Filters and Other Potions for Early Vision and Recognition

Explore the foundations of computer vision and object recognition in this 43-minute lecture by Pietro Perona from the California Institute of Technology. Delve into topics such as edge detection, deep networks, and hierarchical processing. Learn about sampling techniques, performance evaluation, and the evolution of vision technologies. Gain insights into intuition-based approaches and spacetime oriented filters. Examine stability oriented filters, decomposition methods, and approximation theory. Investigate tensor factorization, multiscale analysis, and both linear and nonlinear operators. Understand local operators and their application in pedestrian detection systems. Enhance your understanding of early vision techniques and their role in modern recognition algorithms.

Introduction
Edges
Deep Networks
Hierarchy of Processing
Sampling
Performance through time
Technologies
Intuition
Spacetime Oriented Filters
Stability Oriented Filters
Decomposition
Approximation Theory
Tensor Factorization
Multiscale
Linear and nonlinear operators
Local operators
Pedestrian detector
Conclusion