Artificial Intelligence - Special volume on computer vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Interest point detection using imbalance oriented selection
Pattern Recognition
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Interest points of general imbalance
IEEE Transactions on Image Processing
A global-to-local scheme for imbalanced point matching
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hi-index | 0.00 |
A characteristics of imbalanced points is their localities--an imbalanced point may be contiguous to some other imbalanced points in terms of 8-connectivity. A two-layer scheme was recently proposed for matching imbalanced points based on localities, where the first layer aims to build locality correspondence, and the second layer aims to build point correspondence within corresponding localities. Under the framework of the two-layer matching, we propose a hybrid representation of imbalanced points. Specifically, an imbalanced point in the first layer is represented by a discriminant SIFT-type descriptor, and in the second layer, the imbalanced point is simply represented by a patch-type descriptor (the intensities of its neighborhood). We will justify the rationale of the proposed hybrid representation scheme and show its superiority over nonhybrid representation with experiments.