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Comparative Analysisof Visual Shape Featuresfor Applications to HandPose Estimation
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Being able to determine the pose of a hand is an important

task for an artificial agent in order to facilitate a cognitive

system. Hand pose estimation, in particular - because of

its highly articulated nature, from is essential for a number

of applications such as automatic sign language recognition

and robot learning from demonstration. A typical essential

hand model is formulated using around 30-50 degrees of

freedom, implying a wide variety of possible configurations

with a high degree of self occlusions leading to ambiguities

and difficulties in automatic recognition. In addition, we

are often interested in using a passive sensor, as a cam-

era, to extract this information. These properties of hand

poses warrant robust, efficient and consistent visual shape

descriptors which can be utilized seamlessly for automatic

hand pose estimation and hand tracking.

A conducive view of the environment for its probabilis-

tic modeling, is to perceive it as being controlled from an

underlying unobserved latent variable. Given the observa-

tions from the environment (hand images) and the features

extracted from them, it is interesting to infer the state of

this latent variable which controls the generating process

of the data (hand pose). It becomes essential to investigate

- the generative methods which produce hand images from

well defined poses and the discriminative inverse problems

where a hand pose need be recognized from an observed

image. Central to both these paradigms is also the need to

formulate a measure of goodness for comparing high dimen-

sional data and separately for examining a model tailored

for some data.

In this project, three prototypical state-of-the-art vi-

sual shape descriptors, commonly used for hand and hu-

man body pose estimation are evaluated. The nature of

the mappings from the hand pose space to the feature

spaces spanned by the visual shape descriptors, in terms

of the smoothness, discriminability, and generativity of the

pose-feature mappings, as well as their robustness to noise

in terms of these properties are studied. Based on this,

recommendations are given on which types of applications

each visual shape descriptor is suitable. Novel goodness

measures are devised to quantify data similarities and to

provide a scale for the performance of these visual shape

descriptors. The evaluation of the experiments provides a

basis for creating novel and improved models for hand pose

estimation.

Abstract [sv]

Handposeigenkanning ar, inte minst pa grund av dess le-

dade natur, av central betydelse i ett flertal tillampningar

sasom igenkanning av teckensprak och robot-inlarning fran

exempel. En grundlaggande modell for en hand ar formule-

rad med mellan 30 och 50 frihetsgrader vilket medfor en stor

mangfald av mojliga konfigurationer med en hog grad av

sjalv-overlappning, vilket leder till tvetydigheter och andra

svarigheter vid automatisk igenkanning. Vidare ar det ofta

av intresse att anvanda en passiv sensor, till exempel en ka-

mera, for att hamta denna information. Dessa egenskaper

hos handposer motiverar en robust, effektiv och konsekvent

visuell formdeskriptor som somlost kan anvandas for au-

tomatisk handposeigenkanning och hand-tracking. For att

framja en probabilistisk modell av situationen, kan man se

pa den som kontrollerad av en underliggande, dold, vari-

abel. Givet observationer av situationen (hand-bilder) och

features hamtade fran dem, ar det intressant att ta fram en

indikation pa tillstandet av denna dolda variabel som styr

skapandet av datan (hand-posen). Det ar angelaget att stu-

dera dels de generativa metoder som producerar handbilder

fran valdefinierade poser och dels det inversa diskriminati-

va problemet dar en handpose ska kannas igen fran en bild.

Centralt for bada dessa problem ar att formulera ett matt

for att jamfora hogdimensionell data samt separata matt for

att utvardera modeller skraddarsydda for viss data. I det

har projektet evalueras tre olika prototyper av state-of-the-

art deskriptorer for visuella former, vilka ofta anvands for

uppskattning av mannisko- och handposer. Dessa avbild-

ningar mellan hand-poserummet och feature-rummet som

spanns upp av de visuella formdeskriptorerna utvarderas

betraffande deras jamnhet samt deras formaga att skilja

mellan olika poser. Aven deras robusthet vid brus i termer

av dessa egenskaper studeras. Utifran detta ges rekommen-

dationer gallande vilken typ av visuell formdeskriptor som

passar vid olika tillampningar. Nya matt ar utarbetade for

att kvantifiera likheter i datan samt for att ge ett prestan-

damatt for dessa visuella formdeskriptorer. Utvarderingen

av experimenten ger en grund for att skapa nya och for-

battrade modeller for handposeigenkanning.

1

Place, publisher, year, edition, pages
2013.
Series
Trita-CSC-E, ISSN 1653-5715 ; 13:124
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-138426OAI: oai:DiVA.org:kth-138426DiVA: diva2:680994
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2013-12-19 Created: 2013-12-19 Last updated: 2013-12-19Bibliographically approved

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