Segmentation from motion of non-rigid objects by neuronal lateral interaction
Pattern Recognition Letters
Local Accumulation of Persistent Activity at Synaptic Level: Application to Motion Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
What Can We Compute with Lateral Inhibition Circuits?
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Motion-based stereovision method with potential utility in robot navigation
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Motion features to enhance scene segmentation in active visual attention
Pattern Recognition Letters
Visual surveillance by dynamic visual attention method
Pattern Recognition
Stereovision depth analysis by two-dimensional motion charge memories
Pattern Recognition Letters
Dynamic visual attention model in image sequences
Image and Vision Computing
On how the computational paradigm can help us to model and interpret the neural function
Natural Computing: an international journal
| Hi-index | 0.00 |
Certainly, one of the prominent ideas of Professor Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research of Professor Mira and our team at University of Castilla-La Mancha has been that any bottom-up organization may be made operational using two biologically inspired methods called "algorithmic lateral inhibition", a generalization of lateral inhibition anatomical circuits, and "accumulative computation", a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulations of both methods, which have led to quite efficient solutions of problems related to motion-based computer vision.