Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
International Journal of Computer Vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects
Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Acquiring Robust Representations for Recognition from Image Sequences
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
An Approach to Visual Scenes Matching with Curvilinear Regions
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Basic Image Features (BIFs) Arising from Approximate Symmetry Type
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Classifying a New Descriptor Based on Marr's Visual Theory
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
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There is considerable evidence that object recognition in primates is based on the detection of local image features of intermediate complexity that are largely invariant to imaging transformations. A computer vision system has been developed that performs object recognition using features with similar properties. Invariance to image translation, scale and rotation is achieved by first selecting stable key points in scale space and performing feature detection only at these locations. The features measure local image gradients in a manner modeled on the response of complex cells in primary visual cortex, and thereby obtain partial invariance to illumination, affine change, and other local distortions. The features are used as input to a nearest-neighbor indexing method and Hough transform that identify candidate object matches. Final verification of each match is achieved by finding a best-fit solution for the unknown model parameters and integrating the features consistent with these parameter values. This verification procedure provides a model for the serial process of attention in human vision that integrates features belonging to a single object. Experimental results show that this approach can achieve rapid and robust object recognition in cluttered partially-occluded images.