UCLA
Assembly Line
📐 UCLA Researchers Propose PhyCV: A Physics-Inspired Computer Vision Python Library
In the latest innovation, Jalali-Lab @ UCLA has developed a new Python library called PhyCV, which is the first Physics-based Computer vision Python library. This unique library uses algorithms based on the laws and equations of physics to analyze pictorial data. These algorithms imitate how light passes through several physical materials and are based on mathematical equations rather than a series of hand-crafted rules. The algorithms in PhyCV are built on the principles of a rapid data acquisition method called the photonic time stretch.
The three algorithms included in PhyCV are – Phase-Stretch Transform (PST) algorithm, Phase-Stretch Adaptive Gradient-Field Extractor (PAGE) algorithm, and Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) algorithm.
Hybrid AI-Powered Computer Vision Combines Physics and Big Data
Many computer vision techniques infer properties of our physical world from images. Although images are formed through the physics of light and mechanics, computer vision techniques are typically data driven. This trend is mostly performance related: classical techniques from physics-based vision often score lower on metrics compared with modern deep learning. However, recent research, covered in this Perspective, has shown that physical models can be included as a constraint into data-driven pipelines. In doing so, one can combine the performance benefits of a data-driven method with advantages offered from a physics-based method, such as intepretability, falsifiability and generalizability. The aim of this Perspective is to provide an overview into specific approaches for integrating physical models into artificial intelligence pipelines, referred to as physics-based machine learning. We discuss technical approaches that range from modifications to the dataset, network design, loss functions, optimization and regularization schemes.