The resulting perception is also known as
visual perception, 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 linguistics, psychology, cognitive science, neuroscience, and
molecular biology, collectively referred to as vision science.
Visual system
The visual system in animals allows
individuals to assimilate information from their surroundings. The act of
seeing starts when the cornea and then the lens of the eye focuses light from
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 light into neuronal signals. Based on
feedback from the visual system, the lens of the eye adjusts its thickness to
focus light on the photoreceptive cells of the retina, also known as the rods
and cones, which detect the photons of light and respond by producing neural
impulses. These signals are processed via complex feedforward and feedback
processes 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 octopuses, mollusks, 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 perception of objects and the totality
of the visual scene is accomplished by the visual association cortex. The
visual association cortex combines all sensory information perceived by the
striate cortex which contains thousands of modules that are part of modular
neural networks. The neurons in the striate cortex send axons to the
extrastriate cortex, a region in the visual association cortex that surrounds
the striate cortex.
The human visual system perceives visible
light in the range of wavelengths between 370 and 730 nanometers (0.00000037 to
0.00000073 meters) of the electromagnetic spectrum.
Study
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.
Early studies
The visual dorsal stream (green) and
ventral stream (purple) are shown. Much of the human cerebral cortex is
involved in vision.
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. (In eighteenth-century England
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: The eye has a central
line and everything that reaches the eye through this central line can be seen
distinctly.
Alhazen (965 – c. 1040) carried out many
investigations and experiments on visual perception, extended the work of
Ptolemy on binocular vision, and commented on the anatomical works of Galen. He
was the first person to explain that vision occurs when light bounces on an
object and then is directed to one's eyes.
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.
Issac Newton (1642–1726/27) was the first
to discover through experimentation, by isolating individual colors of the
spectrum of light passing through a prism, that the visually perceived color of
objects appeared due to the character of light the objects reflected, and that
these divided colors could not be changed into any other color, which was
contrary to scientific expectation of the day.
Unconscious inference
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:
light comes from above
objects are normally not viewed from below
faces are seen (and recognized) upright.
closer objects can block the view of more
distant objects, but not vice versa
figures (i.e., foreground objects) tend to
have convex borders
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. However, it is not clear how proponents of this
view derive, in principle, the relevant probabilities required by the Bayesian
equation. 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 theory
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 a German
word that partially translates to "configuration or pattern" along
with "whole or emergent structure". According to this theory, there
are eight main factors that determine how the visual system automatically
groups elements into patterns: Proximity, Similarity, Closure, Symmetry, Common
Fate (i.e. common motion), Continuity as well as Good Gestalt (pattern that is
regular, simple, and orderly) and Past Experience.
Analysis of eye movement
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 right 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 four
different types of eye movements: fixations, vergence movements, saccadic
movements and pursuit movements. Fixations are comparably static points where
the eye rests. However, the eye is never completely still, but gaze position
will drift. These drifts are in turn corrected by microsaccades, very small
fixational eye-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 makes jumps from one position to another position and is used to
rapidly scan a particular scene/image. Lastly, pursuit movement is smooth eye
movement and is used to follow objects in motion.
Face and object recognition
There is considerable evidence that face
and object recognition are accomplished by distinct systems. For example,
prosopagnosic patients show deficits in face, but not object processing, while
object agnosic patients (most notably, patient C.K.) show deficits in object
processing with spared face processing. Behaviorally, it has been shown that
faces, but not objects, are subject to inversion effects, leading to the claim
that faces are "special". Further, face and object processing recruit
distinct neural systems. Notably, some have argued that the apparent
specialization of the human brain for face processing does not reflect true
domain specificity, but rather a more general process of expert-level
discrimination within a given class of stimulus, though this latter claim is
the subject of substantial debate. Using fMRI and electrophysiology Doris Tsao
and colleagues described brain regions and a mechanism for face recognition in
macaque monkeys.
The cognitive and computational approaches
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:
A 2D or primal sketch of the scene, based
on feature extraction of fundamental components of the scene, including edges,
regions, etc. Note the similarity in concept to a pencil sketch drawn quickly
by an artist as an impression.
A 2½ D sketch of the scene, where textures
are acknowledged, etc. Note the similarity in concept to the stage in drawing
where an artist highlights or shades areas of a scene, to provide depth.
A 3 D model, where the scene is visualized
in a continuous, 3-dimensional map.
Marr's 2.5D sketch assumes that a depth map
is constructed, and that this map is the basis of 3D shape perception. However,
both stereoscopic and pictorial perception, as well as monocular viewing, make
clear that the perception of 3D shape precedes, and does not rely on, the
perception of the depth of points. It is not clear how a preliminary depth map
could, in principle, be constructed, nor how this would address the question of
figure-ground organization, or grouping. The role of perceptual organizing
constraints, overlooked by Marr, in the production of 3D shape percepts from
binocularly-viewed 3D objects has been demonstrated empirically for the case of
3D wire objects, e.g. For a more detailed discussion, see Pizlo (2008).
Transduction
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. Rods, are responsible for the
perception of objects in low light. Photoreceptors contain within them a
special chemical called a photopigment, which are embedded in the membrane of
the lamellae; a single human rod contains approximately 10 million of them. The
photopigment molecules consist of two parts: an opsin (a protein) and retinal
(a lipid). 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.
Opponent process
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
constantly 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
of 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.
Artificial visual perception
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.
Source From Wikipedia
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