A Neural Model for Detecting and Labeling Motion Patterns in Image

Sequences

Marc Pomplun1

Julio Martinez-Trujillo2

Yueju Liu2

Evgueni Simine2

John Tsotsos2

1UMass Boston2York University, Toronto, Canada

“Data Flow Diagram”of Visual Areas inMacaque Brain

Blue:motion perception pathway

Green:object recognition pathway

Receptive Fields in Hierarchical Neural Networks

neuron A

receptive field of A

Receptive Fields in Hierarchical Neural Networks

receptive field of A in input layer

neuron Ain top layer

poor localization

crosstalk

Problems with Information Routing in Hierarchical Networks

The Selective Tuning Concept (Tsotsos, 1988)

processingpyramid

inhibited pathways

passpathways:hierarchicalrestriction of input space

unit of interestat top

input

top-down, coarse-to-fine WTA hierarchy for selection and localization

unselected connections are inhibited

WTA achieved through local gating networks

Hierarchical Winner-Take-All

unit and connectionin the interpretive network

unit and connectionin the gating network

unit and connectionin the top-down bias network

B+1,k

U+1, k

I,k

-1,j

,k,jG

g,kb,k

M,k

I+1,x

}

layer +1

layer -1

layer

I

Selection Circuits

3D Visualization of the Selective Tuning NetworkRed: WTA phase 1

activeGreen: WTA phase 2 activeBlue: inhibition Yellow: WTA winner

The Motion Perception Pathway

MST

MT

V1

feed- forward

feed- forward

feedback

input

feed- forward

feedback

feedback

What do We Know about Area V1?• cells have small receptive fields• each cell has a preferred direction of motion

direction of motion

act

ivati

on preferred direction

• there are three types of motion speed selectivity

speed of motion

act

ivati

on low-speed cells

medium-speed cells

high-speed cells

What do We Know about Area MT?• cells have larger receptive fields than in V1• like in V1, each cell has a preferred combination

of the direction and speed of motion• MT cells also have a preferred orientation of the

speed gradient

orientation of speed gradient

act

ivati

on

preferred orientation of speed gradient

without speed gradient

with speed gradient

What do We Know about Area MST?

• cells respond to motion patterns such as– translation (objects shifting positions)– rotation (clockwise and counterclockwise)– expansion (approaching objects)– contraction (receding objects)– spiral motion (combinations of rotation and

expansion/contraction)

• the response of a cell is almost independent on the position of the motion pattern in the visual field

The Motion Hierarchy Model: V1• V1 receives image sequences as input and extracts the

direction and speed of motion

counterclockwise rotationclockwise rotationcontractionexpansion

counterclockwise clockwise contraction expansion

The Motion Hierarchy Model: V1• V1 is simulated as 60x60 hypercolumns• each column contains 36 cells: one for each

combination of direction (12) and speed tuning (3)

• direction and speed selectivity are achieved with spatiotemporal filters

• these filters process local information from the last seven images in the sequence

• example: cells tuned towards upward motion:

input pattern: counter-clockwise rotation

high-speed cells

medium-speed cells

low-speed cells

The Motion Hierarchy Model: MT

• MT is simulated as 30x30 hypercolumns• each column contains 432 cells: one for each

combination of direction (12) speed (3), and speed gradient tuning (12)

• problem: how can gradient tuning be realized from activation patterns in V1?– solution: detect gradient differences across

the three types of speed selective cells– this solution leads to a simple network

structure and remarkably good noise reduction

• the activation of an MT cell is the product of its activation by direction, speed, and gradient

The Motion Hierarchy Model: MST

• how can MST cells detect motion patterns such as rotation, expansion, and contraction based on the activation of MT cells?

counterclockwise clockwise contraction expansion

movement speed gradient

• idea: the presence of these motion patterns is indicated by a consistent angle between the local movement and speed gradient

The Motion Hierarchy Model: MST

direction of movement

orientation of speed gradient

The Motion Hierarchy Model: MST

• MST cells integrate the activation of MT cells that respond to a particular angle between motion and speed gradient

• this integration is performed across a large part of the visual field and across all 12 directions

• therefore, MST can detect 12 different motion patterns

• we simulate 5x5 MST hypercolumns, each containing 36 neurons (tuned for 12 different motion patterns, 3 different speeds)

direction of

movement

speed gradient

V1

MT

MST

Simulation:

clockwiserotation

direction of movement

speed gradient

Simulation:

counter-clockwiserotation

direction of movement

speed gradient

Simulation:recedingobject

direction of movement

speed gradient

Attention in the Motion Hierarchy

What happens if there are multiple motion patterns in the visual input?

Visual attention can be used to• determine the type and location of the most salient motion pattern,• focus on it by eliminating all interfering information,• sequentially inspect all objects in the visual field.

direction of movement

speed gradient

direction of movement

speed gradient

Conclusions and Outlook

• the motion hierarchy model provides a plausible explanation for cell properties in areas V1, MT, and MST

• its use of distinct speed tuning functions in V1 and speed gradient selectivity in MT leads to a relatively simple network structure combined with robust and precise detection of motion patterns

• visual attention is employed to segregate and sequentially inspect multiple motion patterns

Conclusions and Outlook

• the model predicts inhibition of visual functions around any attended motion pattern

• the model also predicts that different motion patterns induce different activation patterns in V1, MT, and MST

• linear motion activates V1, MT, and MST

• speed gradients increase MT and MST activation

• rotation, expansion, and contraction increase MST activation

• this is currently being tested by fMRI scanning experiments in Magdeburg, Germany

Conclusions and Outlook

• the model is well-suited for mobile robots to estimate parameters of ego-motion

• the area MST in the simulated hierarchy is very sensitive to any translational or rotational ego-motion

• in biological vision, MST is massively connected to the vestibular system

• in mobile robots, the simulated area MST could interact with position and orientation sensors to stabilize ego-motion estimation

Conclusions and Outlook

Future work:

• lateral interaction across neighboring sets of gating units for improved perceptual grouping

•simultaneous simulation of both the motion perception and object recognition pathways

• introduction of working memory for an adequate internal representation of the current visual scene