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México Tec de Monterrey Instituto de Inv. Eléctricas

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

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

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

  4. 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

  5. Colon Image Elvira 2002 L. E. SUCAR

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

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

  8. 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

  9. Z Z W Z XY X Y X Mejora Estructural Z X Y Elvira 2002 L. E. SUCAR

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

  11. Endoscopy navegation system Elvira 2002 L. E. SUCAR

  12. Endoscopy navegation system Elvira 2002 L. E. SUCAR

  13. 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

  14. Attention Elvira 2002 L. E. SUCAR

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

  16. 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

  17. Segmentation Elvira 2002 L. E. SUCAR

  18. Recognition network Elvira 2002 L. E. SUCAR

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

  20. Comeattentiongo-rightgo-leftstop Elvira 2002 L. E. SUCAR

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

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

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

  24. 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 Elvira 2002 L. E. SUCAR

  25. Elvira 2002 L. E. SUCAR

  26. 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 Elvira 2002 L. E. SUCAR

  27. 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

  28. 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 Elvira 2002 L. E. SUCAR

  29. 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

  30. 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

  31. 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

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

  33. Validación de sensores Elvira 2002 L. E. SUCAR

  34. 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 Elvira 2002 L. E. SUCAR

  35. 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

  36. 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 Elvira 2002 L. E. SUCAR

  37. Isolation network Elvira 2002 L. E. SUCAR

  38. 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 Elvira 2002 L. E. SUCAR

  39. Red Temporal para Diagnóstico de Plantas Eléctricas

  40. Subsistema de una Planta Eléctrica

  41. 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 Elvira 2002 L. E. SUCAR

  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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