Active shape models—their training and application
Computer Vision and Image Understanding
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Computation of Average Brain Models
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A bootstrapping algorithm for learning linear models of object classes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Automatic Construction of Active Appearance Models as an Image Coding Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Generic vs. person specific active appearance models
Image and Vision Computing
Robust autonomous model learning from 2D and 3D data sets
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Intensity-Based Congealing for Unsupervised Joint Image Alignment
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Computing Accurate Correspondences across Groups of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Landmark annotation for training images is essential for many learning tasks in computer vision, such as object detection, tracking, and alignment. Image annotation is typically conducted manually, which is both labor-intensive and error-prone. To improve this process, this paper proposes a new approach to estimating the locations of a set of landmarks for a large image ensemble using manually annotated landmarks for only a small number of images in the ensemble. Our approach, named semi-supervised least-squares congealing, aims to minimize an objective function defined on both annotated and unannotated images. A shape model is learned online to constrain the landmark configuration. We employ an iterative coarse-to-fine patch-based scheme together with a greedy patch selection strategy for landmark location estimation. Extensive experiments on facial images show that our approach can reliably and accurately annotate landmarks for a large image ensemble starting with a small number of manually annotated images, under several challenging scenarios.