In the recognition phase we use an algorithm based on maximum likelihood estimation for object localization and classification. Experiments made on a real data set with 39600 images compare the recognition rates for the new algorithm, which uses the color information of objects, with the results in the case of gray level images.