SUMMARY DECLUTTERING CLUSTERINGACDC Outline Automated Change Detection and Classification (ACDC) System Computer-Aided Detection (CAD), Classification

SUMMARYDECLUTTERING CLUSTERINGACDC

Outline Automated Change Detection and

Classification (ACDC) System Computer-Aided Detection (CAD),

Classification (CAC), Search (CAS), and Change Detection.

Clustering NRL 6.2 FY05 New Start

Automated declutter mechanism for electronic displays

Summary


ACDC Ability to automatically detect /

classify / identify objects in imagery and perform change detection

Current project: Side-scan imagery (SSI) for mine counter-measures (MCM)

Future/potential applications Real-time Imagery in Cockpit (RTIC) ECDIS Weather / meteorological Common Operational Picture (COP)

ACDC


Change Detection (using SSI)1. Detect seafloor features

(shadows, bright spots)

2. Classify detections (mines, rocks, sand waves, etc.)

3. Search historical database (position error)

4. Match new feature (to ideal features)

5. Perform area matching (uses clustering)

6. Identify features that don’t match: change detection

ACDC


MILECs found by:

Bright spots Sizes Shapes Type of Mines

Shadows Length (look angle) Correct side Proximity to bright

spotMine-Like Echoes (MILECs) Automatically detected in SSI

Computer Aided Detection

ACDC


000000000000000000000000000000 000000000000000000000000000000 000000000000000000000000000000 000000000000000000000000000000 000000000000000000000000000000 000000000000000000000000000000

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00000010000000000000000

00000000000000000000000

00011100000000000000000

00000001111111111100000

00000000000000000000000

00000000000000000000000

00001100000000000000000

00000001111111111100000

SSI stored in UNISIPS format as separate records (Lat/Lon, Altitude, Heading).

Shadow Bitmap

Thresholded using a hard limiting transfer function to find Shadows:

a = func(n) | a = 1 if a < max_shadow_value

Bright Spot BitmapThresholded using a hard limiting transfer function to find Bright Spots:

a = func(n) | a = 1 if a > min_bright_spot_value

Shadows & Bright Spots Marked

Real-Time CADACDC


CAD SNIPPET

ADAPTIVE FILTERING

AUTO-COMPLETE

ACDC

Computer Aided Classification


(historical survey area)

Historical SSI Database

Classification

Attributes

Imagery

Snippets Snippets

Features

ACDC


Vector Searchable Database(Handles position error!)

CAD / CAC new features (N) in areas where historical features (H) exist

Populate search database with H’s

Query search database for each N

Historical:

New:

Spatial Query

“ANDing” position error

ellipses

ACDC

N = N1, N2, …, Nn

H = H1, H2, …, Hn

Results: N1 = H3 | H10

H10

H3

N1

H2


Wavelet Networks for Feature Matching

y(x,y) = Σ ask wsk(x,y)

ask = wavelet coefficients

wsk = basis functions

Neural Network

EXAMPLE:

ACDC

Training Set

rectangle rectangle rectangle rectangle rectangle

rectangle rectangle rectangle rectangle triangle

triangle triangle triangle triangle triangle

triangle circle circle circle circle

circle circle circle unknown unknown

H10N1


NEW

Historical DatabaseNew Survey Data

Cluster Region

ACDC

Wavelet Networks for Area Matching

H2H5

H10

N4

N3

N2

N1

N1

N2

N3

N4


Clustering for MCM NRL has transitioned new algorithm to NAVO

to cluster MILECs detected in SSI, in support of MCM efforts.

I. Detect / classify / search for / identify mine-like objects (MILECs) in SSI.

III. Smooth clusters, calculate density.

12

3

MILECs Clutter/ km2 categoryx < 4 1

4 < x < 12 2x > 12 3

II. Cluster objects into regions.


Clustering algorithm NRL algorithm clusters mine-like objects

detected in SSI

NRL 7440.1 invention disclosed June 2003.

Uses geospatial bitmapping technique patented by Code 7440.1 in 2001 (U.S. Patent 6218965).

Unique method of clustering objects in 2D / 3D space: computationally efficient, single-pass, repeatable, operates on user-defined space, autonomous.

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Longitude (X)

Latit

ude

(Y)

Collection of points in geographic space (here, 2D)

Represent points as a geospatial bitmap. Each bit containing a point is “set;” all other bits are cleared. Bit size depends on scale.

“Grow” each set bit (representing each point) by setting the surrounding bits to form a predefined expansion shape.

Expansion shape dictated by data characteristics and user requirements.

Can use any shape that can be mathematically defined.

Size of expansion shape determines density of resultant clusters.

Points that are geographically close to each other will grow or cluster together. Result: new geospatial bitmaps.

Traverse each bitmap (in a consistent direction) to get vertices.

Smooth the bitmaps by dropping vertices if the resulting polygon still contains all the original points and has an

area equal to or less than the unsmoothed bitmap.

Result of final iteration. Density of region = # original points / area of smoothed polygon.


FY05 6.2 New Start Internally funded by NRL 6.2 program

(FY05-07) After FY07, will need transition

sponsors to implement in fleet systems (NGA / VVOD, NGA / DNC2, NAVAIR / TAMMAC, others)

Leveraging ongoing work from ACDC and other projects

Collaborating with Dr. Greg Trafton (Engineering Research Psychologist)

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Cluttera confused multitude of objects

Clutter as it pertains to this project: when

additional information

would result in performance degradation.


The Clutter Problem Our ability to collect data & “create” information

is outpacing our ability to use and visualize the results. Clutter in all types of electronic displays is a massive and rapidly escalating problem.

Many researchers have documented link between increased clutter and degraded performance.

E.g., in cockpit displays, visual clutter can disrupt a pilot's visual attention, resulting in greater uncertainty concerning target locations.1

1Aretz (1988), Wickens (1993), Wickens & Carswell (1995)

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Problem (cont.) Pilots want ability to declutter displays (e.g., driven by

vector-based GIS-like databases) but such “flexibility means integration complexity and added pilot workload. Pilots should be flying, not building a map!" 1

"If the map display is too cluttered, I just turn it off!" 1

Automated decluttering requires good clutter metrics. Reasonably good clutter metrics exist for text displays, but not for graphical displays.

We propose to apply NRL detection / clustering algorithms and human factors principles to quantify clutter in electronic displays.

1 Quotes of F/A-18 pilots, from Lohrenz, et al. (1999)

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Relevance to COPcharts

imagery

obstructions

terrain

NGA data

Over the past decade, the COP has grown in functional complexity … (DISA, 1998)

recon.

routes

troops

targets

C4I data

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Current declutter method

Sample display downloaded from Nobeltec company website.

Warfighter must manually remove an entire layer at once (brute-force filtering). Need a more “intelligent” way to declutter electronic displays. Warfighters and decision-makers should see all that is needed – but only what

is needed – without extraneous data obscuring critical information.

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http://www.nobeltec.com/News2/vector-levels.gif


Measuring Clutter Considerations:

Display type: aviation, meteorological, ECDIS, etc. User expertise: novice vs. expert Task: read-off, integrate, infer, working-memory

Incorporate established cognitive theory into new / enhanced clutter metrics: “Global” vs. “local” information density “Salience” (e.g., M. Zuschlag, 2004) #Colors, color contrasts among adjacent objects #Features of each “type” (points, lines, areas, text) #Clusters of features, cluster density - NRL algorithm

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Clustering Algorithm Expand technique to cluster objects in N x 2d

(2d geospatial location + other attributes) Primary challenges:

Mathematically define meaningful expansion shapes for feature layers and attributes.

Determine how clusters of various display feature types (points, lines, areas, text) interact with each other.

Apply established theories of human visual attention and search strategies to our methodology.

Bound the problem: focus on clustering features in NGA Vector Product Format (VPF) databases Standard database for many DoD applications Can be tailored with mission-specific data sets

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Validate Clutter Metrics Can our metrics predict good display design

(subjective / user preference)? Interview technical experts Compare clutter metrics with subjective evaluations

Can our metrics predict user performance and workload? Requires experimentation … Independent variables

• Display type (aeronautical; nautical; meteorological; etc.)• Clutter metric (uncluttered very cluttered)• User expertise (novice, expert)• Task performed (read-off, integrate, infer, working memory, etc.)

Dependent variables• Performance: time, accuracy, method/logic (e.g., w/ eye-tracker)• Workload: subjective evaluation, secondary task, pupil dilation

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Summary NRL developing ACDC system with algorithms to

autonomously detect, classify, and cluster mine-like objects in SSI, and perform change detection via historical contact databases.

ACDC concepts/functions applicable to other types of imagery and objects COP.

Detection and clustering algorithms will be exploited for new NRL project (FY05-07) to develop clutter metrics for electronic displays.

Will attempt to validate clutter metrics by comparing with measures of user performance and workload, and subjective evaluations of display design.

Results should be significant for future geospatial databases, db upgrades and display designs COP.

SUMMARY

Questions???

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SUMMARY DECLUTTERING CLUSTERINGACDC Outline Automated Change Detection and Classification (ACDC) System Computer-Aided Detection (CAD), Classification