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Visual perception is the ability to interpret the surrounding environment by processing information that is contained in visible light. The resulting perception is also known as eyesight, sight, or vision (adjectival form: visual, optical, or ocular). The various physiological components involved in vision are referred to collectively as the visual system, and are the focus of much research in psychology, cognitive science, neuroscience, and molecular biology.
The visual system in humans and animals allows individuals to assimilate information from their surroundings. The act of seeing starts when the lens of the eye focuses an image of its surroundings onto a light-sensitive membrane in the back of the eye, called the retina. The retina is actually part of the brain that is isolated to serve as a transducer for the conversion of patterns of light into neuronal signals. The lens of the eye focuses light on the photoreceptive cells of the retina, which detect the photons of light and respond by producing neural impulses. These signals are processed in a hierarchical fashion by different parts of the brain, from the retina upstream to central ganglia in the brain.
Note that up until now much of the above paragraph could apply to octopi, molluscs, worms, insects and things more primitive; anything with a more concentrated nervous system and better eyes than say a jellyfish. However, the following applies to mammals generally and birds (in modified form): The retina in these more complex animals sends fibers (the optic nerve) to the lateral geniculate nucleus, to the primary and secondary visual cortex of the brain. Signals from the retina can also travel directly from the retina to the superior colliculus.
The major problem in visual perception is that what people see is not simply a translation of retinal stimuli (i.e., the image on the retina). Thus people interested in perception have long struggled to explain what visual processing does to create what is actually seen.
There were two major ancient Greek schools, providing a primitive explanation of how vision is carried out in the body.
The first was the "emission theory" which maintained that vision occurs when rays emanate from the eyes and are intercepted by visual objects. If an object was seen directly it was by 'means of rays' coming out of the eyes and again falling on the object. A refracted image was, however, seen by 'means of rays' as well, which came out of the eyes, traversed through the air, and after refraction, fell on the visible object which was sighted as the result of the movement of the rays from the eye. This theory was championed by scholars like Euclid and Ptolemy and their followers.
The second school advocated the so-called 'intro-mission' approach which sees vision as coming from something entering the eyes representative of the object. With its main propagators Aristotle, Galen and their followers, this theory seems to have some contact with modern theories of what vision really is, but it remained only a speculation lacking any experimental foundation.
Both schools of thought relied upon the principle that "like is only known by like", and thus upon the notion that the eye was composed of some "internal fire" which interacted with the "external fire" of visible light and made vision possible. Plato makes this assertion in his dialogue Timaeus, as does Aristotle, in his De Sensu.
Leonardo da Vinci (1452–1519) is believed to be the first to recognize the special optical qualities of the eye. He wrote "The function of the human eye ... was described by a large number of authors in a certain way. But I found it to be completely different." His main experimental finding was that there is only a distinct and clear vision at the line of sight, the optical line that ends at the fovea. Although he did not use these words literally he actually is the father of the modern distinction between foveal and peripheral vision.
Hermann von Helmholtz is often credited with the first study of visual perception in modern times. Helmholtz examined the human eye and concluded that it was, optically, rather poor. The poor-quality information gathered via the eye seemed to him to make vision impossible. He therefore concluded that vision could only be the result of some form of unconscious inferences: a matter of making assumptions and conclusions from incomplete data, based on previous experiences.
Inference requires prior experience of the world.
Examples of well-known assumptions, based on visual experience, are:
The study of visual illusions (cases when the inference process goes wrong) has yielded much insight into what sort of assumptions the visual system makes.
Another type of the unconscious inference hypothesis (based on probabilities) has recently been revived in so-called Bayesian studies of visual perception. Proponents of this approach consider that the visual system performs some form of Bayesian inference to derive a perception from sensory data. Models based on this idea have been used to describe various visual perceptual functions, such as the perception of motion, the perception of depth, and figure-ground perception. The "wholly empirical theory of perception" is a related and newer approach that rationalizes visual perception without explicitly invoking Bayesian formalisms.
Gestalt psychologists working primarily in the 1930s and 1940s raised many of the research questions that are studied by vision scientists today.
The Gestalt Laws of Organization have guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. Gestalt is the German word "Gestalt" that partially translates to "configuration or pattern" along with "whole or emergent structure". According to this theory, there are six main factors that determine how the visual system automatically groups elements into patterns: Proximity, Similarity, Closure, Symmetry, Common Fate (i.e. common motion), and Continuity.
During the 1960s, technical development permitted the continuous registration of eye movement during reading in picture viewing and later in visual problem solving and when headset-cameras became available, also during driving.
The picture to the left shows what may happen during the first two seconds of visual inspection. While the background is out of focus, representing the peripheral vision, the first eye movement goes to the boots of the man (just because they are very near the starting fixation and have a reasonable contrast).
The following fixations jump from face to face. They might even permit comparisons between faces.
It may be concluded that the icon face is a very attractive search icon within the peripheral field of vision. The foveal vision adds detailed information to the peripheral first impression.
It can also be noted that there are three different types of eye movements: vergence movements, saccadic movements and pursuit movements. Vergence movements involve the cooperation of both eyes to allow for an image to fall on the same area of both retinas. This results in a single focused image. Saccadic movements is the type of eye movement that is used to rapidly scan a particular scene/image. Lastly, pursuit movement is used to follow objects in motion.
There is some evidence (including disorders such as prosopagnosia) that face recognition is distinct from object recognition in terms of visual processing. For example, newborns show a preference for following moving faces within the first 30 minutes of life. However, some studies have shown that visual processing of complex non-face shapes happens in the same area of the brain as facial recognition. This implies it may be complexity, rather than the face per se, that influences visual processing in a distinct way.
The major problem with the Gestalt laws (and the Gestalt school generally) is that they are descriptive not explanatory. For example, one cannot explain how humans see continuous contours by simply stating that the brain "prefers good continuity". Computational models of vision have had more success in explaining visual phenomena and have largely superseded Gestalt theory. More recently, the computational models of visual perception have been developed for Virtual Reality systems—these are closer to real life situation as they account for motion and activities which are prevalent in the real world. Regarding Gestalt influence on the study of visual perception, Bruce, Green & Georgeson conclude:
In the 1970s, David Marr developed a multi-level theory of vision, which analyzed the process of vision at different levels of abstraction. In order to focus on the understanding of specific problems in vision, he identified three levels of analysis: the computational, algorithmic and implementational levels. Many vision scientists, including Tomaso Poggio, have embraced these levels of analysis and employed them to further characterize vision from a computational perspective.
The computational level addresses, at a high level of abstraction, the problems that the visual system must overcome. The algorithmic level attempts to identify the strategy that may be used to solve these problems. Finally, the implementational level attempts to explain how solutions to these problems are realized in neural circuitry.
Marr suggested that it is possible to investigate vision at any of these levels independently. Marr described vision as proceeding from a two-dimensional visual array (on the retina) to a three-dimensional description of the world as output. His stages of vision include:
Transduction is the process through which energy from environmental stimuli is converted to neural activity for the brain to understand and process. The back of the eye contains three different cell layers; Photoreceptor layer, Bipolar cell layer and Ganglion cell layer. The photoreceptor layer is at the very back and contains rod photoreceptors and cone photoreceptors. Cones are responsible for color perception. There are three different cones: red, green and blue. Photoreceptors contain within them photopigments, composed of two molecules. There are 3 specific photopigments (each with their own color) that respond to specific wavelengths of light. When the appropriate wavelength of light hits the photoreceptor, its photopigment splits into two, which sends a message to the bipolar cell layer, which in turn sends a message to the ganglion cells, which then send the information through the optic nerve to the brain. If the appropriate photopigment is not in the proper photoreceptor (for example, a green photopigment inside a red cone), a condition called color vision deficiency will occur.
Transduction involves chemical messages sent from the photoreceptors to the bipolar cells to the ganglion cells. Several photoreceptors may send their information to one ganglion cell. There are two types of ganglion cells: red/green and yellow/blue. These neuron cells consistently fire—even when not stimulated. The brain interprets different colors (and with a lot of information, an image) when the rate of firing of these neurons alters. Red light stimulates the red cone, which in turn stimulates the red/green ganglion cell. Likewise, green light stimulates the green cone, which stimulates the red/green ganglion cell and blue light stimulates the blue cone which stimulates the yellow/blue ganglion cell. The rate of firing of the ganglion cells is increased when it is signaled by one cone and decreased (inhibited) when it is signaled by the other cone. The first color in the name if the ganglion cell is the color that excites it and the second is the color that inhibits it. i.e.: A red cone would excite the red/green ganglion cell and the green cone would inhibit the red/green ganglion cell. This is an opponent process. If the rate of firing of a red/green ganglion cell is increased, the brain would know that the light was red, if the rate was decreased, the brain would know that the color of the light was green.
Theories and observations of visual perception have been the main source of inspiration for computer vision (also called machine vision, or computational vision). Special hardware structures and software algorithms provide machines with the capability to interpret the images coming from a camera or a sensor. Artificial Visual Perception has long been used in the industry and is now entering the domains of automotive and robotics.
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