I’m interested in computer vision, computer graphics, image processing, generative (algorithmic) art… you get the picture. I’ll label it all as “visual computing” and attempt to found a base for it here.
Fluid Simulation
a compilation of my own notes on fluid simulation.
CS 280: Computer Vision
notes from UC Berkeley’s computer vision class.
CSE 252A: Computer Vision I
notes from UC San Diego’s computer vision class.
CSE 252B: Computer Vision II
notes from another UCSD computer vision class.
OpenGL Dictionary
a collection of OpenGL terms and definitions.
The fundamental problem of vision, as Donald D. Hoffman describes in his book Visual Intelligence, is the notion that “the image at the eye has countless possible interpretations.” For example, the image on the retina has only two spatial dimensions despite our minds seeing three. Likewise, the image on the retina is just a bundle of responses to light energy, but we perceive people we know and objects we name and scratchy marks that rend our hearts to ash. How do we manage reconstruction? Recognition? In seeking to understand this, we seek to understand vision. In seeking to understand vision, we seek to understand this.
(11/2017) What is Computer Vision?
in which 2017 Owen attempts to describe computer vision.
(02/2018) Matrix Ordering
in which I make sense of reversed-order matrix multiplications in ML code.
(03/2018) Tensor Algebra
in which I go over some of the tensor algebra examples that Kevin showed me.
(11/2018) Effective Receptive Field
a small illustration of effective receptive field in a CNN.
