Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
1.2.4. Photorealism
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From the course by University of Illinois at Urbana-Champaign
Data Visualization
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University of Illinois at Urbana-Champaign
716 ratings
Course 1 of 6 in the Specialization Data Mining
From the lesson
Week 1: The Computer and the Human
In this week's module, you will learn what data visualization is, how it's used, and how computers display information. You'll also explore different types of visualization and how humans perceive information.
Meet the Instructors
John C. HartProfessor of Computer Science
Department of Computer Science
When we use three dimensional graphics for data visualization, we're going to need to
add photorealism in order to cue us to the three-dimensional spacial configuration
of shapes that are being displayed to us in a two-dimensional image.
So photorealism describes visual cues that tell our perceptual system that when it
see a two-dimensional image, it's seeing a depiction of a three dimensional scene.
Some of these cues are occlusion.
Occlusion is shown here, the fact that this boys hand is in front of the frame.
It's start to trigger your perceptual system to think that this is
a three-dimensional scene of a boy climbing out of
the frame instead of a two-dimensional image of a boy on a frame.
Other things like perspective, the fact that this water's receding into
the horizon and details are getting closer together in the distance.
Others like lighting,
the light bouncing off the forehead of this boy or attenuation that
the water is becoming whiter as the fog is attenuating the light.
All of these add cues, so that your perceptual system that's looking at
a two-dimensional image can understand that.
It's actually a three-dimensional scene that's being depicted.
One of the strongest cues of these is occlusion.
For example, this red-orange rectangle is in front of this blue rectangle,
because you can see all of this red-orange rectangle.
You can't see all of this blue rectangle,
because it's being occluded and this sets up a depth ordering.
We don't know how much closer the red-orange rectangle is to us from
the blue rectangle, but we do know that it is closer.
An occlusion is the strongest cue and
it is fun to play with it with optical illusions, because of that strength.
Illumination helps us perceive the orientation of surfaces and
there's two kinds of illuminations that we tend to use.
One is diffuse illumination,
which is brightest when a surface is facing a light source.
And we usually see this from soft objects, rough objects, things like cloth or
pencil erasers or other surfaces that are scattering light in many directions.
And then there's specular illumination and that adds these white highlights and
we usually see these on shiny objects, metallic objects.
And these highlights are added in regions of the surface that are facing in
a mirror reflection that would reflect the light source towards the eye.
And so, we can combine these when we're doing data visualization.
This is an example of particles that are leaving trails to show the simulation
of the flow of blood in the abdominal aorta and we're using certain cues.
For example, occlusion that some of these trails are in front of other trails as you
can see this green trail is in front of the blue trail.
We're using lighting, because you can see that these trails are cylindrical.
They have a brighter side and the darker side, because of diffused sliding.
Also loosing perspectives, we see larger things here and
smaller things more detailed at the back.
And all of those add up to give you better sense of the shape of this aorta and
the blood flow inside of it.
There's some other cues that are important.
One is shadowing.
Shadowing is an inclusion cue from a second viewpoint where that second
viewpoint is at the light source.
So here, I've got two two-dimensional images.
In both cases, we can think of these due to perspective as a flat blue
plain with a red-orange ball on top of it, but
we don't know if this red orange ball is hovering over the middle of the plain or
if it's laying on the plain toward the back of it.
If I had a shadow, you can see on the left case here,
the red orange ball is laying on the blue plane towards its back.
But in this case, which is the exact same image I've just changed the shadow,
we can see that this blue ball is hovering over the middle of the blue square.
And so in, for example, this visualization of a molecule,
you can use these shadows to get a better sense of depth due to the directions
that the shadows are being cast from in understood light source direction.
There's also a perspective in addition to being able to see more detail
far away with perspective.
Perspective is based on size constancy.
That objects are the same size, but farther ones appear smaller.
You don't think that the object is changing its size.
You understand that the visual system is looking at a larger area
that's further away, because of the way it gets projected onto our retina or
onto our image plane in the case of computer graphics.
In computer graphics, the way we do this is we actually make things that
are farther away smaller and make things that are closer to us larger by
actually moving the vertices and then projecting onto a projection plane.
And so to get this perspective view where you've got the front of the table larger
than the back of the table, what we've actually done is actually made
the primitives of the table, larger in the front and smaller at the back.
But computer graphics does this for you automatically, so
you don't actually have to do this.
And so these examples are demonstrated in this animation from
the National Center for Supercomputing Application on an F3 tornado.
And you can see, we're using perspective.
The perspective allows us to see the glyphs in the front of this visualization
and also the patterns that these glyphs form as you go farther away.
And so this is a storm chaser view and you can see the occlusions of these arrows, so
you can get a sense and the arrows are larger in the front and
smaller in the back.
So you can get a sense of the depth of the simulation of this tornado and the paths
that these particles are tracing, and also we see shadows on the ground.
Shadows being cast of one primitive to another and
also the lighting that these primitives are illuminated on one side
better than they're illuminated or more brightly than on another side
to give you a sense that the shape of these arrows are cylindrical.
Also, as the storm chaser moves through the dataset,
things in the front are moving by the viewer faster than things in the back.
That's another 3D cue called motion parallax.
And finally, there's stereo.
We can use stereo in the absence of other cues.
In this case, I was just plotting points of a mathematical equation to create
some shapes and the way you visualize this is you hold your fingers up
in front of your two eyes and right across your eyes.
So that your right eye is looking at the left image and
your left eye is looking at the right image, basically by focusing on the finger
until those two images in the background start to merge together.
If you do that, then you get a stereogram and this two-dimensional set of points on
this image plane should pop-out into a three-dimensional configuration.
If you want to simulate this, then you can simulate this with two separate views.
A view from a left eye and a view from a right eye,
looking in parallel at the data.
Just make sure you don't rotate those views.
You want to make sure the views are in parallel.
So, what did we learn?
We learned that occlusion is the strongest of all the depth cues and
that shadowing is an occlusion from a viewpoint at the light source.
Illumination is useful for
surface orientation that helps reveal details and perspective gives us
different scales of visualization in addition to adding depth perception.
And when none of these cues are available to us,
sometimes some of the other cues can be used.
For example, stereo.
We can use stereo in the absence of other cues.
In this case, I was just plotting points of a mathematical equation to create some
shapes.
And the way you visualize this is you hold your finger up in front of your two eyes
and try to close your eyes, so that your right eye is looking at the left image and
your left eye is looking at the right image.
Basically, by focusing on the finger until those to images in the background start to
merge together.
If you do that, then you get a stereogram and this two-dimensional set of points on
this image plane should pop-out into a three-dimensional configuration.
If you want to simulate this, then you can simulate this with two separate views.
A view from a left eye and a view from a right eye looking in parallel at the data.
Just make sure you don't rotate those views.
You want to make sure the views are in parallel.
We learn that occlusion is the strongest of these depth cues and
we learned that shadows are just occlusion from a light source.
We also learned that illumination cues us to the orientation of surfaces and
we also learned that perspective,
allows us to visualize multiple scales in the same image.
[MUSIC]
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