Neural networks and the bias/variance dilemma
Neural Computation
Combining Symbolic and Neural Learning
Machine Learning
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Segmentation from motion of non-rigid objects by neuronal lateral interaction
Pattern Recognition Letters
The Neural Network Pushdown Automaton: Architecture, Dynamics and Training
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Computation: finite and infinite machines
Computation: finite and infinite machines
Motion features to enhance scene segmentation in active visual attention
Pattern Recognition Letters
Parallel algorithms development for programmable logic devices
Advances in Engineering Software
Visual surveillance by dynamic visual attention method
Pattern Recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Finite state automata and simple recurrent networks
Neural Computation
Road-traffic monitoring by knowledge-driven static and dynamic image analysis
Expert Systems with Applications: An International Journal
Accelerating colour space conversion on reconfigurable hardware
Image and Vision Computing
Inductive inference from noisy examples using the hybrid finite state filter
IEEE Transactions on Neural Networks
Optical flow or image subtraction in human detection from infrared camera on mobile robot
Robotics and Autonomous Systems
Model-driven engineering techniques for the development of multi-agent systems
Engineering Applications of Artificial Intelligence
Human activity monitoring by local and global finite state machines
Expert Systems with Applications: An International Journal
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
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Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. In the few last years, the neurally inspired lateral inhibition in accumulative computation (LIAC) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to LIAC in motion detection by means of a formal model described as finite state machines. This paper introduces two steps towards that direction: (a) A simplification of the general LIAC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed LIAC module, as well as an 8x8 LIAC module, has been tested on several video sequences, providing promising performance results.