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Reunión Elvira, Albacete 2002. México Tec de Monterrey Instituto de Inv. Eléctricas. Gustavo Arroyo, Pablo Ibargüengoytia, Eduardo Morales, L. Enrique Sucar. Visión Endoscopía Reconocimiento de ademanes Aplicaciones industriales Validación de sensores Diagnóstico.

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México Tec de Monterrey Instituto de Inv. Eléctricas

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Reunión Elvira, Albacete 2002

MéxicoTec de Monterrey Instituto de Inv. Eléctricas

Gustavo Arroyo,

Pablo Ibargüengoytia,

Eduardo Morales,

L. Enrique Sucar


  • Visión

    • Endoscopía

    • Reconocimiento de ademanes

  • Aplicaciones industriales

    • Validación de sensores

    • Diagnóstico

Elvira 2002 L. E. SUCAR


A “general” BN model for Vision

Elvira 2002 L. E. SUCAR


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”

Elvira 2002 L. E. SUCAR


Colon Image

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Segmentation – dark region

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RB para endoscopía (parcial)

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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

Elvira 2002 L. E. SUCAR


Z

Z

W

Z

XY

X

Y

X

Mejora Estructural

Z

X

Y

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Semi-automatic Endoscope

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Endoscopy navegation system

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Endoscopy navegation system

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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

Elvira 2002 L. E. SUCAR


Attention

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Goodbye – Right - Attention

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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

Elvira 2002 L. E. SUCAR


Segmentation

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Recognition network

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Gesture recognition

  • Recognize 5 dynamic gestures with the right hand

  • The gestures are for commanding a mobile robot

  • Recognition based on HMM

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Comeattentiongo-rightgo-leftstop

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Feature Extraction

  • Skin detection

  • Face and hand segmentation

  • Hand tracking

  • Motion features

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Segmentation

Radial scan for

skin pixel detection

Segmentation by grouping

skin pixels in the scan lines

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Tracking

Locate face and hand based on antropometric measures

Track the hand by using the radial scan

segmentation in region of interest

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Features

  • From each image we obtain the features:

    • change in X (DX)

    • change in Y (DY)

    • change in area (DA)

    • change in size ratio (DR)

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

X2,Y2

X1,Y,1

A2

A1

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Elvira 2002 L. E. SUCAR


DBN for gesture recognition

St

St+1

St+2

X,Y

A

S

X,Y

A

S

X,Y

A

S

T

T+1

T+2

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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)

Elvira 2002 L. E. SUCAR


Preliminary Results

  • Correct recognition:

    • come100 %

    • attention66.2 %

    • stop68.26 %

    • go-right99.25 %

    • go-left100 %

    • average 86%

  • Parameter reduction:

    • HMM: 81 per state

    • DBN: 15 per state

Elvira 2002 L. E. SUCAR


Probabilistic - Logic Networks

  • Logic Nodes - logic programs

  • Probabilistic Nodes - Bayesian networks

X

Y

Z:

binary-

relation (X,Y)

multi-valued -

relation(X,Y,Z)

Z

W

V

Elvira 2002 L. E. SUCAR


Inference

  • Probability of Z depends on values of X and Y and if R is satisfied:

    P(Z) = S S 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

Elvira 2002 L. E. SUCAR


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

Elvira 2002 L. E. SUCAR


Model

Xh

Xe

Xs

Xe

Xh

Xs

Rhe

Res

Rhe

Res

S

S

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Validación de sensores

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Detectionnetwork

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

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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)

Elvira 2002 L. E. SUCAR


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

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Isolation network

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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

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Red Temporal para Diagnóstico de Plantas Eléctricas


Subsistema de una Planta Eléctrica


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

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Red bayesiana con nodos temporales

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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