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Articles 91 - 93 of 93
Full-Text Articles in Entire DC Network
An Algorithm For Parallel 3d Reconstruction Of Asymmetric Objects From Electron Micrographs, Robert E. Lynch, Hong Lin, Dan C. Marinescu
An Algorithm For Parallel 3d Reconstruction Of Asymmetric Objects From Electron Micrographs, Robert E. Lynch, Hong Lin, Dan C. Marinescu
Department of Computer Science Technical Reports
No abstract provided.
Velocity Estimation Via A Neural Network Enhanced By Classical Detection Algorithms, Zeki Berk Hamşioğlu
Velocity Estimation Via A Neural Network Enhanced By Classical Detection Algorithms, Zeki Berk Hamşioğlu
Electrical & Computer Engineering Theses & Dissertations
The goal of this research is to show how to solve a velocity estimation problem using a neural network connected to an array of sensors. Motivated by biological studies involving insect vision, the neural network utilized is a member of a class of shunting neural networks. When an object moves across the face of the sensor array, the neural network's pulse response is first temporally located using classical M-ary detection techniques. Both the deterministic and stochastic cases are considered. Then the network's pulse response is post-processed via an existing velocity estimation algorithm based on a Volterra series model of the …
Physiologically-Based Vision Modeling Applications And Gradient Descent-Based Parameter Adaptation Of Pulse Coupled Neural Networks, Randy P. Broussard
Physiologically-Based Vision Modeling Applications And Gradient Descent-Based Parameter Adaptation Of Pulse Coupled Neural Networks, Randy P. Broussard
Theses and Dissertations
In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded …