Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple analog and hybrid networks for surface interpolation
AIP Conference Proceedings 151 on Neural Networks for Computing
Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
A parameter-free multiplier method for constrained minimization problems
Journal of Computational and Applied Mathematics
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Analog VLSI and neural systems
Analog VLSI and neural systems
Integration of visual modules: an extension of the Marr paradigm
Integration of visual modules: an extension of the Marr paradigm
Introduction to numerical linear algebra and optimisation
Introduction to numerical linear algebra and optimisation
Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analog VLSI chip for thin-plate surface interpolation
Advances in neural information processing systems 1
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Networks and the Best Approximation Property
Networks and the Best Approximation Property
Multi-Level Reconstruction of Visual Surfaces: Variational Principles and Finite Element Representations
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Height and Gradient from Shading
Height and Gradient from Shading
Physically Based Adaptive Preconditioning for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in the cooperation of shape from shading and stereo vision
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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Applications in machine vision of constraint networks based on an augmented Lagrangian formulation are discussed. Only those applications that have a fundamental significance are addressed. The first of these provides a generalization of the Harris coupled depth-slope analog model of visual reconstruction. Because of the generality of the approach, one can derive many more alternative structures, and the mathematical setting places this approach within the bounds of mixed finite element theory. This offers many advantages in terms of the associated mathematical theory and implementation on digital machines. The second use is in data fusion, which is a crucial task for systems using multiple sensors or methods of analysis of data.