Computer Vision & Smart Embedded Systems: R&D&I in Applied ...€¦ · Human eye cross-sectional...

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Computer VisionEmbedded Systems

BCAM: The BBIPED platform

Computer Vision & Smart Embedded Systems:R&D&I in Applied Research

Carmen Alonso Montes

Basque Center for Applied Mathematics

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September 16th, 2013

Carmen Alonso Montes Computer Vision & Smart Embedded Systems

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Technical Background

MSc. Software Engineering, University of A Coruna (Spain) 2003

1 paper has been published in a peer-review conference from the results of my MasterThesis 3D Object Surface Reconstruction using Growing Self-Organized Networks,CIARP 2004

PhD in Artificial Intelligence and Computer Science in 2008 (University of A Coruna)

Automatic Pixel-Parallel Extraction of the Retinal Vascular Tree: Algorithm Design,On-Chip Implementation and ApplicationsResearch Stays: University of Manchester (UK) 2006,2007

Professional experience in several R&D&I projects in two companies: Tecnalia Research &Innovation and IK4-Ideko.

Research interest: medical image processing, parallel computing architectures, neuralnetworks, pattern recognition, embedded systems, decision support systems, ambientintelligence...

Motivation

Current state of the art in the IT research trends focusing on:

Computer vision techniques based on neural networks applied to several fields (Health,Security & Surveillance, Machine Tool)Smart Embedded Systems focusing mainly in ambient intelligence, software productlines and support-decision systems


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Medical Imaging ApplicationsIndustrial Image Processing

Outline

1 Computer VisionMedical Imaging ApplicationsIndustrial Image Processing

2 Embedded Systems

3 BCAM: The BBIPED platform


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Computer Vision: An overview

Computer vision field includes methods for acquiring, processing, analyzing, and understandingimages in order to be used or transformed in the form of decisions within high-level systems.

Medical Imaging

Management, analysis and processing of medical images of the human body.

Typical applications: disease diagnostics, biometric analysis ...

Several medical image sources: radiography, magnetic resonance imaging (MRI), X-raycomputed tomography (CT), ultrasound ...


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Application 1: 3D Medical Surface Reconstruction

The Visible Human Project, project run by the U.S. National Library of Medicine (NLM) underthe direction of Michael J. Ackerman. (1989)

Detailed data set of cross-sectional photographs of the human body, easing anatomyvisualization applications

Challenge: to guarantee surface continuity and spatial-topological relations in automaticapproaches

Alternative: Neural Networks, in particular from Self-Organised Maps (SOM)


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3D Object Surface Reconstruction

3D Object Surface Reconstruction using Growing Self-Organised Networks,C. Alonso-Montes and M. G. Penedo, LNCS CIARP 2004, pp 163-170

Goal: Comparison of different SOM neural networks to compute 3D mesh surface

Solution: Growing Neural Gas (GNG) with 2-step training (fast to get closer to the target andslower for fine adjustment) using weighted training patterns (position,gradient)

Good results in terms of accuracy and topological maintenance


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Application 2: Retinal Vessel Tree Extraction

Human eye cross-sectional view Non-Mydriatic Canon CR6-45NM

Motivation

Non-Mydriatic Cameras capture ultra high-resolution digital images of the eye fundus withoutusing dilation drops

Goal: Extraction of the retinal vessel tree to be used in medical or biometric applications

Bottleneck: High computation effort required for the extraction of the retinal vessel tree

Solution: Design of an algorithm to extract the retinal vessel tree at a high computationspeed taking advantage of Cellular Neural Networks (CNN) and SIMD processors


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Automatic Pixel-Parallel Extraction of the Retinal Vascular Tree

Image features

Different vessel widths, vessellow contrast

Retina boundary, optic disk,pathologies...

Central reflex causing acomplicated intensitycross-section

Challenge: Real-timerequirements

Alternative: Active Contours

Solution

Pixel Level Snakes (PLS): resolve the highcomputational cost of classic active contour

Pixel level discretization of the contoursMassively parallel computation on everycontour cellImplemented on HW SIMD architectures

Strategy: Fitting the exterior of the vessels

Robust control of the evolutionEasier initialization (only 12.7% of pixelsbelong to vessels)


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Pixel Level Snakes (PLS)

External PotentialElastic curve

u(s) = (x(s), y(s)), s ∈ [0, 1]Evolves from its initial shape and position as a result of the combined action of

External forces: Guide the contours towards the features of interestInternal forces: Control the smoothness of the contourBalloon Potential: Guide the contours when the external potential is too weak

Main input imagesInitial contourExternal potential image (guiding information image)

Mathematical definition of the potential field

P(x, y) = kint Pint (x, y) + kext Pext (x, y) + Kinf Pinf (x, y)


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Pixel parallel retinal vessel tree extraction algorithm

Goal

Automatic computation of the initial conditions from the original image and their parametriccalibration to fit the vessels

Definition of an implementable HW version for a pixel parallel processor

Stage 1: Vessel pre-estimation , Pre-filtering steps to improve the signal-to-noise rationto pre-estimate vessel locationsStage 2: Initial contour estimation , determines the initial conditions for PLSStage 3: External potential estimation , computes the guiding informationStage 4: PLS evolution


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Stage 1: Vessel Pre-estimation

An adaptive segmentation is needed

Diffusion gives a suitable local threshold

The substraction of the original and diffused image gets a suitable segmentation of the image

A local threshold value is used to refine the final result


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Stage 2: Initial contour estimation

Computation of suitable initial conditions for the vessel removing vessel discontinuities

We need to assure that the initial image needed by PLS are completely outside of the vessellocations

Step 1. Dilation : Several dilations are actually needed to remove the discontinuitiesStep 2. Binary edge detection : To get the initial contours

Fitting the exterior of the vessels simplifies the computation of the initial contours (Noticethat only 12.7% of pixels belongs to vessels)


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Stage 3. External potential estimation

The application of Sobel operator obtains accurate edges, but weak vessels are not properlysegmented and vessels topology is neither maintained nor assured

The image segmented in Stage 1 contains more vessel information and also noise

The combination of both results will gives more robustness to control PLS evolution

A distance estimation is made to guide the evolution towards the vessel locations. This mapis computed by several dilations followed by a diffusion to smooth the values

The diffusion step leads towards a loose of accuracy of the boundary location, so edgesmust be emphasized


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Stage 4: PLS evolution

Input images have been automatically computed from local statistics of the original image

Main parameters of PLS should be calibrated to control the evolution towards the vessels


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Final algorithm: General scheme

Automatic Pixel-Parallel Extraction of the Retinal Vascul ar Tree: Algorithm design, on-chipimplementation and applications Carmen Alonso Montes, PhD Thesis, 18 July 2008


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Experimental results

DRIVE: Digital Retinal Images for Vessel Extraction database

40 images available (7 images with pathologies and 33 images without diseases) with aresolution of 768x584

The maximum size allowed in the chip implementations, used in this thesis, is 128x128


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Method MAAManual Method 0.9473Soares [1] 0.9466Al-Rawi [2] 0.9458Kirsch [3] 0.9151Staal [4] 0.9611Chaudhuri [5] 0.8773Proposed algorithm 0.9180

Analysis of the accuracy

Maximum Average Accuracy (MAA)

Accuracy =Tpos + Tneg

NP

Tpos is the vessel (true positive) correctly classified pixelsTneg is the non-vessel (true negative) correctly classified pixelsNP is the number of pixels considered into the FOV region

A total number of 20 images (from the test set) has been used

The manual segmentation of the second observer has been used as the gold standard


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Time performance Analysis: SCAMP vision-3 implementation

High-performance (1.25 MOPS per pixel) low-power (250mW at the maximum processing)solution for computer vision applications

The processor array operates under the SIMD (Single Instruction Multiple Data) paradigm

The processing elements simultaneously execute identical instructions (addition, inversion,one-neighbour access) on their local data


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Summary

Execution time required for a 128x128 sub window

No. Stage Stage Exec. Time ( µs)1 Vessel Region Pre-estimation 12.82 Initial Region Estimation 55.23 External Potential Estimation 134.4

41st PLS Step (6 cycles) 518Hole Filling 1954.52nd PLS Step (40 cycles) 3870.8

Analysis of the execution time

128x128 windows are considered to perform the algorithm in the SCAMP

The I/O time required is 1.25 ms

The execution time for a sub window is 6.5 ms

The global execution time for a retinal image is about 0.1925 s (approximately 30 subwindows)


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Applications

The algorithm proposed in this thesis has been included into the following applications:

Authentication applications use the vessel-pattern due to its robustness against forgery

Creases-based authentication systemPoint feature-based authentication system

Medical applications use the vessel-pattern for early diagnosis, based on thearteriolar-to-venular diameter ratio estimation ( stenosis, malformations, cardiovascular risk)


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Authentication applications

Notes

Both of these systems use the skeleton instead of the retinal vessel tree

An skeletonisation step has been included to process the output of the pixel-parallel algorithm

Goal: Improving the computation time for obtaining the retinal vessel tree


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Authentication system using point feature extraction


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Experimental results

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Conclusions

False Acceptance (FAR) and False Rejection (FRR) rates can be reduced to 0 (FAR = FRR)

Equal Error Rate (EER) = 0 which implies a 100% of effectiveness

The mean execution time is about 0.19 s to get the skeleton, 250 ms. for the authenticationstage

The whole execution time for the authentication system is 0.44 s.


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Arteriolar-to-Venular Ratio Estimation Application

Steps in the system

1 Selection of the retinal image by the specialist

2 Concurrently extraction of the retinal vessel tree

3 Selection of the optic disk

4 Drawing three circles, concentric to the optic disk

5 Obtaining crossing points

6 Estimation of the vessel diameter

7 Manual classification of the two types of vessels into vein or artery

8 Computation of the AVR ratio


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Pixel-Parallel Tree Extraction: New developments

This algorithm has been used as benchmark for the Artificial Vision Group (Univ.Santiago de Compostela) since 2008

A new implementation on a DSP and a PC (Intel Core Duo 2.10GHz) was made (6.64sfor the whole image) (2008)A new hybrid HW development (SIMD & MIMD) to deal with computer vision algorithms(tested under a FPGA-based System-on-Chip) 1

The Artificial Vision Group has already started the formal process for patent this chip

The future: A portable device capable of hosting complex artificial vision algorithms

The algorithm as well as the HW developments can tackle with applications withreal-time computation requirements

Different fields: medicine, video-based applications, robotics, ...

The increment on the size of processor arrays will balance the distance againstPC-based

Alternatives like GPGPU could overcome some of the current challenges but at the costof high algorithm complexity

1Dynamically reconfigurable architecture for embedded Computer Vision systems, Alejandro Manuel Nieto Lareo(CITIUS, Univ. Santiago de Compostela), Dec 2012


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Machine Vision

Image processing within industrial environments is usually called machine vision

Typically, machine vision applications must deal with issues like: dust, fat, poorillumination conditions, real-time constraints, physical restrictions within themachines, etcThis presentation will focus on:

Laser-based processNon Destructive Techniques (NDT) for railway inspectionIdentification of Artificial Markers for machining process


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Laser-based process

Laser cladding is a metal surface strengthening process where the controllerlaser heat is used combined with powder metal material applied onto any surface,usually for maintenance and repairing purposes.

Laser beam welding (LBW) is a welding technique used to join multiple pieces ofmetal through the use of a laser

Typical machine vision applications

Analysis of the turbine blade shape

To compute its thickness and compute the suitable laser power potentialTo provide a set of coordinates for laser movement and alignment of the laser head

Computation of melt pool width for laser power control purposes in LBW applications

Non-destructive Techniques (NDT)

Non Destructive Testing (NDT) is a wide group of analysis techniques used toevaluate the properties of a material, component or system without causing anydamage. Some of their techniques are used within inspection and quality analysis,defects identification, detection of surface flaws,etc

NDT techniques examples are: Visual Inspection (VT), Radiographic Testing (RT)or Ultrasonic inspection (UI).

Fields of usage: Railway inspection, aerospace, automotive, machine toolCarmen Alonso Montes Computer Vision & Smart Embedded Systems

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Machining: Using artificial markers

CT Structure ExampleCT Structure used in

the experimentsDecoded CT Example

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Goal: Automatic raw part alignment for machining process by means of artificial CodedTargets (CT) within a Phogrammetry-based ApplicationMiguel Fernandez-Fernandez, Carmen Alonso-Montes, et al.: Industrial Non-intrusiveCoded-Target Identification and Decoding Application. IbPRIA 2013: 790-797


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Software Product Lines ParadigmSmart Systems & Ambient IntelligenceKnowledge Based Systems (KBS)

Outline

1 Computer Vision

2 Embedded SystemsSoftware Product Lines ParadigmSmart Systems & Ambient IntelligenceKnowledge Based Systems (KBS)



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Embedded Systems: An overview

Definition: An Embedded System is a computer system with a dedicated functionwithin a larger mechanical or electrical system, often with real-time computingconstraints

They can be found everywhere: consumer, cooking, industrial, automotive,medical, commercial and military applications

They are designed to do some specific task, rather than be a general-purposecomputer for multiple tasks.

Embedded software (firmware) is computer software written to specificallycontrol machines or devices, with those particular hardware fulfilling time andmemory constraints


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Software Product Lines: The theory

Systematic Software Reuse is for

Organizations developing similar projects leading to a family of software products orapplicationsMost units developing software may look at their production as a family of similarapplications

Success Story: Nokia phone series


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Product Line Unified Modeler (PLUM): A decision-based SPL tool

Tool suite for the design, implementation and management of Software Product Lines(SPL) following a Model-Driven Software Development approach.

Projects

The PLUM tool and SPL technologies have been one of the core technologies in severalinternational projects (e.g. CESAR, MOSIS,FLEXI) as well as customer projects (e.g.REUSE-SECC with the IT Ministery of Egypt). Currently, there is a formal proposal in the OMG formaking the CVL language as standard.


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CESAR project: Pilot Case

CESAR stands for Cost-efficient methods and processes forsafety relevant embedded systems. Three transportationdomains automotive, aerospace, and rail, as well as theautomation domain

Pilot Case: A pilot was proposed to define a SPL tool-chain for the whole plane designprocess (from business towards HW design)

The PLUM tool was mainly used for the high level decisions (business and SW model)whereas other options like CVL or feature modelling are used for low level decisions (HW)

Product Line Tool-Chain: Variability in Critical Systems , PLEASE 2012 (C. Alonso-Montes etal.)


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Smart Systems & Ambient Intelligence: An overview

Ambient Intelligence paradigm (AmI) refers to those environments capable of interactingwith humans. It is mainly build upon ubiquitous computing, context awareness, andhuman-centric computer interaction design

Smart systems are those devices capable of taking decisions based on their own capabilitiesand the interaction with their environment

The key: Ontology

Ontologies are formal representations of a set of domain concepts and their relationships,that provides a common domain vocabulary

They are widely used in areas like: Knowledge Management, Natural Language Processing(NLP), Intelligent Information Integration, Bio-informatics, and the Semantic Web

Advantages: standardization, allow reasoning over the formal models, scalability,interoperability


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SOFIA Project: A middleware for smart systems

SOFIA project aims to make information in the physical worldavailable for smart services, connecting both physical andinformation world, maintaining cross-industry interoperabilityProjects: SOFIA, SMARCOS, CHIRON, SMOOL, LIFEWEAR


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Knowledge Based System (KBS): An overview

Knowledge Based System is a computer system that emulates thedecision-making ability of a human expert, giving reasoning capabilities over theacquired knowledge.

Parts of the KBS: the inference engine and the knowledge base.

Where they are used?: Autonomous robots or pure software agents

Challenges: The extraction of the expert Knowledge (Knowledge Engineering)

R3-COP project : Resilient reasoning robotic cooperating systems

The project aims to create a cross-domain platform of methods and tools for the design,development and validation of autonomous systems.

These systems will be able to reason, learn and cooperate in different application domains:surveillance and rescue, agriculture, people care, home environments and transport.

Erwin Schoitsch, Wolfgang Herzner, Carmen Alonso-Montes, P. Chmelar, Lars Dalgaard: TowardsComposable Robotics: The R3-COP Knowledge-Base Driven Tec hnology Platform .SAFECOMP Workshops 2012: 427-435


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R3-COP: Reasoning and mission planning

Target: Take a decision

Goal: To take the best decision according real conditions, learning based onexperience and maintaining the consistency of the acquired knowledge


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R3-COP: Validation & Verification

Verification and Validation are independent procedures that are used togetherfor checking that a product, service, or system meets requirements andspecifications and that it fulfils its intended purpose.

Validation: Are we building the right product?

Verification: Are we building the product right?

The R3-COP Robotic Reference Technology Platform: Interop erability Issues E. Schoitsch, W.Herzner (AIT, Austria), C. Alonso-Montes (Tecnalia, Spain),P. Chmelar (TU Brno, Czech Republic),Lars Dalgaard (DTI, Denmark)


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Outline

1 Computer Vision

2 Embedded Systems



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The BBIPED platform: The GUI

The BCAM-Baltogar CFD Platform is tailored for a maximum performance on turbo-fans tomeet industrial requirements in terms of accuracy, efficiency, cost-effectiveness, robustnessand geometric flexibility.

The BBiped platform will unify the CFD process from mesh generation till the resultsvisualization, under a common graphical user interface (GUI). Among other features, this GUIwill provide an standardized and easy configuration process for the SU2 platform and thosenew proposals developed by the BCAM CFD team

Features

Easy configuration view

Graphical residual view

Easy versioning for simulations

Advanced and Simplified

Configuration views


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BBIPED Platform: Features

Common platform that homogenizes the usage of SU2 tool with the others

Easing project version and associated documentation storage and trackThe SU2 configuration file features:

Easing the configuration of the simulation with different viewsBasic configuration view: Only for those engineers that need to change some values,but they have no depth knowledge about SU2Advanced configuration view: For expert users for a more customized configuration

Automatic generation of configuration files for simulation according the user input(single files, keeping several files versioning, etc)

Automatic launch of SU2 running with your own configuration and mesh files fromthe platformThe SU2 simulation features:

Graphical evolution of the simulationCustomized graphical views based on different parameters selected by the user

Integration with Paraview and with Salome Platform


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Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J.:Retinal Vessel Segmentation using the 2-D Gabor Wavelet and SupervisedClassification.IEEE Trans. Med. Imag. 25 (2006) 1214–1222

Al-Rawi, M., Qutaishat, M., Arrar, M.:An improved matched filter for blood vessel detection of digital retinal images.Comput. Biol. Med. 37(2) (2007) 262–267

Kirsch, R.A.:Computer Determination of the Constituent Structure of Biological Images.Comp. Biomed. Res. 4(3) (1971) 315–328

Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.:Ridge-Based Vessel Segmentation in Color images of the Retina.IEEE Trans. Med. Imag. 23 (2004) 501–509

Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.:Detection of Blood Vessels in Retinal Images using Two-Dimensional MatchedFilters.IEEE Trans. Med. Imag. 8 (1989) 263–269


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