In modern computer vision, there's a significant shift from geometry to machine learning, where algorithms learn to recognize and segment objects from vast amounts of image data.

The motivation for using parameter estimation in image processing has shifted towards machine learning, which has generated more interest in this area.

One of my early projects involved translating classical projective geometry concepts into geometric algebra, which simplified the mathematical complexity of computer vision tasks.

In my research, we are keen on analyzing moving images to extract geometric objects and understand motion, which is crucial for applications like self-driving cars. Geometric algebra serves as a unifying language, simplifying complex computations in fields like computer vision, making it easier to work with concepts like rotations and translations without confusion.

Geometric algebra provides a mathematical framework that allows us to work with lines in computer vision, which is more complex than working with points.

The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.

Visual spatial intelligence is as fundamental as language, and we have the right ingredients now, including advancements in compute and algorithms, to unlock its potential.

The breakthrough moment in computer vision for deep learning was the 2012 AlexNet paper, which significantly outperformed previous algorithms in the ImageNet challenge.

Using geometric algebra provides a clear origin point for calculations, eliminating the confusion of multiple coordinate systems and making it easier to handle rotations and translations in computer vision.