EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Design and Use of Linear Models for Image Motion Analysis
International Journal of Computer Vision
On Utilising Template and Feature-Based Correspondence in Multi-view Appearance Models
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Part III: dynamic texture synthesis
ACM SIGGRAPH 2007 courses
Face recognition across pose: A review
Pattern Recognition
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
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The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. The representation consists of two image measurements made at the feature points: shape and texture. Feature geometry, or shape, is represented using the (x, y) locations of features relative to the some standard reference shape. Image grey levels, or texture, are represented by mapping image grey levels onto the standard reference shape. Computing this representation is essentially a correspondence task, and in this paper we explore an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. In addition to describing the vectorizer, an application to the problem of facial feature detection will be presented.