Analyses of objects in Images by Computer Vision ...tavares/downloads/... · Analyses of objects in Images by Computer Vision: Techniques & Applications 10 Original images Computational

Analyses of objects in Images by Computer Vision: Techniques & Applications

João Manuel R. S. Tavares

[email protected] www.fe.up.pt/~tavares

Johann Bernoulli Institute for Mathematics and Computer Science

September 17, 2014

Outline

1. Introduction

2. Segmentation

3. Motion Tracking

4. Analysis of Objects: Matching, Morphing and Registration

5. 3D Reconstruction

6. Conclusions

7. Research Team

8. Publications & Events

2 Analyses of objects in Images by Computer Vision: Techniques & Applications João Manuel R. S. Tavares

Presentation

Presentation

• Associate Professor at the Faculty of Engineering of the University of Porto (FEUP) / Department of Mechanical Engineering

• Senior Research and Projects Coordinator of the Optics and Experimental Mechanics Lab (LOME) of the Institute of Mechanical Engineering and Industrial Management (INEGI)

• PhD and MSc degrees in Electrical and Computer Engineering from FEUP in 2001 and 1995, respectively

• BSc degree in Mechanical Engineering from FEUP in 1992 • Research Areas: Image Processing and Analysis, Medical

Imaging, Biomechanics, Human Posture and Control, Product Development


FEUP: Identity • With more than 80 years of history, FEUP is the largest school of the

University of Porto; since 2000, FEUP has moved to a brand new campus, just outside the city centre, in eastern Porto, Portugal


FEUP: PORTO World heritage


FEUP: In Figures (2012/2013)


FEUP: In Figures (2012/2013)


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) human’s vision system

10 Analyses of objects in Images by Computer Vision: Techniques & Applications

Original images Computational 3D models built voxelized and poligonized

Introduction


Azevedo et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(3):359-369

• 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


Introduction


Introduction: Usual Computational Pipeline for Image Processing and Analysis

Analyses of objects in Images by Computer Vision: Techniques & Applications 12

Image(s) enhancement

Image(s) segmentation / features extraction

tracking

matching

morphing

Image(s)

motion analysis registration

image (pre)processing

image analysis / computational

vision João Manuel R. S. Tavares

3D vision

computer vision

Introduction • (Pre)processing of noisy images using an intelligent

worm

João Manuel R. S. Tavares Analyses of objects in Images by Computer Vision: Techniques & Applications 13

Original images, noisy corrupted images and smoothed using different smoothing methods

Araujo et al. (2014) Expert Systems with Applications 41(13):5892–5906

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.


Segmentation • Image segmentation by threshold (binarization)


Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation • Example: segmentation of contours in dynamic

pedobarography (Otsu method, morphologic operators)


Original images After segmentation

Bastos & Tavares (2004) 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 due to drill machining in

composite materials (binarization and region analysis)


Original image After segmentation

Damaged area Measures obtained

Marques et al. (2009) Composites Science and Technology 69(14):2376-2382 Albuquerque et al. (2010) Journal of Composite Materials 44(9):1139-1159

Segmentation • Image segmentation by region growing


Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation • Example: segmentation of ear structures (region growing)


Region Growing, x=215; y=254

Segmentation obtained (bony labyrinth)

Barroso et al. (2011) CNME 2011 Ferreira et al. (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(8):888-904

X: 254 Y: 214Index: 116.7RGB: 0.459, 0.459, 0.459

Original Image

Segmentation • Example: hardness evaluation from indentation images

(Johannsen & Bille threshold, region growing)


Vickers hardness Brinell hadness

Filho et al. (2010) Journal of Testing and Evaluation 38(1):88-94

Segmentation • Segmentation of images using neuronal networks


Original images After segmentation (material microstructures )

Albuquerque et al. (2008) Nondestructive Testing and Evaluation 23(4):273-283 Albuquerque et al. (2009) NDT & E International 42(7):644-651

Segmentation • Example: evaluation of the nickel alloy secondary

phases from SEM images (neuronal network)


Original image Image segmented

Albuquerque et al. (2011) Microscopy Research and Technique 74(1):36-46

Segmentation • Example: assessment of material porosity from optical

microscopic images (neuronal network)


Original images with training pixels

Image segmented

Albuquerque et al. (2010) Journal of Microscopy 240(1):50-59


Segmentation • Segmentation of objects in images using image templates

Carvalho & Tavares (2005) CMNI 2005

×fft fft

ift

( )3ift D CC( )2ift D CC

max CC

Original image Template image


Segmentation • Segmentation of objects in images using deformable

templates

Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 Carvalho & Tavares (2007) VipIMAGE 2007, 209-215

Example of a deformable template

Segmentation • Example: segmentation of facial features

(deformable geometric template)


Original image and associated energy fields

Segmentation of the iris using a deformable template (a circle)

Segmentation of an eye using an

deformable template Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 Carvalho & Tavares (2007) VipIMAGE 2007, 209-215

• Statistical modeling of objects in images (point distribution models)


Vasconcelos & Tavares (2008) Computer Modeling in Engineering & Sciences 36(3):213-241

Segmentation


Segmentation • Segmentation of objects in images using active shape

models (point distribution model, optimization)


Segmentation • Example: segmentation of faces and hands in images

(active Shape Model)


Segmentations achieved (initial, intermediate and final steps)


Segmentation • Example: analysis of the vocal tract shape during speech

production from MR images (active shape model)


Intermediate segmentation II

Original image

Final segmentation

Intermediate segmentation I

Vasconcelos et al. (2011) Journal of Voice 25(6):732-742

• Segmentation of objects in images using active appearance models (statistical models, optimization)



Segmentation

Segmentation • Example: segmentation of faces in images (active appearance

model)


Segmentations achieved (initial, intermediate and final steps)


Segmentation • Example: analysis of the vocal tract shape during speech

production from MR images (active appearance model)


Intermediate segmentations

Initial segmentation

Final segmentation

Intermediate segmentations

Vasconcelos et al. (2011) Journal of Engineering in Medicine 225(1):68-76 Vasconcelos et al. (2012) Journal of Engineering in Medicine 226(3):185-196

• Segmentation of objects in images using active contours (i.e. snakes – parametric models)


Tavares et al. (2009) International Journal for Computational Vision and Biomechanics 2(2):209-220

Segmentation

Segmentation • Example: segmentation of a medical image (active contours -

snakes)


Original image and initial contour

Final contour

Tavares et al. (2009) International Journal for Computational Vision and Biomechanics 2(2):209-220

Segmentation • Example: segmentation of objects in images (deformable

contour, FEM, Lagrange equation)


Original images and initial contours Final contours

rubber k = 200N/m 14s

Gonçalves et al. (2008) Computer Modeling in Engineering & Sciences 32(1):45-55


Segmentation • Segmentation of objects in images using level-set

method (geometrical models)

Ma et al. (2010) Medical Engineering & Physics 32(7):766-774 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246


Segmentation • Example: segmentation of the carotid bifurcation in

Doppler images (active contour and level-set)

Segmentation using the contour active method (Yessi’s model)

Segmentation using the level-set method (Chan-Vese’s model)

Silva et al. (2011) VipIMAGE 2011, 117-122 Santos et al. (2013) Expert Systems with Applications 40(16):6570-6579


Ma & Tavares (2014) ComIMAGE’14

Segmentation examples under different imaging conditions and different types of skin lesions

Segmentation • Example: segmentation of skin lesions in dermoscopic

images (level-set method, color spaces)

An illustration of the segmentation process


Segmentation • Segmentation of objects in images using level set method

+ prior knowledge

Ma et al. (2010) Medical Engineering & Physics 32(7):766-774 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation • Example: segmentation of the pelvic floor in MR images

(level-set model, prior knowledge, shape influence field)


Pelvic floor segmented Ma et al. (2010) Medical Engineering & Physics 32(7):766-774


Segmentation

Ma et al. (2013) Computers in Biology and Medicine 43(4):248-258 Ma et al. (2012) The Int. Journal for Numerical Methods in Biomedical Engineering 28(6-7):714-726

• Example: segmentation of organs of the female pelvic cavity in MRI images (level-set method, prior knowledge)

Segmentation of the bladder, vagina and anus from pelvic cavity images (3 examples)

• Example: segmentation of the bladder walls in MRI images (level-set method, prior knowledge)


Segmentation

Ma et al. (2011) Annals of Biomedical Engineering 39(8):2287-2297

Segmentation of the interior and external walls of the bladder (3 examples)

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.


Motion Tracking • Computational framework to

track features in image sequences (Kalman Filter or Unscented Kalman Filter, optimization, Mahalanobis distance, management model)

Analyses of objects in Images by Computer Vision: Techniques & Applications 47 João Manuel R. S. Tavares

Pinho et al. (2007) Int. Journal of Simulation Modelling 6(2):84-92 Pinho & Tavares (2009) VipIMAGE 2009, 299-304 Pinho & Tavares (2009) Computer Modeling in Engineering & Sciences 46(1):51-75

Motion Tracking • Example: tracking marks in gait analysis (Kalman filter,

Mahalanobis distance, optimization, management model)


Prediction Uncertainty Area Measurement Correspondence Result

(5 frames)


Pinho et al. (2005) ICCB 2005, 915-926 Pinho & Tavares (2009) Computer Modeling in Engineering & Sciences 46(1):51-75

Motion Tracking • Example: tracking marks to detect gait

events (Kalman filter, Mahalanobis distance, optimization)


Sousa et al. (2007) ISHF2007, 331-340 Sousa et al. (2007) ICCB2007, 291-296


(547 frames)

Motion Tracking • Example: tracking mice in long image sequences (Kalman

filter, Mahalanobis distance, optimization, management model)


Pinho et al. (2005) LSCCS, Vol. 4A:463-466 Pinho et al. (2007) International Journal of Simulation Modelling 6(2):84-92

Analysis of Objects: Matching

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.


Matching • Using physical or geometrical modeling and modal

matching


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) Inverse Problems in Science and Engineering 14(5):529-541 Tavares & Bastos (2010) Progress in Computer Vision and Image Analysis 339-368

• Example: matching contours in dynamic pedobarography (FEM modeling, modal matching, optimization)

Matching


Original images Matched contours

camera mirror

contact layer + glass

reflected light glass

pressure opaque layer

lamp

lamp transparent layer


Bastos & Tavares (2004) LNCS 3179:39-50 Tavares & Bastos (2010) Progress in Computer Vision and Image Analysis, 339-368

Matching • Example: matching contours and surfaces in dynamic

pedobarography (FEM modeling, modal analysis, optimization)


Image of dynamic pedobarography

Tavares & Bastos (2005) Electronic Letters on Computer Vision and Image Analysis 5(3):1-20

Matching found between two contours

Matching found between two intensity (pressure) surfaces (2 views)

Matching found between iso-contours (2 views)


Analysis of Objects: Morphing


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 to the estimation of the involved forces and with the properties of the adopted (virtual) material

• The adequate matching of the objects is crucial

Morphing


• Physical morphing/simulation of contours in images (FEM modeling, modal analysis, optimization, Lagrange equation)


• Example: morphing contours in images (FEM modeling, modal analysis, optimization, Lagrange equation)

Matching found

Deformations estimated

Morphing


Gonçalves et al. (2008) Computer Modeling in Engineering & Sciences 32(1):45-55

Original images


Analysis of Objects: Registration

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.


Registration • Registration of contours by contours matching,

optimization and dynamic programming


The cost matrix is built based on geometric or physical principles

The matching is found based on the minimization of the sum of the costs associated to the possible correspondences

To search for the best matching is used an optimization assignment algorithm

Bastos & Tavares (2006) Inverse Problems in Science and Engineering 14(5):529-541 Oliveira & Tavares (2009) Computer Modeling in Engineering & Sciences 43(1):91-110 Oliveira, Tavares, Pataky (2009) Journal of Biomechanics 42(15):2620-2623

Registration • Example: registration of contours in images (geometrical

modeling, matching, optimization, dynamic programming)


Original images and contours

Matched contours before registration

Matched contours after registration

Oliveira & Tavares (2009), Computer Modeling in Engineering & Sciences 43(1):91-110 João Manuel R. S. Tavares

Registration • Example: registration of images in pedobarography

(geometrical modeling, matching, optimization, dynamic programming)


Original images and contours Contours and images before and after registration

Oliveira et al. (2009) Journal of Biomechanics 42(15):2620-2623


Registration: 2D, monomodal, intrasubject

Processing time: 0.5 s (AMD Turion64, 2.0 GHz, 1.0 GB of RAM)

Images dimension: 217x140 pixels

Fixed image and contour (MRI)

Moving image and contour (MRI)

Overlapped images before the registration

Overlapped images after the registration

Difference between the images after the registration

Correspondences found between the Corpus Callosum contours

Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93 João Manuel R. S. Tavares Analyses of objects in Images by Computer Vision: Techniques & Applications 65

Registration • Example: registration of brain images (geometrical modeling,

matching, optimization, dynamic programming)

Registration • Registration of images based on Fourier transform


Original images Images before and after registration


Oliveira, Pataky, Tavares (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(6):731-740

Registration: 2D, monomodal, intrasubject




Registration • Example: registration of brain images (Fourier transform)

Registration • Registration based on the iterative search for the

parameters of the transformation that optimizes a similarity measure between the input images


Moving image Fixed image

Pre-registration transformation

(optional) Interpolator Similarity measure

Optimizer Geometric transformation

The optimization algorithm stops when a similarity criterion is achieved

Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

Registration


• Example: registration of images in pedobarography (Hybrid method: Fourier transform based registration + optimization of a similarity measure)

Original images Images before and after registration

Oliveira & Tavares (2012) Medical & Biological Engineering & Computing 49,(3):313-323

Registration: 2D, multimodal, intrasubject (without pre-registration)

Similarity measure: MI



Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93 João Manuel R. S. Tavares Analyses of objects in Images by Computer Vision: Techniques & Applications 70

Registration • Example: registration/fusion of head images (optimization of a

similarity measure)

A computational platform has been developed to assist biomechanical studies based on the registration of plantar pressure images, which can be used in:

– Foot segmentation – Foot classification: left/right,

high arched, flat, normal, … – Foot axis computation – Footprint indices computation – Posterior statistical analysis

Oliveira, Sousa, Santos, Tavares (2012) Computer Methods in Biomechanics and Biomedical Engineering 15(11):1181-1188

Registration • Example: applications in plantar pressure image studies



Registration • Registration using iterative optimization and a curved

transformation (based on B-splines)

Fixed image Moving image

Registered moving image

Pre-registration using a rigid transformation

New pre-registration using an affine transformation

Coarse registration based on B-splines

Fine registration based on B-splines

The registration based on B-splines is of the free-form

deformation type Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

“Checkerboard” of the slices before the registration (CT/MRI-PD, brain)

F F

F F F

F F F

M M M

M M M

M

M

(The “checkerboard” slice is built by interchanging square patches of both slices and preserving their original spatial position in the fixed (F) and moving (M) slices)

Registration • Example: registration/fusion using iterative optimization


Registration: 3D, multimodal, intrasubject; Similarity measure: MI

Checkerboard of the slices after the registration (CT/MRI-PD, brain)

Registration • Example: registration/fusion using iterative optimization (cont.)


Checkerboard of the slices (CT, thorax, Δt: 8.5 months) before the registration

Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

Registration • Example: registration using iterative optimization


Registration: 3D, monomodal, intrasubject; Similatity measure: MI

Checkerboard of the slices (CT, thorax, Δt: 8.5 months) after a cubic B-spline based reg.


Registration • Example: registration using iterative optimization (cont.)

Brain DaTSCAN SPECT images are used to assist the diagnosis of the Parkinson’s disease and to distinguish it from other degenerative diseases. The solution developed is able to:

– Segment the relevant areas and perform dimensional analysis – Quantify the binding potential of the basal ganglia – Computation of statistical data relatively to a reference population – Image classification for diagnosis purposes

Normal Alzheimer Idiopathic Parkinsonism

Essential tremor


Registration • Example: application in brain DaTSCAN SPECT images

Mean slice from the population used as

reference

Corresponding slice of a patient

Difference of intensities

Z-scores mapping over the slice (red – high Z-scores)

3D volume images are automatically registered and statistical analysis relatively to a reference population can be attained

(The blue rectangles represent the 3D ROIs used to compute the binding potentials)

Oliveira et al. (2014) The Quarterly Journal of Nuclear Medicine and Molecular Imaging 58(1):74-84 João Manuel R. S. Tavares Analyses of objects in Images by Computer Vision: Techniques & Applications 78


Basal ganglia from a mean

image of a normal population

Basal ganglia from a patient with idiopathic

Parkinson’s disease

Basal ganglia from a patient with vascular

Parkinson’s disease

Basal ganglia 3D shape reconstruction and quantification

Oliveira et al. (2014) The Quarterly Journal of Nuclear Medicine and Molecular Imaging 58(1):74-84



Three slices (coronal, sagittal and axial) after registration and identification of the potential lesion

3D visualization after CT/SPECT fusion (the lesion identified in the SPECT

slices is indicated)

Registration • Example: application in brain DaTSCAN SPECT/CT fusion


Fully automated segmentation and classification of the images based on image registration and an artificial classifier

Template image (top), segmented image (bottom-left) and artery mapping (bottom-right)

Registration • Example: application in gated myocardial perfusion

SPECT images


Oliveira, Faria, Tavares (2014) Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 228(8):810-818

3D surface of the incus and malleus surface built TC slices with the incus and malleus ossicles (inside the red

ellipse) to be segmented

Registration • Example: application in the fully automated segmentation

of the incus and malleus ear ossicles in conventional CT images


Registration • Registration of image sequences: spatial and temporal

registration


Moving sequence

Fixed sequence

Apply the spatio & temporal

transformation

Compute the similarity measure

Optimizer Build the spatio &

temporal transformation

Oliveira, Sousa, Santos, Tavares (2011) Medical & Biological Engineering & Computing 49(7):843-850 Oliveira & Tavares (2013) Medical & Biological Engineering & Computing 51(3):267-276

Build the temporal representative

images

Search for the transformation that register the temporal

representative images

Estimate the linear temporal

registration

Pre-registration Registration optimization

Registration • Example: registration of image sequences in dynamic

pedobarography (spatial and temporal registration)

Analyses of objects in Images by Computer Vision: Techniques & Applications João Manuel R. S. Tavares 84

Device: Light reflection (25 fps, resolution 30 pixels/cm2)

Image similarity measure: MSD

Sequences dimension: 160x288x22, 160x288x25

Processing time: 1 min (using an AMD Turion64, 2.0 GHz, 1.0 GB of RAM)

Template sequence

Source sequence

Overlapped sequences

Before the registration

After the registration


Device: EMED (25 fps, resolution: 2 pixels/cm2, images dimension: 32x55x13; 32x55x18)

Registration: rigid (spatial), polynomial (temporal); similarity measure: MSD

Processing time: 4 s - AMD Turion64, 2.0 GHz, 1.0 GB of RAM

Fixed sequence

Moving sequence

Overlapped sequences

Before the registration

After the registration

Registration • Example: registration of image sequences in dynamic

pedobarography (spatial and temporal registration)

3D Reconstruction

3D Reconstruction • It is intended to accomplish 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, interpolation and fusion

• Common problems: geometric distortions, bad or unstable illumination, occlusion, noise, multiple objects, complex shapes and topologies, etc.


3D Reconstruction • 3D reconstruction of organs from medical images based

on 2D segmentation, loft, smooth and Delaunay


Segmentation done in a 2D slice

Pelvic floor reconstructed

Reconstructed organs from a pelvic

cavity

Pimenta et al. (2006) CompIMAGE 2006, 343-348 Alexandre et al. (2007) VipIMAGE 2007, 359-362

slices

3D Reconstruction • 3D reconstruction of scenes using techniques of active

vision (dense stereo vision)


Disparity map obtained

Original image pair

Azevedo et al. (2006) VISAPP 2006, 383-388


Azevedo et al. (2008) Advances in Computational Vision and Medical Image Processing: Methods and Applications, 117-136

3D Reconstruction • 3D reconstruction of objects by space carving

Pattern and object turntable image sequence

Pattern image sequence

Background/object segmentation

Camera calibration

Volumetric 3D reconstruction

3D model polygonization

3D Reconstruction • Example: 3D reconstruction of objects by space carving


Azevedo et al. (2008) Advances in Computational Vision and Medical Image Processing: Methods and Applications, 117-136 Azevedo et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(3):359-369


3D Reconstruction • Example: 3D reconstruction of objects by space carving



Azevedo et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(3):359-369


Axial and sagittal T2-weighted MR images

3D Reconstruction of the bladder by fusion data from the axial and sagittal images (2 views)

Ma et al. (2013) Medical Engineering & Physics 35(12):1819-1824

3D Reconstruction • Example: 3D reconstruction from multiple views

(registration/fusion)

• Example: 3D reconstruction of the spine from two orthogonal X-ray images using a deformable model (atlas)


Moura et al. (2010) Computer Modeling in Engineering & Sciences 60(2):115-138 Moura et al. (2011) Medical Engineering & Physics 33(8):924-933

Interface developed Adjusted model (2 views) and reconstruction obtained

3D Reconstruction

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


Acknowledgments

The work presented has been done with the support of Fundação para a Ciência e a Tecnologia, in Portugal, mainly trough the funding of the research projects:

– PTDC/BBB-BMD/3088/2012 – PTDC/SAU-BEB/102547/2008 – PTDC/SAU-BEB/104992/2008 – PTDC/EEA-CRO/103320/2008 – UTAustin/CA/0047/2008 – UTAustin/MAT/0009/2008 – PDTC/EME-PME/81229/2006 – PDTC/SAU-BEB/71459/2006 – POSC/EEA-SRI/55386/2004


http://www.utaustinportugal.org/

Research Team (Computational Vision)

Research Team (Computational Vision)

• Post-Doc students (3): – Finished: Alexandre Carvalho – In course: Zhen Ma, Simone Prado

• PhD students (14): – Finished: Zhen Ma, Francisco Oliveira, Teresa Azevedo, Daniel Moura, Sandra

Rua – In course: Maria Vasconcelos, João Nunes, Alex Araujo, Carlos Gulo, Roberta

Oliveira, Danilo Jodas, Pedro Morais, Andre Pilastri, Nuno Sousa • MSc students (27):

– Finished: Carolina Tabuas, Jorge Pereira, Luis Ribeiro, Luis Ferro, Rita Teixeira, Liliana Azevedo, Diana Cidre, Célia Cruz, Priscila Alves, Pedro Gomes, Nuno Sousa, Diogo Faria, Elisa Barroso, Ana Jesus, Frederico Jacobs, Gabriela Queirós, Daniela Sousa, Francisco Oliveira, Teresa Azevedo, Maria Vasconcelos, Raquel Pinho, Luísa Bastos, Cândida Coelho, Jorge Gonçalves

– In course: Raquel Alves, André Silva, Silva Bessa • BSc students (2)

– Finished: Ricardo Ferreira, Soraia Pimenta


Publications & Events

Taylor & Francis journal “Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization”

www.tandfonline.com/tciv

Lecture Notes in Computational Vision and Biomechanics (LNCV&B) Series Editors: João Manuel R. S. Tavares, Renato Natal Jorge ISSN: 2212-9391, Publisher: SPRINGER

www.springer.com/series/8910

Events:

http://www.dem.ist.utl.pt/cmn2015/html/index_eng.html

Webpage (www.fe.up.pt/~tavares)

Analyses of objects in Images by Computer Vision: Techniques & Applications


[email protected] www.fe.up.pt/~tavares

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Analyses of objects in Images by Computer Vision ...tavares/downloads/... · Analyses of objects in Images by Computer Vision: Techniques & Applications 10 Original images Computational