méxico tec de monterrey instituto de inv. eléctricas gustavo arroyo, pablo ibargüengoytia,...

MéxicoTec de Monterrey

Instituto de Inv. Eléctricas

Gustavo Arroyo,

Pablo Ibargüengoytia,

Eduardo Morales,

L. Enrique Sucar

Reunión Elvira, Albacete 2002

Elvira 2002 L. E. SUCAR 2

• Visión– Endoscopía– Reconocimiento de ademanes

• Aplicaciones industriales– Validación de sensores– Diagnóstico


A “general” BN model for Vision


Endoscopía

• Endoscopy is a tool for direct observation of the human digestive system

• Recognize “objects” in endoscopy images of the colon for semi-automatic navigation

• Main feature – dark regions

• Main objects – “lumen” & “diverticula”


Colon Image


Segmentation – dark region


RB para endoscopía (parcial)


Combinación de conocimiento y datos

• Mejora:– Se parte de una estructura dada por un experto

(subjetiva) y se mejora con datos– Por ejemplo, verificando relaciones de

independencia y alterando la estructura:• Eliminar nodos• Combinar nodos• Insertar nodos


Mejora Estructural

YX

Z

X

Z

XY

Z W

Z

YX


Semi-automatic Endoscope


Endoscopy navegation system


Human activity recognition

• Recognize different human activities based on videos (walk, run, goodbye, attention, etc.)

• Consider the movement of several limbs (arms, legs)

• The movements can differ for different persons or even for the same person

• Several activities can be performed at the same time

• Consider continuos activities


Attention


Goodbye – Right - Attention


Feature extraction

• The color marks (for each limb) are segmented, with its position in each frame

• The directions of movement (discretized in 8 direction) are obtained for each image pair

• A window is used to obtain each sequence of changes (6), which are the observations for the recognition model – a Bayesian network


Segmentation


Recognition network


Gesture recognition

• Recognize 5 dynamic gestures with the right hand

• The gestures are for commanding a mobile robot

• Recognition based on HMM


Come

attention

go-right

go-left

stop


Feature Extraction

• Skin detection

• Face and hand segmentation

• Hand tracking

• Motion features


Segmentation

Radial scan forskin pixel detection

Segmentation by groupingskin pixels in the scan lines


TrackingLocate face and hand based on antropometric measures

Track the hand by using the radial scansegmentation in region of interest


Features• From each image we obtain the features:

– change in X (X)– change in Y (Y)– change in area (A)– change in size ratio (R)

• Each one is codified in 3 values: (+, 0, -)

X1,Y,1X2,Y2

A1 A2


St St+1 St+2

DBN for gesture recognition

A

T T+1 T+2

SX,Y A SX,Y A SX,Y


Training and Recognition

• The parameters (conditional probabilities) for the DBN are obtained from examples of each gesture using the EM algorithm (similar to Baum-Welch used in HMM)

• For recognition, the posterior probability of each model is obtained by probability propagation (forward)


Preliminary Results• Correct recognition:

– come 100 %– attention 66.2 %– stop 68.26 %– go-right 99.25 %– go-left 100 %– average 86%

• Parameter reduction:– HMM: 81 per state– DBN: 15 per state


Probabilistic - Logic Networks

• Logic Nodes - logic programs

• Probabilistic Nodes - Bayesian networks

W V

X Y

Z

Z:binary-

relation (X,Y)multi-valued -

relation(X,Y,Z)


Inference• Probability of Z depends on values of X and

Y and if R is satisfied:

P(Z) = R(x,y) P(x) P(y)• Reasoning

– off-line: compute the CPT for all values of X and Y (discrete variables with few values) - deterministic node P(Z | X, Y)

– on-line: evaluate during propagation• discrete: compute summation for unknowns • continuos: sampling techniques


Gesture Recognition

• Based on relations between the different parts of the arm (hand, elbow, shoulder)

• These relations are expressed as logic nodes in a dynamic logic-probabilisic network

• The model is used for gesture recognition via probability propagation


Model

S

Xh Xe

Rhe

Xs

Res

S

Xh

Rhe

Xe Xs

Res


Validación de sensores


Detection network

dp: position demand fuel valve

pr: real fuel valve position

da: position demand IGVs

pa: real IGV position

ps: gas fuel pressure supply

fg: flow of gas

ga: flow of air

t: temperature

p: pressure


Detection Algorithm:

For all nodes:• Instantiate all nodes except one of the nodes

(Ci)• Propagate probabilities and obtain a posterior

probability distribution of Ci

• Read real value of variable represented by Ci

• If P(real value) pvalue then return(ok) else return(faulty)


Isolation Network Construction

• Markov blanket (MB): set of variables that makes a variable independent from the others

• EMB(n) = MB(n) + n• A faulty node affects only its EMB• Faults outside the EMB of a node do not affect the

value of the node• The isolation network relates real and apparent

faults: A real fault in a node causes apparent faults in all its EMB


Isolation network


Isolation Algorithm

• Instantiate the apparent fault node corresponding to Ci in the isolation network

• Propagate probabilities and obtain a posterior probability of all Real fault nodes

Red Temporal

para Diagnóstico de Plantas Eléctricas

Red Temporal

para Diagnóstico de Plantas Eléctricas

Subsistema de una Planta Eléctrica

DRUM

S U P E R H E A T E R S T E A M S Y S T E M

F E E D W A T E R S Y S T E M C O N D E N S E R S Y S T E M

W A T E R - S T E A MG E N E R A T O RS Y S T E M

S T E A M - T U R B I N E S Y S T E M

R E H E A T E RS T E A M S Y S T E M

F E E D A T E R P U M P

F E E D A T E R V A L V E

S P R A Y V A L V E P S T E A M V A L V E

T R U B I N E

S T F

S T T

D R PF

S W F


Nodo Temporal

• Nodo que representa un “evento” o cambio de estado de una variable de estado

• Sus valores corresponden a diferentes intervalos de tiempo en que ocurre el cambio

• Ejemplo:– Nodo: incremento de nivel– Valores (3):

• Cambio 0 - 10• Cambio 10 - 50• No cambio

Red bayesiana con nodos temporales

FWF

FWPF LI

SWVF

SWV

SWF

FWVF

FWV FWP STV

STF

DRL

DRP

STT

FWPFOccur 0.58¬Occur 0.42

LIOccur 0.88¬Occur 0.12

FWVFOccur 0.57¬Occur 0.43

SWVFOccur 0.18¬Occur 0.82

FWPtrue, [10-29] = 0.36true, [29-107] = 0.57false, [10-107] = 0.07

STVTrue, [0-18] = 0.69True, [18-29] = 0.20False, [0-29] = 0.11

STFTrue, [52-72] = 0.65True, [72-105] = 0.24False, [52-105] = 0.11

FWVTrue, [28-41] = 0.30True, [41-66] = 0.27False, [28-66] = 0.43

SWVTrue, [20-33] = 0.11True, [33-58] = 0.13False, [20-58] = 0.76

FWFTrue, [25-114] = 0.77True, [114-248] = 0.18False, [25-248] = 0.05

SWFTrue, [108-170] = 0.75True, [170-232] = 0.21False, [108-232] = 0.04

STTDecrement, [10-42] = 0.37Decrement, [42-100] = 0.14Decrement, [100-272] = 0.47False, [10-272] = 0.02

DRPTrue, [30-70] = 0.58True, [70-96] = 0.40False, [30-96] = 0.02

DRLIncrement, [10-27] = 0.49Increment, [27-135] = 0.09Decrement, [22-37] = 0.28Decrement [37-44] = 0.12False, [10-135] = 0.02

Variables

LI=Load increment

FWPF=FW pump failure

FWVF=FW valve failure

SWVF=SW valve failure

STV=Steam valve

FWP=FW pump

FWV=FW valve

SWV=SW valve

STF=Steam flow

FWF=FW flow

SWF=SW flow

DRL=Drum level

DRP=Drum pressure

STT=Steam temperature

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