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Image Processing and Analysis: Applications and Trends
Joo Manuel R. S. Tavares
tavares@fe.up.pt www.fe.up.pt/~tavares
AES ATEMA 2010 (Le Quebec, CANADA) Int. ConferenceMontreal & Quebec City, CANADA
June 27 to July 03 , 2010on
Advances and Trends in Engineering Materialsand their Applications
Outline
1. Introduction
2. Segmentation
3. Motion Tracking
4. Analysis of Objects: Matching, Registration and Morphing
5. 3D Reconstruction
6. Conclusions
7. Research Team
2Image Processing and Analysis: Applications and TrendsJoo Manuel R. S. Tavares
Introduction
The researchers of Computational Vision aim the development of algorithms to perform in fully or semi-automatically manner operations and tasks carry out by the (quite complex) humans vision system
4Image Processing and Analysis: Applications and Trends
Original images
Azevedo et al. (2010), Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods, Computer Methods in Biomechanics and Biomedical Engineering 13(3): 359-369
Computational 3D model built voxelized and poligonized
Introduction
Joo Manuel R. S. Tavares
Image processing and analysis are topics of the most importance for our Society
Algorithms of image processing and analysis are frequently used, for example, in:
Medicine Biology Industry Natural Sciences Engineering
Examples of common tasks involving algorithms of image processing and analysis are:
noise removal geometric correction segmentation, recognition (2D-4D) motion tracking and analysis, including matching, registration and morphing (2D-4D) 3D reconstruction
5Image Processing and Analysis: Applications and Trends
Introduction
Joo Manuel R. S. Tavares
Introduction: Usual Computational Pipeline for Image Processing and Analysis
Image Processing and Analysis: Applications and Trends 6
Image(s) enhancement
Image(s) segmentation / features extraction
tracking
matching
morphing
Image(s)
motionanalysis registration
image processing
image analysis /computational
visionJoo Manuel R. S. Tavares
3D vision
computer vision
Segmentation
Segmentation It is intended to identify in a full or semi- automatic manner
objects (2D/3D) presented in static images or in image sequences
The most usual methodologies are based on template matching, statistical, geometric or physical modeling, or neuronal networks
It is one of the most usual operations involved in the computational analysis of objects from images, and very often it is the first important step of image processing and analysis
Frequent problems: noise, low resolution, reduce contrast, shapes not previously known, occlusion, multiple objects, etc.
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 8
Segmentation Example: segmentation of contours in dynamic
pedobarography (Otsu method, morphologic operators)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 9
Original images After segmentation
Bastos & Tavares (2004), Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques, Lecture Notes in Computer Science 3179:39-50
camera mirror
contact layer + glass
reflected light glass
pressure opaque layer
lamp
lamp transparent layer
Segmentation Example: analysis of damage in composite materials
(binarization and region analysis)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 10
Original image After segmentation
Damage areaMeasures obtained
Marques et al. (2009), Delamination Analysis of Carbon Fibre Reinforced Laminates: Evaluation of a Special Step Drill , Composites Science and Technology, 69(14): 2376-2382
Segmentation Example: hardness evaluation from indentation images
(Johannsen and Bille threshold, region growing)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 11
Vickers hardness Brinell hadnessFilho et al. (2010), Brinell and Vickers Hardness Measurement using Image Processing and Analysis Techniques, Journal of Testing and Evaluation 38(1):88-94
Segmentation Example: analysis of material microstructures (neuronal
network)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 12
Original images After segmentationAlbuquerque et al. (2008), A New Solution for Automatic Microstructures Analysis from Images Based on a Backpropagation Artificial Neural Network, Nondestructive Testing and Evaluation 23(4):273-283
Segmentation Example: evaluation of nickel alloy secondary phases from
Scanning Electron Microscopic images (neuronal network)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 13
Original image Image segmented
Albuquerque et al. (2010), Automatic Evaluation of Nickel Alloy Secondary Phases from SEM Images, Microscopy Research and Technique, doi: 10.1002/jemt.20870 (in press)
Segmentation Example: evaluation on synthetic material porosity from
optical microscopic images (neuronal network)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 14
Original images withtraining pixels
Image segmented
Albuquerque et al. (2010), New computational solution to quantify synthetic material porosity from optical microscopic images, Journal of Microscopy, doi: 10.1111/j.1365-2818.2010.03384.x (in press)
Segmentation Example: control of a servomechanism by
gesture recognition (orientation histograms)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 15
Tavares et al. (2005), Control a 2-Axis Servomechanism by Gesture Recognition using a GenericWebCam, International Journal of Advanced Robotic Systems 2(1):39-44
Running mode
Conversion for 256 gray levels
Orders orientation histogram vectors
initialization (stored in memory)
1 2 3 4
Orders images acquisition (one by one)
Command image
acquisition Conversion for 256
gray levels
Comparison of the images orientation histogram vector
with the stored vectors of the preset orders images
Associated command
order
1 3 4
1 2 4
1 2 3
2 3 4
4
2
1
Learning mode
Segmentation Example: detection of breast tumours from mammography
images (Hough transform)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 16
Chagas et al. (2007), An Application of Hough Transform to Identify Breast Cancer in Images, VIPimage 2007, 363-368
Original image After segmentation
Segmentation Example: object recognition (image template matching)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 17
Carvalho & Tavares (2005), Metodologias para identificao de faces em imagens: Introduo e exemplos de resultados, CMNI 2005
fft fft
ift
( )3ift D CC( )2ift D CC
max CC
Original imageTemplate image
Segmentation Example: segmentation of facial features
(deformable geometric template)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 18
Carvalho & Tavares (2006), Two Methodologies for Iris Detection and Location in Face Images, CompIMAGE 2006, 129-134Carvalho & Tavares (2007), Eye detection using a deformable template in static images, VipIMAGE 2007, 209-215
Original image and associated energy fields
Segmentation of the iris using adeformable template (a circle)
Segmentation of an eye using an
deformable template
Segmentation Example: segmentation of skin regions in images (statistical
model)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 19
Skin samples used to build the statistical model Original image and
segmentation obtained
Carvalho & Tavares (2005), Metodologias para identificao de faces em imagens: Introduo e exemplos de resultados, CMNI 2005
Probably functionused
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 20
Segmentation Example: segmentation of scene background/foreground in
image sequences (statistical models)
Backgroundsubtraction
Foreground objectdetection
Vasconcelos & Tavares (2008), Image Segmentation for Human Motion Analysis: Methods and Applications, WCCM8 / ECCOMAS 2008
Original images
Segmentation Example: segmentation of faces and hands in images
(Active Shape Model)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 21
Segmentations achieved (initial, intermediate and final steps)Vasconcelos & Tavares (2008), Methods to Automatically Built Point Distribution Models for Objects like Hand Palms and Faces Represented in Images, Computer Modeling in Engineering & Sciences 36(3):213-241
Segmentation Example: segmentation of faces in images (Active Appearance
Model)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 22
Vasconcelos & Tavares (2008), Methods to Automatically Built Point Distribution Models for Objects like Hand Palms and Faces Represented in Images, Computer Modeling in Engineering & Sciences 36(3):213-241
Segmentations achieved (initial, intermediate and final steps)
Segmentation Example: analysis of the vocal tract shape during speech
production from MRI (Active Shape Models)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 23
Vasconcelos et al. (2010), Towards the Automatic Study of the Vocal Tract from Magnetic Resonance Images, Journal of Voice, doi:10.1016/j.jvoice.2010.05.002 (in press)
Intermediate segmentation II
Originalimage
Finalsegmentation
Intermediate segmentation I
Segmentation Example: analysis of the vocal tract shape during speech
production from MRI (Active Appearance Models)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 24
Vasconcelos et al. (2010), Towards the Automatic Study of the Vocal Tract from Magnetic Resonance Images, Journal of Voice, doi:10.1016/j.jvoice.2010.05.002 (in press)
Intermediate segmentations
Initialsegmentation
Finalsegmentation
Intermediate segmentations
Segmentation Example: segmentation of medical images (active contours -
snakes)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 25
Original image andinitial contour
Final contour
Tavares et al. (2009), Computer Analysis of Objects Movement in Image Sequences: Methods and Applications, International Journal for Computational Vision and Biomechanics 2(2): 209-220
Segmentation Example: segmentation of objects in images (deformable
contour, FEM, Lagrange equation)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 26
Original images and initial contours Final contoursGonalves et al. (2008), Segmentation and Simulation of Objects Represented in Images using Physical Principles, Computer Modeling in Engineering & Sciences 32(1):45-55
rubberk = 200N/m14s
Segmentation Example: segmentation of medical images (level-set method)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 27
Original image Initial segmentation Final segmentation
Perdigo et al. (2005), Gerao de modelos de malhas de elementos finitos a partir de imagens mdicas 2D, Encontro_1_Biomecnica, 81-85
Segmentation Example: segmentation of pelvic floor from MRI (level-set
method, prior knowledge, shape influence field)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 28
Ma et al. (2010), A shape guided C-V model to segment the levator ani muscle in axial magnetic resonance images, Medical Engineering & Physics, doi:10.1016/j.medengphy.2010.05.002 (in press)
Pelvic floor segmented
Segmentation Example: new computational framework for medical image
segmentation (VC++, OpenCV, ITK)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 29
Interface of our platform
Ma et al. (2008), Segmentation of Structures in Medical Images: Review and a New Computational Framework, CMBBE2008
Segmentation Example: segmentation of organs from female pelvic cavity
MRI (using our platform)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 30
Ma et al. (2010), A Review of Algorithms for Medical Image Segmentation and their Applications to the Female Pelvic Cavity, Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246
Region Growing Watershed
Malladis methodGeodesic Active Contour
Motion Tracking
Motion Tracking It is intended to track the motion (and/or the deformation) of
objects along image sequences In this area, the methodologies based on optical flow, block
matching and stochastic methods are widespread Usually, it concerns the estimation of the motion involved,
the management of the features being tracked, and the analysis of the motion tracked as well as its quantification
Usual problems: non-rigid motions, geometric distortions, non-constant illumination, occlusions, noise, multiple objects, etc.
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 32
Motion Tracking Computational framework to
track features in image sequences(Kalman Filter or Unscented KalmanFilter, optimization, Mahalanobisdistance, management model)
Image Processing and Analysis: Applications and Trends 33
Pinho & Tavares (2009), Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model, Computer Modeling in Engineering & Sciences 46(1):51-75
Pinho & Tavares (2009), Comparison between Kalman and Unscented Kalman Filters in Tracking Applications of Computational Vision, VipIMAGE 2009
Joo Manuel R. S. Tavares
Motion Tracking Kalman Filter
Optimal recursive Bayesian stochastic method One of its drawbacks is the restrictive assumption of Gaussian
posterior density functions at every time step Many tracking problems involve non-linear motions (i.e. human gait)
Image Processing and Analysis: Applications and Trends 34Joo Manuel R. S. Tavares
Motion Tracking Example: tracking marks in gait analysis (Kalman filter,
Mahalanobis distance, optimization, management model)
Image Processing and Analysis: Applications and Trends 35
Prediction Uncertainty Area Measurement Correspondence Result
Pinho et al. (2005), Human Movement Tracking and Analysis with Kalman Filtering and Global Optimization Techniques, ICCB 2005, 915-926Pinho & Tavares (2009), Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model, Computer Modeling in Engineering & Sciences 46(1):51-75
(5 frames)
Joo Manuel R. S. Tavares
36Image Processing and Analysis: Applications and Trends
Pinho et al. (2007), Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter, International Journal of Simulation Modelling 6(2):84-92
(547 frames)
Pinho et al. (2005), A Movement Tracking Management Model with Kalman Filtering, Global Optimization Techniques and Mahalanobis Distance, LSCCS, Vol. 4A:463-466
Motion Tracking Example: tracking mice in long image sequences (Kalman
filter, Mahalanobis distance, optimization, management model)
Joo Manuel R. S. Tavares
Motion Tracking Unscented Kalman Filter
A set of sigma-points from the distribution of the state vector is propagated through the true non-linearity, and the parameters of the Gaussian approximation are then re-estimated
Addresses the main shortcomings of Kalman Filter, as well as of Extended Kalman Filter, and is more suitable for non-linear motions
Image Processing and Analysis: Applications and Trends 37Joo Manuel R. S. Tavares
Motion Tracking Example: tracking the centre of a square that is moving
according to a non-linear model (Kalman Filter (KF) and Unscented Kalman Filter (UKF))
Image Processing and Analysis: Applications and Trends 38
Motion equations:
0 0
22( 1) 12.51 ,12.51
with 6, 10
x x ii iy xi i
x y
= + += +
= =
#6
+ predictionsx measurementsx corrections
Kalman Filter results:
Joo Manuel R. S. Tavares
(8 frames)
Motion Tracking Example: tracking the centre of a square that is moving
according to a non-linear model (Kalman Filter (KF) and Unscented Kalman Filter (UKF)) cont.
Image Processing and Analysis: Applications and Trends 39Joo Manuel R. S. Tavares
Motion equations:
0 0
22( 1) 12.51 ,12.51
with 6, 10
x x ii iy xi i
x y
= + += +
= =
(8 frames)
Tracking error (predicted/real state)
Image Processing and Analysis: Applications and Trends 40
Motion Tracking Example: tracking the motion of three mice in a real image
sequence (Kalman Filter (KF) and Unscented Kalman Filter (UKF))
+ predictionsx measurementsx corrections
(22 frames)
Joo Manuel R. S. Tavares
Image Processing and Analysis: Applications and Trends 41
Motion Tracking Example: tracking the motion of three mice in a real image
sequence (Kalman Filter (KF) and Unscented Kalman Filter (UKF)) cont.
Kalman Filter results Unscented Kalman Filter results
Joo Manuel R. S. Tavares
(22 frames)
Image Processing and Analysis: Applications and Trends 42
Motion Tracking Example: tracking the motion of three mice in a real image
sequence (Kalman Filter (KF) and Unscented Kalman Filter (UKF)) cont.
Joo Manuel R. S. Tavares
(22 frames)Tracking error (predicted/real state)
Motion Tracking Influence of the adopted filter: Kalman Filter (KF) and
Unscented Kalman Filter (UKF) If the motion is highly nonlinear, then UKF justifies its superior
computational load Otherwise, KF with the undertaken matching (association)
methodology can accomplish efficiently the tracking Hence, the decision between KF or UKF is application-dependent
Frequently, UKF gets superior results However, when the computational load is somewhat constrained, KF
with a suitable matching strategy can be a good tracking solution
Image Processing and Analysis: Applications and Trends 43Joo Manuel R. S. Tavares
Analysis of Objects:Matching, Registration and
Morphing
Analysis of Objects Matching
It is regularly used in the computational analysis of objects from images, for example, to register (i.e. align) objects, recognize objects, attain 3D information, analyze the motion tracked, and so forth
Generally, it is achieved by considering invariant objects characteristics, as curvature, or displacements in a global space (like in modal space)
Common problems: occlusion, non-rigid deformations, high shape variations, etc.
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 45
Analysis of Objects Registration
It is commonly required in order to compare objects in images acquired at different time instants or according to distinct conditions
It is essential, for example, in Medicine to follow up the evaluation of patients diseases from images
Usually, it is achieved by considering objects characteristic features, as points of maximum curvature, and their matching followed by the estimation of the involved transformation
Common problems: key and invariant features not easily identified, occlusion, non-rigid deformations, severe shape variations, etc.
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 46
Analysis of Objects Morphing (i.e. simulation)
It is an especially used in Computer Graphics, but also very useful in the analysis of objects from images, for example, to estimate the deformation involved between two objects or between two configurations of an object, to simulate the transitions between two shapes acquired under a high temporal gap, etc.
Normally, it is attained by considering simple geometric transformations
However, when it must be considered the real behavior of the objects, physical methodologies and modeling as, for example, FEM, should be considered
Common difficulties are related with the estimation of the involved forces and with the properties of the adopted (virtual) material
The adequate matching of the objects is crucial
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 47
Matching Using physical or geometrical modeling and modal matching
Image Processing and Analysis: Applications and Trends 48
Modeling(physical or geometrical)
Eigenvalues / eigenvectors computation
Matching matrix
assembly
Contour 1
Contour 2
Matches achievement(optimization)
Modeling(physical or geometrical)
Eigenvalues / eigenvectors computation
Bastos & Tavares (2006), Matching of Objects Nodal Points Improvement using Optimization, Inverse Problems in Science and Engineering 14(5):529-541
Joo Manuel R. S. Tavares
Example: matching contours in dynamic pedobarography (FEM, modal matching, optimization)
Matching
Image Processing and Analysis: Applications and Trends 49
Original images Matched contours
Bastos & Tavares (2004), Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques, Lecture Notes in Computer Science 3179:39-50
camera mirror
contact layer + glass
reflected light glass
pressure opaque layer
lamp
lamp transparent layer
Tavares & Bastos (2010), Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques, Progress in Computer Vision and Image Analysis, Chapter 19, 339-368
Joo Manuel R. S. Tavares
Matching Example: matching contours and surfaces in dynamic
pedobarography (FEM, modal analysis, optimization)
Image Processing and Analysis: Applications and Trends 50
Image of dynamic pedobarography
Tavares & Bastos (2005), Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques, Electronic Letters on Computer Vision and Image Analysis 5(3):1-20
Matching foundbetween two contours
Matching found between two intensity (pressure) surfaces (2 views)
Matching found between iso-contours (2 views)
Joo Manuel R. S. Tavares
Registration Registration of contours in images (geometrical modeling,
optimization, dynamic programming)
Image Processing and Analysis: Applications and Trends 51
Oliveira & Tavares (2008), Algorithm of dynamic programming for optimization of the global matching between two contours defined by ordered points, Computer Modeling in Engineering & Sciences 31(11):1-11
Joo Manuel R. S. Tavares
Registration Example: registration of contours in images (geometrical
modeling, optimization, dynamic programming)
Image Processing and Analysis: Applications and Trends 52
Original images and contours
Matched contours before registration
Matched contours after registration
Oliveira & Tavares (2009), Matching Contours in Images through the use of Curvature, Distance to Centroidand Global Optimization with Order-Preserving Constraint, Computer Modeling in Engineering & Sciences 43(1):91-110
Joo Manuel R. S. Tavares
Registration Example: registration of images in pedobarography
(geometrical modeling, optimization, dynamic programming)
Image Processing and Analysis: Applications and Trends 53
Original images and contours Contours and images before and after registration
Oliveira et al. (2009), Rapid pedobarographic image registration based on contour curvature and optimization, Journal of Biomechanics 42(15):2620-2623Joo Manuel R. S. Tavares
Registration Example: registration of images in pedobarography (Fourier
transform)
Image Processing and Analysis: Applications and Trends 54
Original images Images before andafter registration
Oliveira et al. 2010, Registration of pedobarographic image data in the frequency domain, Computer Methods in Biomechanics and Biomedical Engineering (in press)
Joo Manuel R. S. Tavares
Registration Example: registration of images in pedobarography (Hybrid
method: Contours registration or Fourier transform based registration + Optimization of a Similarity Measure MSE, MI or XOR)
Image Processing and Analysis: Applications and Trends 55
Original images Images before andafter registration
Oliveira & Tavares 2010, Novel Framework for Registration of Pedobarographic Image Data, Medical & Biological Engineering & Computing (submitted)
Joo Manuel R. S. Tavares
Registration Registration of image sequences in dynamic
pedobarography (spatial and temporal registration)
Image Processing and Analysis: Applications and Trends 56Joo Manuel R. S. Tavares
Oliveira & Tavares 2010, Spatio-temporal Registration of Pedobarographic Image Sequences, Journal of Biomechanics (submitted)
Registration Example: registration of image sequences in dynamic
pedobarography (spatial and temporal registration)
Image Processing and Analysis: Applications and Trends
57
Joo Manuel R. S. Tavares
Original sequencesbefore registration
Preprocessed sequences
Original image sequences
Sequencesafter registration
57
Morphing
Image Processing and Analysis: Applications and Trends 58
Physical morphing/simulation of contours in images (FEM, modal analysis, optimization, Lagrange equation)
Joo Manuel R. S. Tavares
Example: morphing contours in images (FEM, modal analysis, optimization, Lagrange equation)
Matching found
Deformations estimated
Morphing
Image Processing and Analysis: Applications and Trends 59
Gonalves et al. (2008), Segmentation and Simulation of Objects Represented in Images using Physical Principles, Computer Modeling in Engineering & Sciences 32(1):45-55
Original images
Joo Manuel R. S. Tavares
3D Reconstruction
3D Reconstruction It is intended to achieve the 3D reconstruction of objects or
scenes from images In this area, the following methodologies are common:
external shapes active techniques (with energy projection or relative motion), passive techniques (without energy projection nor relative motion) and of space carving; inner shapes 2D segmentation (contours, for example) and data interpolation
Usually, it involves tasks of camera calibration, data segmentation, matching, triangulation and interpolation
Common problems: geometric distortions, bad or unstable illumination, occlusion, noise, multiple objects, complex shapes and topologies, etc.
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 61
3D Reconstruction Example: 3D reconstruction of organs from medical images
(segmentation 2D, marching cubes, Delaunay)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 62
Segmentation done in a 2D slice and the 3D reconstruction
obtained
3D reconstruction of some structures of an arm
Perdigo et al. (2005), Sobre a Gerao de Malhas Tridimensionais para fins Computacionais a partir de Imagens Mdicas, CMNI 2005
3D Reconstruction Example: 3D reconstruction of organs from medical images
(segmentation 2D, loft, smooth, Delaunay)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 63
Segmentation donein a 2D slice
Pelvic floorreconstructed
Reconstructed organs from a pelvic
cavityPimenta et al. (2006), Reconstruction of 3D Models from Medical Images: Application to Female Pelvic Organs, CompIMAGE 2006, 343-348Alexandre et al. (2007), 3D reconstruction of pelvic floor for numerical simulation purpose, VipIMAGE2007, 359-362
slices
3D Reconstruction Example: 3D reconstruction of a scene using a technique
of active vision (dense stereo vision)
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 64
Disparity mapobtained
Original image pair
Azevedo et al. (2006), Development of a Computer Platform for Object 3D Reconstruction using Active Vision Techniques, VISAPP 2006, 383-388
3D Reconstruction Example: 3D reconstruction of objects by space carving
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 65
Azevedo et al. (2008), 3D Object Reconstruction from Uncalibrated Images using an Off-the-Shelf Camera, Advances in Computational Vision and Medical Image Processing: Methods and Applications, 117-136
Original images Computational 3D model built voxelizedand poligonized
3D Reconstruction Example: 3D reconstruction of objects by space carving
Joo Manuel R. S. Tavares Image Processing and Analysis: Applications and Trends 66
Original images Computational 3D model built voxelizedand poligonized
Azevedo et al. (2010), Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods, Computer Methods in Biomechanics and Biomedical Engineering 13(3): 359-369
Conclusions
Conclusions
The area of image processing and analysis is very complex and demand, but of raised importance in many domains
Numerous hard challenges exist, as for example, adverse conditions in the image acquisition process, occlusion, objects with complicate shapes, with topological variations or undergoing complex motions
Considerable work has already been developed, but important and complex goals still to be reached
Methods and methodologies of other research areas, as of Mathematics, Computational Mechanics, Medicine and Biology, can contribute significantly for their reaching
For that, collaborations are welcome
68Image Processing and Analysis: Applications and TrendsJoo Manuel R. S. Tavares
Research Team(Computational Vision)
Research Team (Computational Vision)
PhD students (15): In course: Raquel Pinho, Patrcia Gonalves, Maria
Vasconcelos, Ilda Reis, Teresa Azevedo, Daniel Moura, Zhen Ma, Elza Chagas, Victor Albuquerque, Francisco Oliveira, Eduardo Ribeiro, Antnio Gomes, Joo Nunes, Alex Araujo, Sandra Rua
MSc students (13): In course: Carlos Faria, Elisa Barroso, Ana Jesus, Veronica
Marques, Diogo Faria Finished: Daniela Sousa, Francisco Oliveira, Teresa Azevedo,
Maria Vasconcelos, Raquel Pinho, Lusa Bastos, CndidaCoelho, Jorge Gonalves
BSc students (2) Finished: Ricardo Ferreira, Soraia Pimenta
70Image Processing and Analysis: Applications and TrendsJoo Manuel R. S. Tavares
Image Processing and Analysis:Applications and Trends
Joo Manuel R. S. Tavares
tavares@fe.up.pt www.fe.up.pt/~tavares
Slide Number 1OutlineIntroductionIntroductionIntroductionIntroduction: Usual Computational Pipeline for Image Processing and AnalysisSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationSegmentationMotion TrackingMotion TrackingMotion TrackingMotion Tracking Motion TrackingMotion TrackingMotion TrackingMotion TrackingMotion TrackingMotion TrackingMotion TrackingMotion TrackingMotion TrackingAnalysis of Objects:Matching, Registration and MorphingAnalysis of ObjectsAnalysis of ObjectsAnalysis of ObjectsMatchingMatchingMatchingRegistrationRegistrationRegistrationRegistrationRegistrationRegistrationRegistrationMorphingMorphing3D Reconstruction3D Reconstruction3D Reconstruction3D Reconstruction3D Reconstruction3D Reconstruction3D ReconstructionConclusionsConclusionsResearch Team(Computational Vision)Research Team (Computational Vision)Slide Number 71
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