Computational Intelligence Research in ECETarget detection and

Recognition

Linguistic Scene Description

Sketch Understanding

What’s Coming?

Jim Keller, Marge Skubic, Dominic Ho and others

Robot Spatial Reasoning

Scene Matching

Computational Intelligence Technologies

• Fuzzy Set Theory and Fuzzy Logic

• Neural Networks

• Probabilistic Reasoning – Including Evolutionary Computation and Genetic Algorithms

• To Build Intelligent Systems for:– Image/Signal Processing

– Pattern Recognition

– Robotics

• Strong Applications Orientation

Object Detection and Recognition

Large long-term effort in Landmine Detection

Forward-looking GPR

Seismic/Acoustic Sensing

Technology Transfer from Basic Research to Fielded Systems

Army Research Office MURIBasic Research

TechnologyTransfer

HSTAMIDS GSTAMIDS

Robotic Tripwire Detection

Little Bob

Morphological Shared Weight Neural Networks for Object Recognition

First Developed to Find Object (Blazer) in Visible Imagery

Original Frame

Output Plane

Target Aim Point Selection

Final Output

All Twelve Targets Detected with no

False Alarms

Target Detection in SAR Imagery

Application to Tank Detection in (Processed) LADAR Range Images

Trained on 2 frames from one sequence (8 instances)

Tested on Different Flight Sequence

Face Recognition (Homeland Security)

Typical Training Image of Bob

Examples of “Bob” Detection

Even with Glasses on

Automated Amblyopia Screening Assistant

1. Input Image (from video sequence)

2. Locate Iris Pair 3. Locate Eyelids

4. Locate Pupil Pair 5. Locate Hirschberg Points / Estimate Fixation

6. Extracting Features(e.g., Crescents)

Screenshot of Program User InterfaceSelect Patient Step through images

Process all images of a patient

Get summaryof sequence

Intermediateimages to view

Intermediate image - irises found

Intermediate image - pupils found

Text feedback

Result of all images Sequence summary dialog

Equine Gait AnalysisMotion capture Data analysis

Classification

Animation for visualization

Database

Pre-processand store

normal dysfunctional

Scene Description

• Natural scene understanding is an important aspect of computer vision

• Spatial relations among image objects play a vital role in the description of a scene

2 3 41

F

A B

( )rA B

v

A

B

Histogram of forces

gravitational

constant

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-

3

2

1

1

3. A system of 27 fuzzy rules and meta-rules allows meaningful linguistic descriptions to be produced.

1. Each histogram gives its opinion about the relative position between the objects that are considered.

2. The two opinions are combined. Four numeric and two symbolic features result from this combination.

Linguistic Scene Description

Linguistic Scene Description

There are 5 missile launchers (1, 2, 3, 6, 8)They surround a center vehicle (4)The image includes a SAM siteA convoy of vehicles (5, 7, 9, 10) is BelowRight of the SAM site

Scene Description and Recognition

Loosely ABOVE-LEFT.

The system here describes the relative positionof the red object(s) with respect to the group of buildings (in blue).

Do These Images Contain The Same Power Plant ?Do These Images Contain The Same Power Plant ?

Image 1

Image 2

Another Question : If they are indeed the same power plant, which building(s) that appear on Scene 1, also appear on Scene 2 ? (labeling problem)

DIRECT MATCHINGIS DIFFICULT

overhead view

?

overhead view

?

THIS WOULDBE EASIER

Scene Matching Using Fuzzy Regions

The only true matching got the highest matching degree

3,628,800 ways to match the two scenes.

Scene Matching and Recovery of View Parameters

Human/Robot Dialog• Spatial Reasoning incorporated into NRL’s Natural

Language Understanding System for mobile robots

• Sensed data results in a “grid map” that displays occupancy of cells (doesn’t need to be binary)

Grid map after component labeling – robot heading towards Object 5

DETAILED SPATIAL DESCRIPTIONS for 6 OBJECTS:

•Object 1 is mostly behind me but somewhat to the right (the description is satisfactory). The object is very close.

•Object 2 is behind me (the description is satisfactory) The object is very close.

•Object 3 is to the left of me but extends to the rear relative to me (the description is satisfactory). The object is very close.

•Object 4 is mostly to the right of me but somewhat forward (the description is satisfactory). The object is very close.

•Object 5 is in front of me (the description is satisfactory). The object is very close.

•Object 6 is to the left-front of me (the description is satisfactory). The object is close.

Scene 1

High-Level Description

There are objects in front of me and behind me.

Object number 3 is to the left of me.

Object number 4 is mostly to the right of me.

What do you see, Roby?

PATH DESCRIPTION GENERATED FROM THE SKETCHED ROUTE MAP1. When table is mostly on the right and door is mostly to the rear (and close) Then

Move forward2. When chair is in front or mostly in front Then Turn right3. When table is mostly on the right and chair is to the left rear Then Move forward4. When cabinet is mostly in front Then Turn left5. When ATM is in front or mostly in front Then Move forward6. When cabinet is mostly to the rear and tree is mostly on the left and ATM is mostly in front Then Stop

Understanding Sketched Route MapsUnderstanding Sketched Route Maps

Sketch-Based NavigationSketch-Based Navigation

The sketched route mapThe robot traversing the sketched route

Sketch-Based NavigationSketch-Based Navigation

The digitized sketched route map

The robot traversing the sketched route

Identification of Spatial Regions

“GORT, go behind object #3”

Region shown in greenBased on Histograms of Forces

Centered here

Future work: combine ATR with spatial language

Cognitive Robotics:Collaborative Research with Vanderbilt Univ.

Funded by NSF

What’s Coming? (Here, Actually)

Robot Skill Acquisition: Teaching Robonaut New Skills

What’s Coming? (Proposed)

What’s Coming? (We Hope)

Sensor-loaded (Sensor nets)

Intelligent Action (Reasoning)

Cooperative Behavior

Power Issues

Communication

Hardware and Systems Concerns

Mini-, Micro-, Nano- and Bio-Scales

Potential Cast of Characters:

Jim, Marge, Henry, Lex, Shubhra, Randy, Mike, Rusty, Dominic, Yi (CS), Sheila (BAE), …

Mobile Robot Teams Research Laboratory