Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning and Recognition of 3D Visual Objects in Real-Time
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Scale-invariant proximity graph for fast probabilistic object recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
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In this paper, we present a real-time algorithm for 3D object detection in images. Our method relies on the Ullman and Basri [13] theory which claims that the same object under different transformations can often be expressed as the linear combinations of a small number of its views. Thus, in our framework the 3D object is modelized by two 2D images associated with spatial relationships described by local-invariant feature points. The recognition is based on feature points detection and alignment with the model. Important theoretical optimizations have been introduced in order to speed up the original full alignment scheme and to reduce the model size in memory. The recognition process is based on a very fast recognition loop which quickly eliminates outliers. The proposed approach does not require a segmentation stage, and it is applicable to cluttered scenes. The small size of the model and the rapidity of the detection make this algorithm particularly suitable for real-time applications on mobile devices.