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Series in Machine Perception and Artificial Intelligence - Vol. 3
NEURAL NETWORKS IN VISION AND PATTERN RECOGNITION
edited by J Skrzypek & W Karplus (UCLA)
The neural network paradigm with its various advantages might be the next promising bridge between artificial intelligence and pattern recognition that will help with the conceptualization of new computational artifacts. This volume contains ten papers which represent some of the work being done in the field, such as in computational neuroscience, pattern recognition, computational vision, and applications.
Contents:
- Introduction (J Skrzypek & W Karplus)
- Lightness Constancy from
Luminance Contrast (J Skrzypek & D Gungner)
- Bringing the Grandmother Back into the Picture: A Memory- Based View of Object Recognition (S Edelman & T Poggio)
- Internal Organization of Classifier Networks Trained by Backpropagation (D F Michaels)
- System Identification with Artificial Neural Networks (E R Tisdale & W J Karplus)
- Mixed Finite Element Based Neural Networks in Visual Reconstruction (D Suter)
- The Random Neural Network Model for Texture Generation (V Atalay et al.)
- Neural Networks for Collective Translational Invariant Object Recognition (L-W Chan)
- Image Recognition and Reconstruction Using Associative Magnetic Processing (J M Goodwin et al.)
- Incorporating Uncertainty in Neural Networks (B R Kämmerer)
- Neural Networks for the Recognition of Engraved Musical Scores (P Martin & C Bellissant)
Readership: Computer scientists, engineers and neuroscientists.
| 224pp |
Pub. date: Jul 1992 |
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