Transcript Session 6

Perception, Illusion and VR
HNRS 299, Spring 2008
Lecture 6
Seeing Motion
1
Causes of Image Motion
Image motion can result from numerous causes:
• A moving object in the scene
• Eye movements
• Motion of the observer
2
Uses of Image Motion
Image motion on the retina can be used to compute a variety of
scene properties. Among them are:
• Image segmentation (dividing up the scene into individual
objects or surfaces)
• 3D structure of an object (structure from motion)
• Depth (motion parallax)
• Time to collision
• Heading direction
• Moving object direction
• Speed of eye movements (for smooth pursuit)
3
Two stages of Motion
processing
Visual motion processing is thought to occur in two stages:
1) Extract the 2D image velocity field.
2) Use the 2D velocity field to compute properties of the scene
(as listed in the previous slide).
4
Motion Detection by Neurons
Problem:
• A single photoreceptor (or retinal ganglion cell) cannot detect
motion unambiguously.
•
A spot of light moving across its receptive field will cause a
temporary increase in light followed by a decrease.
•
The photoreceptor cannot distinguish between motion or
changes in ambient lighting.
5
A Neural Circuit for Motion
Barlow and Levick proposed a model to compare the response at one
location with a delayed response at a neighboring position.
1.
2.
3.
4.
Prefers right motion
Each neuron has a receptive field that
is shifted to the left with respect to
the receptive field of the previous
neuron.
Each neuron causes the neuron with a
RF to its left to be inhibited a short
time after the first neuron is
stimulated.
The neurons in the circuit will not
respond to leftward motion because
of this inhibition.
They will still respond to rightward
motion.
6
Dorsal and Ventral Streams
7
Motion Processing in V1
In V1, some simple cells and complex cells are tuned to
direction of motion. I.e. they respond most strongly to motion
in a given direction and their response falls off as the motion
deviates from that direction.
Tuning for 180 deg
Firing
Rate
120o
180o 240o
Direction of Motion
Direction Tuning
Polar Plot
(tuning for zero deg)
8
Motion Processing in MT
MT (The Middle Temporal Area) is thought to be important for
processing motion information.
Characteristics of MT neurons:
1) Cells tuned for direction of motion (more broadly tuned than
V1 cells.
2) Cells tuned for speed.
3) Large receptive field sizes. (Some are 100x bigger than V1
receptive fields). They range from 1-2deg in diameter in the
foveal region and increase in the periphery.
9
Speed Selectivity
McKee and Nakayama have shown that people are very good at
discriminating two different speeds.
The Weber fraction gives a measure of how big a change in speed
is necessary to distinguish two different speeds. It is fairly
constant over a broad range of speeds:
DV/V = .05
MT may be the area that first computes speed.
10
The Aperture Problem
•If our view is limited to an edge seen through an aperture, we can
only find the component of motion perpendicular to the edge.
•The aperture problem is a fundamental problem when one is trying
to measure image velocity using local detectors.
•This is true in biological vision (neurons have local receptive fields).
•In the "barber pole" illusion, the edges of the aperture affect
perception.
http://www.aceviper.net/Optical2/barber_pole_illusion.htm
Aperture
Edge
Perpendicular velocity
component
11
Motion Adaptation
•MT cells do not respond if opposite directions of motion (e.g.
left and right) presented at the same time. This is called motion
opponency.
•The detectors for opposite directions balance each other.
The waterfall effect:
•If you view one direction of motion for a long time, the detectors
for that direction become fatigued.
•If you then look at a stationary surface, it will appear to move in
the opposite direction.
•This works for expansion and contraction as well.
•Demo:
http://www.michaelbach.de/ot/mot_adaptSpiral/index.html
12
2D Motion is just the Beginning
2D image motion contains information about:
• Relative depth of surfaces
• 3D motion of objects
• 3D structure of objects
• Direction of observer motion
Among other things.
Many of these tasks require local comparisons of neighboring motions.
13
Motion Parallax
•For an observer moving in a straight line, the images of objects that are
nearby move faster than the images of objects that are far away.
•This is the result of perspective projection.
•The difference in image speed, known as motion parallax, is a strong
cue for the relative depth of objects.
•A moving observer can find changes in depth in the scene by finding
changes in image speed.
object 1
object 2
14
MT cells have inhibitory
surround
-
Many MT cells have an inhibitory surround.
+
Motion in the surround inhibits the response to
motion in the center.
The inhibitory surround may be involved in:
• Figure-ground segmentation based on motion.
• Motion parallax
• Heading judgments (judging where one is going).
15
Structure From Motion
•Relative motion can give information about the 3D structure of
objects.
•Structure from motion originally studied rigorously by Wallach
and O'Connell (1953).
•They studied wire-frame objects and examined peoples ability to
judge the structure of the objects when moving.
•The ability to see a 3D structure from a moving 2D image is
known as the Kinetic Depth Effect.
Demo: http://www.michaelbach.de/ot/mot_ske/index.html
Quick Time™a nd a
TIFF ( Unco mpre ssed ) dec ompr esso r
ar e nee ded to see this pictur e.
16
Detecting Biological Motion
Johansson attached points of light to the joints of people and
filmed them walking.
He showed that when the lights are in motion, people can
recognize the actions being performed.
Demo: http://www.michaelbach.de/ot/mot_biomot/index.html
Relative motion of the points of light is certainly important for
recognizing the actions.
17
Using Motion to See Where
You're Going
When one moves in a straight line, the images on the retina move in a radial
pattern.
The center of the pattern coincides with the direction you're going.
When we move on a curved path, the computation is more difficult (and
involves relative motion), but we can still judge our path of motion.
(Viperlib demo)
18