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México Tec de Monterrey Instituto de Inv. Eléctricas Gustavo Arroyo, Pablo Ibargüengoytia, Eduardo Morales, L. Enrique Sucar Reunión Elvira, Albacete 2002.

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Presentation on theme: "México Tec de Monterrey Instituto de Inv. Eléctricas Gustavo Arroyo, Pablo Ibargüengoytia, Eduardo Morales, L. Enrique Sucar Reunión Elvira, Albacete 2002."— Presentation transcript:

1 México Tec de Monterrey Instituto de Inv. Eléctricas Gustavo Arroyo, Pablo Ibargüengoytia, Eduardo Morales, L. Enrique Sucar Reunión Elvira, Albacete 2002

2 Elvira 2002 L. E. SUCAR2 Visión –Endoscopía –Reconocimiento de ademanes Aplicaciones industriales –Validación de sensores –Diagnóstico

3 Elvira 2002 L. E. SUCAR3 A “general” BN model for Vision

4 Elvira 2002 L. E. SUCAR4 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”

5 Elvira 2002 L. E. SUCAR5 Colon Image

6 Elvira 2002 L. E. SUCAR6 Segmentation – dark region

7 Elvira 2002 L. E. SUCAR7 RB para endoscopía (parcial)

8 Elvira 2002 L. E. SUCAR8 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

9 Elvira 2002 L. E. SUCAR9 Mejora Estructural YX Z X Z XY Z W Z YX

10 Elvira 2002 L. E. SUCAR10 Semi-automatic Endoscope

11 Elvira 2002 L. E. SUCAR11 Endoscopy navegation system

12 Elvira 2002 L. E. SUCAR12 Endoscopy navegation system

13 Elvira 2002 L. E. SUCAR13 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

14 Elvira 2002 L. E. SUCAR14 Attention

15 Elvira 2002 L. E. SUCAR15 Goodbye – Right - Attention

16 Elvira 2002 L. E. SUCAR16 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

17 Elvira 2002 L. E. SUCAR17 Segmentation

18 Elvira 2002 L. E. SUCAR18 Recognition network

19 Elvira 2002 L. E. SUCAR19 Gesture recognition Recognize 5 dynamic gestures with the right hand The gestures are for commanding a mobile robot Recognition based on HMM

20 Elvira 2002 L. E. SUCAR20 Come attention go-right go-left stop

21 Elvira 2002 L. E. SUCAR21 Feature Extraction Skin detection Face and hand segmentation Hand tracking Motion features

22 Elvira 2002 L. E. SUCAR22 Segmentation Radial scan for skin pixel detection Segmentation by grouping skin pixels in the scan lines

23 Elvira 2002 L. E. SUCAR23 Tracking Locate face and hand based on antropometric measures Track the hand by using the radial scan segmentation in region of interest

24 Elvira 2002 L. E. SUCAR24 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,1 X2,Y2 A1 A2

25 Elvira 2002 L. E. SUCAR25

26 Elvira 2002 L. E. SUCAR26 StSt S t+1 S t+2 DBN for gesture recognition A TT+1 T+2 SX,Y AS AS

27 Elvira 2002 L. E. SUCAR27 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)

28 Elvira 2002 L. E. SUCAR28 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

29 Elvira 2002 L. E. SUCAR29 Probabilistic - Logic Networks Logic Nodes - logic programs Probabilistic Nodes - Bayesian networks WV XY Z Z: binary- relation (X,Y) multi-valued - relation(X,Y,Z)

30 Elvira 2002 L. E. SUCAR30 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

31 Elvira 2002 L. E. SUCAR31 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

32 Elvira 2002 L. E. SUCAR32 Model S XhXe Rhe Xs Res S Xh Rhe Xe Xs Res

33 Elvira 2002 L. E. SUCAR33 Validación de sensores

34 Elvira 2002 L. E. SUCAR34 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

35 Elvira 2002 L. E. SUCAR35 Detection Algorithm: For all nodes: Instantiate all nodes except one of the nodes (C i ) Propagate probabilities and obtain a posterior probability distribution of C i Read real value of variable represented by C i If P(real value)  pvalue then return(ok) else return(faulty)

36 Elvira 2002 L. E. SUCAR36 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

37 Elvira 2002 L. E. SUCAR37 Isolation network

38 Elvira 2002 L. E. SUCAR38 Isolation Algorithm Instantiate the apparent fault node corresponding to C i in the isolation network Propagate probabilities and obtain a posterior probability of all Real fault nodes

39 Red Temporal para Diagnóstico de Plantas Eléctricas

40 Subsistema de una Planta Eléctrica

41 Elvira 2002 L. E. SUCAR41 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 Cambio No cambio

42 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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