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4th European Micro-UAV Meeting
Sept. 15-17, 2004Generic Visual Perception Processor
GVPP
www.gvpp.org
Topic
- Real time processing- Detect persons, objects and events by temporal
coincidence with unsupervised collective decision - Track persons and objects in the image with anticipation
of their motions - Learning by example.
CAMERA
GenericVisualPerceptionProcessor
Action
4 adaptive properties
Introduction
Imaging processing• Perception
• Understanding• Action
GVPP
Perception Understanding Action
Power
Nb. frames/seconds
IP
GVPP
Method - 1Method - 1
Sequencer
SpatialDomain
Angle 1Angle
Angle n
Temporal Domain
Mot
ion
Col
or
Sensor
1Input signal segmentation in sequences and
sub-sequences with relation:- sequences to temporal domain
- sub-sequences to spatial domain
Subsequences
Sequence
Spatio-Temporal Neuron bloc modelisationby electronic implementation
P.P. 2002
Method - 2
Sequencer
SpatialDomain
Angle 1Angle
Angle n
Temporal Domain
Mot
ion
Col
or
Sensor
2The
sequenceis divide in
3 steps
1Input signal segmentation in sequences and
sub-sequences with relation:- sequences to temporal domain
- sub-sequences to spatial domain
INITCOMPUTATIONRESULTS
Subsequences
Sequence
Spatio-Temporal Neuron bloc modelisationby electronic implementation
P.P. 2002
Method - 3
Sequencer
SpatialDomain
Angle 1Angle
Angle n
Temporal Domain
Mot
ion
Col
or
Sensor
WHERE
Motion
D
Reg.
X/Y
D
Reg.
WHAT
2The
sequenceis divide in
3 steps
1Input signal segmentation in sequences and
sub-sequences with relation:- sequences to temporal domain
- sub-sequences to spatial domain
INITCOMPUTATIONRESULTS
Subsequences
Sequence
3Fonctional group:
temporal and spatialinput parameters
STN STN
Spatio-Temporal Neuron bloc modelisationby electronic implementation
TemporalDomain
SpatialDomain
P.P. 2002
Method - 4
Sequencer
SpatialDomain
Angle 1Angle
Angle n
Temporal Domain
Mot
ion
Col
or
Sensor
WHERE
Motion
D
Reg.
X/Y
D
Reg.
WHAT
2The
sequenceis divide in
3 steps
1Input signal segmentation in sequences and
sub-sequences with relation:- sequences to temporal domain
- sub-sequences to spatial domain
histogramcomputation
Validation
Reg.
Parameter
4Biological properties
- population analysis- majority vote- time coincidences amplification- prediction
INITCOMPUTATIONRESULTS
Subsequences
Sequence
3Fonctional group:
temporal and spatialinput parameters
STN STN
STN
Spatio-Temporal Neuron bloc modelisationby electronic implementation
TemporalDomain
SpatialDomain
P.P. 2002
Decisionmaking
Tim
eC
oinc
iden
ces
A generic Spatio-Temporal Neuron
REG REG
REG
RegistersRegistersAPI
Bus
par.
Time-Coincidences Bus
PARAM
D
FoGReg.
API
F: automatic ClassificationG: Anticipation
STN block
Self Action
STN
MVT
STN
X-YFoG
Initial ROI Final ROI
NBPTS
Z
TEMPORAL DOMAIN SPATIAL DOMAIN
MOTION
WHAT and WHERE*
Self Organization - 1
• Motion PerceptionX/Y
D
Reg.
MVT
D
Reg.
Z 0 MVT 0
Z 0
BAR z0
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
Self Organization - 2
• Recruitment
Color Analysis
X/Y
D
Reg.
MVT
D
Reg.
Col.
D
Reg.
Z 0
MVT 0
C 0
Z 0
BAR z0
BAR z0
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
Self Organization - 3
• Main Color Found
• Inhibition
No Main Color Analysis
On the Main Area
• Tree generation
For Labeling
X/Y
D
Reg.
Col.
D
Reg.
X/Y
D
Reg.
Z 0
BAR z0
Z 1
BAR z1 01
Z 0
C 0
BAR z0
BAR z1
01
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
HIGHER SPATIAL RESOLUTION
Spatial Domain
Self Organization - 4
• Improvement
X/Y
D
Reg.
X/Y
D
Reg.
Col.
D
Reg.
X/Y
D
Reg.
Z 0
BAR z0
Z 1
BAR z1 01 02
Z 2
Z 2
Z 1
Z 0
C 0
Z 0
C 0
Z 1
BAR z0
BAR z1 BAR z2
01 02
MVT
D
Reg.
MVT 1
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
HIGHER SPATIAL RESOLUTION
Spatial Domain Temporal Domain
Self Organization - 5
• Face Organization
Labeled and learned
• Perception/SynthesisX/Y
D
Reg.
X/Y
D
Reg.
Col.
D
Reg.
X/Y
D
Reg.
BAR z0
BAR z1 BAR z2 BAR z3
01 02 03
MVT
D
Reg.
MVT 1
X/Y
D
Reg.
MVT
D
Reg.
MVT 2
Z 0
BAR z0
Z 1
BAR z1 01 02
Z 2
Z 3
03
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
HIGHER SPATIAL RESOLUTION
Spatial Domain Temporal Domain
A LONG STORY
• 1986 2000• One statistic computation One System on Chip with 23 statistics computations
Lines Perception
X/Y
D
Reg.
OE
D
Reg.
Z 0 OE 0
LOW SPATIAL RESOLUTION
Spatial Domain Temporal Domain
OE
Q 1 2
GVPP7-BMemoryVRAM
CMOS Imager
PC
I
M
Screen
VIDEO BUS
I2C
RS
232
I/O
Quartz
MemoryFlash
CK
Res
et
Pow
er
Re
tro
-act
ion
D.
GVPPChip
GVPP-7B
4
2
2 2 8 1 1 4 50 12+4 40
20x17 159 beams
Debug option
CMOSImager
GVPP-7B
PRELIMINARY INFORMATION
• Array Format (max): 800Hx600V max• Frame Rate: 0-100 VGA frames per second progressive-scan• Interface Mode: Master/Slave• Data Rate (max): 40 mega pixel per second• Dynamic Range: 10-bits• Parameters: Luminance, Hue, Saturation, Motion (orientation, velocity),
Spatial lines orientation,curves,corners• Computation: 64 STN blocks• Multi-scales possibilities• Internal OS• C language• 0.5 Watt 3.3 Volts @ 13.5MHz• Interface: PCI, I2C, RS232
GVPP road map
FIRSTCHIP
GVPP-6100 mm²
GVPP-7176 mm²
GVPP-7B
Next Generation50 mm²
Commercial use0,35
0,25
0,18
25mm²
85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10YEARS
1 3 7 23 64 STN
100
50
128
STN
The Robotic Future
1970 1980 1990 2000 2010 2020 2030
Analog TV, VCR
(PC)
Mobile PhoneMP3
PPDARobotics
$ 30Milliars
PERSPECTIVES