Human-Centric Machine Vision

188 Pages·5.528 MB·English

by  Chessa M., Solari F., Sabatini S.P. (eds.)

HUMAN-CENTRIC
MACHINE VISION

Edited by Manuela Chessa,
Fabio Solari and Silvio P. Sabatini
Human-Centric Machine Vision
Edited by Manuela Chessa, Fabio Solari and Silvio P. Sabatini

Published by InTech
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First published April, 2012
Printed in Croatia

A free online edition of this book is available at www.intechopen.com
Additional hard copies can be obtained from [email protected]

Human-Centric Machine Vision,
Edited by Manuela Chessa, Fabio Solari and Silvio P. Sabatini
p. cm.
ISBN 978-953-51-0563-3

Contents

Preface VII
Chapter 1 The Perspective Geometry of the Eye:
Toward Image-Based Eye-Tracking 1
Andrea Canessa, Agostino Gibaldi,
Manuela Chessa, Silvio Paolo Sabatini and Fabio Solari
Chapter 2 Feature Extraction Based on Wavelet Moments
and Moment Invariants inMachine Vision Systems 31
G.A. Papakostas, D.E. Koulouriotis and V.D. Tourassis
Chapter 3 A Design for Stochastic Texture Classification Methods in
Mammography Calcification Detection 43
Hong Choon Ong and Hee Kooi Khoo
Chapter 4 Optimized Imaging Techniques to Detect and Screen
the Stages of Retinopathy of Prematurity 59
S. Prabakar, K. Porkumaran, Parag K. Shah and V. Narendran
Chapter 5 Automatic Scratching Analyzing System for
Laboratory Mice: SCLABA-Real 81
Yuman Nie, Idaku Ishii, Akane Tanaka and Hiroshi Matsuda
Chapter 6 Machine Vision Application to Automatic Detection
of Living Cells/Objects 99
Hernando Fernández-Canque
Chapter 7 Reading Mobile Robots and 3D Cognitive Mapping 125
Hartmut Surmann, Bernd Moeller,
Christoph Schaefer and Yan Rudall
Chapter 8 Transformations of Image Filters for Machine Vision Using
Complex-Valued Neural Networks 143
Takehiko Ogawa
Chapter 9 Boosting Economic Growth Through
Advanced Machine Vision 165
Soha Maad, Samir Garbaya, Nizar Ayadi and Saida Bouakaz

Preface

In the last decade, the algorithms for the processing of the visual information have
greatly evolved, providing efficient and effective solutions to cope with the variability
and the complexity of real-world environments. These achievements yield to the
development of Machine Vision systems that overcome the typical industrial
applications, where the environments are controlled and the tasks are very specific,
towards the use of innovative solutions to face with everyday needs of people. In
particular, the Human-Centric Machine Vision can help to solve the problems raised
by the needs of our society, e.g. security and safety, health care, medical imaging,
human machine interface, and assistance in vehicle guidance. In such applications it is
necessary to handle changing, unpredictable and complex situations, and to take care
of the presence of humans.
This book focuses both on human-centric applications and on bio-inspired Machine
Vision algorithms. Chapter 1 describes a method to detect the 3D orientation of human
eyes for possible use in biometry, human-machine interaction, and psychophysics
experiments. Features’ extraction based on wavelet moments and moment invariants
are applied in different fields, such as face and facial expression recognition, and hand
posture detection in Chapter 2. Innovative tools for assisting medical imaging are
described in Chapters 3 and 4, where a texture classification method for the detection
of calcification clusters in mammography and a technique for the screening of the
retinopathy of the prematurity are presented. A real-time mice scratching detection
and quantification system is described in Chapter 5, and a tool that reliably determines
the presence of micro-organisms in water samples is presented in Chapter 6. Bio-
inspired algorithms are used in order to solve complex tasks, such as the robotic
cognitive autonomous navigation in Chapter 7, and the transformation of image filters
by using complex-value neural networks in Chapter 8. Finally, the potential of
Machine Vision and of the related technologies in various application domains of
critical importance for economic growth is reviewed in Chapter 9.

Dr. Fabio Solari, Dr. Manuela Chessa and Dr. Silvio P. Sabatini
PSPC-Group, Department of Biophysical and Electronic Engineering (DIBE)
University of Genoa
Italy

0
1
The Perspective Geometry of the Eye:
Toward Image-Based Eye-Tracking
AndreaCanessa,AgostinoGibaldi, ManuelaChessa,
SilvioPaoloSabatiniandFabioSolari
UniversityofGenova-PSPCLab
Italy
1.Introduction
Eye-tracking applications are used in large variety of fields of research: neuro-science,
psychology, human-computer interfaces, marketing and advertising, and computer
science. The commonly known techniques are: contact lens method (Robinson,
1963), electro-oculography (Kaufmanetal., 1993), limbus tracking with photo-resistors
(Reulenetal., 1988; Starketal., 1962), corneal reflection (Eizenmanetal., 1984;
Morimotoetal.,2000)andPurkinjeimagetracking(Cornsweet&Crane,1973;Crane&Steele,
1978).
ThankstotherecentincreaseofthecomputationalpowerofthenormalPCs,theeyetracking
system gained a new dimension, both in term of the technique used for the tracking, and
in term of applications. In fact, in the last years raised and expanded a new family of
techniquesthatapplypassivecomputervisionalgorithmstoelaboratetheimagessotoobtain
the gaze estimation. Regarding the applications, effective and real-time eye tracker can
be used coupled with a head tracking system, in order to decrease the visual discomfort
in an augmented reality environment (Chessaetal., 2012), and to improve the capability
of interaction with the virtual environment. Moreover, in virtual and augmented reality
applications, the gaze tracking can be used with a display with variable-resolution that
modifies the image in order to provide a high level of detail at the point of gaze while
sacrificingtheperiphery(Parkhurst&Niebur,2004).
Groundingtheeyetrackingontheimageoftheeye,thepupilpositionisthemostoutstanding
feature in the image of the eye, and it is commonly used for eye-tracking, both in corneal
reflections and in image-based eye-trackers. Beside, extremely precise estimation can be
obtainedwitheyetrackerbasedonthelimbusposition(Reulenetal.,1988;Starketal.,1962).
Limbus is the edge between the sclera and the iris, and can be easily tracked horizontally.
Because of the occlusion of the iris done by the eyelid, limbus tracking techniques are
very effective in horizontal tracking, but they fall short in vertical and oblique tracking.
Nevertheless, the limbus proves to be a good feature on which to ground an eye tracking
system.
Startingfromtheobservationthatthelimbusisclosetoaperfectcircle,itsprojectiononthe
image plane of a camera is an ellipse. The geometrical relation between a circle in the 3D
2 Human-Centric Machine Vision
2 Will-be-set-by-IN-TECH
spaceanditsprojectiononaplanecanbeexploitedtogatheraneyetrackingtechniquethat
resortsonthe limbus positionto track the gaze directionon3D. In fact, the ellipseand the
circlearetwosectionsofanellipticconewhosevertexisattheprincipalpointofthecamera.
Once the points that define the limbus are located on the image plane, it is possible to fit
theconicequationthatisasectionofthiscone.Thegazedirectioncanbeobtainedcomputing
whichistheorientationinspaceofthecirclethatproducesthatprojection(Forsythetal.,1991;
Wangetal.,2003). Fromthisperspective,the morethelimbusdetectioniscorrect,themost
theestimationofgazecomestobepreciseandreliable.Inimagebasedtechniques,acommon
waytodetecttheirisisfirsttodetectthepupilinordertostartfromaguessofthecenterofthe
irisitself,andtoresortonthisinformationtofindthelimbus(Labati&Scotti,2010;Mäenpää,
2005;Ryanetal.,2008).
Commonlyinsegmentationandrecognitiontheirisshapeontheimageplaneisconsideredto
becircular,(Kyung-Nam&Ramakrishna,1999;Matsumoto&Zelinsky,2000)andtosimplify
thesearchforthefeature,theimagecanbetransformedfromaCartesiandomaintoapolar
one(Ferreiraetal.,2009;Rahib&Koray,2009). Asamatteroffact,thisistrueonlyiftheiris
plane is orthogonal to the optical axis of the camera, and fewalgorithms take into account
theprojectivedistortionspresentinoff-axisimagesoftheeyeandbasethesearchfortheiris
onanellipticshape(Ryanetal.,2008).Inordertorepresenttheimageinadomainwherethe
ellipticalshapeisnotonlyconsidered,butalsoexploited,wedevelopedatransformationfrom
theCartesiandomaintoan“elliptical”one,thattransformboththepupiledgeandthelimbus
into straight lines. Furthermore, resorting on geometrical considerations, the ellipse of the
pupil can be used to shape the iris. In fact, even though the pupil and the iris projections
are not concentric, their orientation and eccentricity can be considered equal. From this
perspective, a successful detection of the pupil is instrumental for iris detection, because it
allowsforadomaintobeusedfortheellipticaltransformation,anditconstrainsthesearch
fortheirisparameters.
Thechapterisorganizedasfollows:inSec.3wepresenttheeyestructure,inparticularrelated
to pupil and iris, and the projective rule on the image plane; in Sec. 4 we show how to fit
the ellipse equation on a set of points without any constraint or given its orientation and
eccentricity; inSec.5wedemonstratehowtosegmentthe iris,resortingontheinformation
obtained by the pupil and we show some results achieved on an iris database and on the
imagesacquiredbyoursystem;inSec.6weshowhowthefittedellipsecanbeusedforgaze
estimationandinSec.7weintroducesomediscussionsandwepresentourconclusion.
2.Relatedworks
The study of eye movements anticipates the actual wide use of computers by more than
100 years, for example, Javal (1879). The firstmethods to track eye movements were quite
invasive, involving direct mechanical contact with the cornea. A first attempt to develop
a not invasive eye tracker is due to Dodge&Cline (1901) which exploited light reflected
fromthecornea. Inthe1930s, MilesTinkerandhiscolleaguesbegantoapplyphotographic
techniques to study eye movements in reading (Tinker, 1963). In 1947 Paul Fitts and his
colleagues began using motion picture cameras to study the movements of pilots’ eyes as
they used cockpit controls and instruments to land an airplane (Fittsetal., 1950). In the
sametearsHartridge&Thompson(1948)inventedthefirsthead-mountedeyetracker. One