A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Detection of 3D objects in cluttered scenes using hierarchical eigenspace
Pattern Recognition Letters
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
3D object recognition and pose with relational indexing
Computer Vision and Image Understanding
Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Statistical Image Object Recognition using Mixture Densities
Journal of Mathematical Imaging and Vision
Video Coding: An Introduction to Standard Codecs
Video Coding: An Introduction to Standard Codecs
Improved Appearance-Based 3-D Object Recognition Using Wavelets
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Model Based Object Recognition by Robust Information Fusion
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Illumination insensitive recognition using eigenspaces
Computer Vision and Image Understanding
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
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In this article we present a new appearance-based approach for the classification and the localization of 3-D objects in complex scenes. A main problem for object recognition is that the size and the appearance of the objects in the image vary for 3-D transformations. For this reason, we model the region of the object in the image as well as the object features themselves as functions of these transformations. We integrate the model into a statistical framework, and so we can deal with noise and illumination changes. To handle heterogeneous background and occlusions, we introduce a background model and an assignment function. Thus, the object recognition system becomes robust, and a reliable distinction, which features belong to the object and which to the background, is possible. Experiments on three large data sets that contain rotations orthogonal to the image plane and scaling with together more than 100000 images show that the approach is well suited for this task.