Computational Intelligence Research in ECE Target 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 MURI Basic Research Technology Transfer 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 Pair3. Locate Eyelids 4. Locate Pupil Pair5. Locate Hirschberg Points / Estimate Fixation 6. Extracting Features (e.g., Crescents)

Screenshot of Program User Interface

Equine Gait Analysis Motion capture Data analysis Classification Animation for visualization Database Pre-process and 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  v A B Histogram of forces

gravitational constant -  -    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

There are 5 missile launchers (1, 2, 3, 6, 8) They surround a center vehicle (4) The image includes a SAM site A 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 position of the red object(s) with respect to the group of buildings (in blue).

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 MATCHING IS DIFFICULT overhead view ? overhead view ? THIS WOULD BE 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?

Spatial Language for Human-Robot Dialog Human:“What do you see?” Robot:“There are objects on my front left. I am surrounded from the rear. The pillar is mostly in front of me but somewhat to the left.”

PATH DESCRIPTION GENERATED FROM THE SKETCHED ROUTE MAP 1. When table is mostly on the right and door is mostly to the rear (and close) Then Move forward 2. When chair is in front or mostly in front Then Turn right 3. When table is mostly on the right and chair is to the left rear Then Move forward 4. When cabinet is mostly in front Then Turn left 5. When ATM is in front or mostly in front Then Move forward 6. 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 Maps

Sketch-Based Navigation The sketched route map The robot traversing the sketched route

Sketch-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 green Based 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