Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern Recognition Letters
Stability and likelihood of views of three dimensional objects
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Digital Picture Processing
On View Likelihood and Stability
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity and Symmetry Measures for Convex Shapes Using Minkowski Addition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Alignment and Correspondence Using Singular Value Decomposition
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Relational Constraints for Point Distribution Models
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Automatic Hierarchical Classification of Silhouettes of 3D Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A unified framework for alignment and correspondence
Computer Vision and Image Understanding
Object recognition using wavelets, L-G graphs and synthesis of regions
Pattern Recognition
Polygonal shape description for recognition of partially occluded objects
Pattern Recognition Letters
Invariant kernel functions for pattern analysis and machine learning
Machine Learning
Globally Consistent Reconstruction of Ripped-Up Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Recovering the 3D shape and poses of face images based on the similarity transform
Pattern Recognition Letters
A complete system of measurement invariants for Abelian lie transformation groups
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Shape prior embedded geodesic distance transform for image segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Geometric invariant curve and surface normalization
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
On similarity-invariant fairness measures
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Mathematical analysis on affine maps for 2D shape interpolation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Mathematical analysis on affine maps for 2D shape interpolation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Shape priors extraction and application for geodesic distance transforms in images and videos
Pattern Recognition Letters
Shape retrieval and recognition based on fuzzy histogram
Journal of Visual Communication and Image Representation
Mathematical description of motion and deformation: from basics to graphics applications
SIGGRAPH Asia 2013 Courses
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We develop expressions for measuring the distance between 2D point sets, which are invariant to either 2D affine transformations or 2D similarity transformations of the sets, and assuming a known correspondence between the point sets. We discuss the image normalization to be applied to the images before their comparison so that the computed distance is symmetric with respect to the two images. We then give a general (metric) definition of the distance between images, which leads to the same expressions for the similarity and affine cases. This definition avoids ad hoc decisions about normalization. Moreover, it makes it possible to compute the distance between images under different conditions, including cases where the images are treated asymmetrically. We demonstrate these results with real and simulated images.