Structural Image Restoration through Deformable Templates

Amit, Yali; Grenander, Ulf; Piccioni, Mauro

doi:10.1080/01621459.1991.10475053

Cited by 263 publications

(132 citation statements)

References 6 publications

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“…In this paper, we have presented three concepts that have been extensively used in image matching fields: templates [5,31], landmarks [31,33] and trust regions [32].…”

Section: Related Workmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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a b s t r a c tPredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.

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“…In this paper, we have presented three concepts that have been extensively used in image matching fields: templates [5,31], landmarks [31,33] and trust regions [32].…”

Section: Related Workmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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show abstract

“…The approach presented in this paper is based on Deformable Templates [1], [10], [11], [15], [6], [28], [29], [30], [31], [20], [26]. In the context of intravascular ultrasound imaging we introduce improvements mainly in two directions: 2.…”

Section: The Proposed Approach: Outline Of the Papermentioning

confidence: 99%

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Escolano

Cazorla

Gallardo

et al. 1997

Lecture Notes in Computer Science

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“…These deformations prevent the use of usual methods in data analysis and deserve a special statistical treatment in order to align the data. We refer for instance to (Gamboa et al, 2007), (Dupuy et al, 2011), (Ramsay and Silverman, 2005), (Bercu and Fraysse, 2012) and references therein to applications to functional data analysis, to (Trouve and Younes, 2005) or (Amit et al, 1991) for applications in image analysis or to (Bolstad et al, 2003) for applications in biology.…”

Section: Introductionmentioning

confidence: 99%

A parametric registration model for warped distributions with Wasserstein’s distance

Agulló-Antolín

Cuesta-Albertos

Lescornel

et al. 2015

Journal of Multivariate Analysis

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We consider a parametric deformation model for distributions. More precisely, we assume we observe J samples of random variables which are warped from an unknown distribution template. We tackle in this paper the problem of estimating the individual deformation parameters. For this, we construct a registering criterion based on the Wasserstein distance to quantify the alignment of the distributions. We prove consistency of the empirical estimators.

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Structural Image Restoration through Deformable Templates

Cited by 263 publications

References 6 publications

Predefined pattern detection in large time series

Predefined pattern detection in large time series

Deformable templates for tracking and analysis of intravascular ultrasound sequences

A parametric registration model for warped distributions with Wasserstein’s distance

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