The human brain is an important part of visualization. The human brain contains approximately a hundred billion neurons and glia. Neurons are the building block of the brain that receive and send chemical signals. It helps to feel the environment around us. ‘Glia’ is Latin for ‘glue’. Glia also helps in brain signaling along with the neurons. It can act like insulation and speed up the signal transmission. It can also act as an immune cell for non-responding neurons in our brain. Artificial intelligence also follows the same mathematics.
How the brain works for visuals. Our eyes act as cameras for capturing real-world images. The images are passed to the back of the brain for detection and recognition. There are different nerves present in the retina of the eye. These nerves convert the captured light into electrical impulses. These electric impulses are sent to the back of the brain using the optic nerve. This particular area in the back of the brain is the primary visual cortex. The visualization depends on light intensity rather than on color. The same method is followed in computer vision.
The visualization of the brain function is studied on a newborn kitten with one eye closed. The experiment is executed by David Hubel and Torstein Wiesel in 1964. They hypothesized that there is a period during which the visual nerve cells develop. The retina should receive any visual information during visual nerve cells development. If it did not receive, the cell of the visual cortex redistributes its response in favor of the working eye. Closing one eye of a newborn kitten reduced visual input to the retina. One conclusion was made based on this experiment. There are few brain cells that are mostly focused on detecting vertical lines. There are also few other brain cells that are focused on detecting other patterns of an object.
With these findings, the computer scientist developed a mathematical model. The model works in the same way as biological neurons. This model is known as artificial neuron. This artificial neuron contains weights for the input streams and has output. The output is based on the mathematical computation of the input and its weight. There can be n number of neurons in the layers of the neural network. There can be m number of layers in the neural network. A convolutional neural network is one such network widely used for computer vision. This is because of its computational efficiency and spatial invariance. You will get to know more about convolutional neural networks in the CV X.X theory part.
From here, the idea for computer vision such as object detection started. A lot of algorithms are present today on this principle of pattern recognition. Artificial intelligence or machine learning can be understood as a pattern recognizer. Artificial intelligence as described in Britannica is a system endowed with intellectual processes. The intelligence of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
This is all explanation of how human vision is replicated into computer vision. This is done with the help of biologists and computer scientists. Mathematicians are extracting information from digital images for a long time. Now, computer vision is the most common technology used in various domains. New fields are also emerging where computer vision can play a very crucial role. Few of the applications are in medicine, machine vision, military, autonomous vehicle, etc.