Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Independent component analysis: algorithms and applications
Neural Networks
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
Facial expressional image synthesis controlled by emotional parameters
Pattern Recognition Letters
Using Bilinear Models for View-invariant Action and Identity Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Illumination-robust face recognition using ridge regressive bilinear models
Pattern Recognition Letters
Bilinear Models for Spatio-Temporal Point Distribution Analysis
International Journal of Computer Vision
Bimode model for face recognition and face representation
Neurocomputing
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Bilinear models have been proposed to separate the factors from the observations for joint factors identification or translation tasks. However, the performance of existing bilinear models may degrade under challenging conditions when local image information cannot be obtained caused by occlusions or image noises. In this paper, a novel sub-pattern bilinear model (SpBM) is proposed. Different from existing bilinear models, SpBM constructs the sub-pattern bilinear model through a novel learning algorithm utilizing local patterns generated by dividing global patterns in a deterministic way. As a result, the specific factors of testing observation are identified by synthesizing the discriminative information provided by the local sub-patterns. To further improve the identification performance of SpBM, a new ridge regressive parameter estimation algorithm (RRPE) is also proposed. RRPE introduces the ridge regression into parameter estimation to stabilize the matrix inverse computation and alleviate the non-convergent cases. The proposed sub-pattern bilinear model is introduced into pose estimation of work-pieces to separate and estimate some key pose factors individually. Experimental results demonstrate the effectiveness of the proposed method.