Non-Rigid Graph Registration using Active Testing Search

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00226745" target="_blank" >RIV/68407700:21230/15:00226745 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Serradell-PAMI2015.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Serradell-PAMI2015.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2014.2343235" target="_blank" >10.1109/TPAMI.2014.2343235</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-Rigid Graph Registration using Active Testing Search

Popis výsledku v původním jazyce

We present a new approach for matching sets of branching curvilinear structure s that form graphs embedded in $mathbb{R}^2$ or $mathbb{R}^3$ and may be subject to deformations. Unlike earlier method s, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformatio ns, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian Processes to represen t the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches consider ed at each iteration is not selected at random as in many RANSAC-based algorithms. Instead, we introduce a so-called Active Testin g Search

Název v anglickém jazyce

Non-Rigid Graph Registration using Active Testing Search

Popis výsledku anglicky

We present a new approach for matching sets of branching curvilinear structure s that form graphs embedded in $mathbb{R}^2$ or $mathbb{R}^3$ and may be subject to deformations. Unlike earlier method s, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformatio ns, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian Processes to represen t the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches consider ed at each iteration is not selected at random as in many RANSAC-based algorithms. Instead, we introduce a so-called Active Testin g Search

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GAP202%2F11%2F0111" target="_blank" >GAP202/11/0111: Automatická analýza obrazů nervové tkáně ze světelné a elektronové mikroskopie</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

—

Svazek periodika

37

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

625-638

Kód UT WoS článku

000349626200011

EID výsledku v databázi Scopus

2-s2.0-84923013613