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Segmentation of Prostate Using Interactive Finsler Active Contours and Shape Prior [chapter]

Foued Derraz, Abdelmalik Taleb-Ahmed, Azzeddine Chikh, Christina Boydev, Laurent Peyrodie, Gerard Forzy
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We first explicitly address the segmentation problem based on fast globally Finsler Active Contours (FAC) by incorporating both statistical and geometric shape prior knowledge.  ...  In addition, once the prostate shape has been segmented, a cost functional is designed to incorporate both the local image statistics as user feedback and the learned shape prior.  ...  We proposed to segment prostate shape using a fast version of the interactive Finsler Active contours in the TV framework. We proposed a two stage fast globally Finsler Active Contours (FAC).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-31254-0_45">doi:10.1007/978-3-642-31254-0_45</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/egx7p4psnbaoxpofnwb3tpnyhu">fatcat:egx7p4psnbaoxpofnwb3tpnyhu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180729051026/https://link.springer.com/content/pdf/10.1007%2F978-3-642-31254-0_45.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/62/c762444f3070c4cd5afc06b422e68e055b9092fb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-31254-0_45"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

Xavier Bresson, Pierre Vandergheynst, Jean-Philippe Thiran
<span title="2006-03-01">2006</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfdglwo5wbbmta6wop52fam7a4" style="color: black;">International Journal of Computer Vision</a> </i> &nbsp;
In this paper, we propose a variational model for object segmentation using the active contour method, a geometric shape prior and the Mumford-Shah functional.  ...  We propose an energy functional composed by three terms: the first one is based on image gradient, which detects edges, the second term constrains the active contour to get a shape compatible with a statistical  ...  Active contour is in solid line and the shape prior in dotted line. Figures (a)-(c) show the matching of a cat (initial active contour) into a cow (shape prior).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11263-006-6658-x">doi:10.1007/s11263-006-6658-x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3z3o2tiwwzfwregfgkthixlv5a">fatcat:3z3o2tiwwzfwregfgkthixlv5a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830035730/http://math.ipm.ac.ir/conferences/2004/computervision2004/lectures/Thiran/shapes.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/30/b330f10466bbc5bdf72446ba83040ad2015737e4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11263-006-6658-x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Covariance Matching for PDE-based Contour Tracking

Bo Ma, Yuwei Wu
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wb67xmkcafgm3auyv65al6i2ey" style="color: black;">2011 Sixth International Conference on Image and Graphics</a> </i> &nbsp;
This paper presents a novel formulation for object tracking. We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.  ...  Specifically, covariance matrix is adopted as a visual object representation for partial differential equation (PDE) based contour tracking.  ...  We model visual tracking as a covariance template matching problem under the geometric active contour framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icig.2011.88">doi:10.1109/icig.2011.88</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icig/MaW11.html">dblp:conf/icig/MaW11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j6qqwysidjaptejftlu2in25lu">fatcat:j6qqwysidjaptejftlu2in25lu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812012308/http://iitlab.bit.edu.cn/mcislab/~wuyuwei/paper_pdf/icig2011_covariance%20matching%20for%20pde-based%20contour%20tracking.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4c/26/4c267c7422946c81053fa07060cedf40e51caa6c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icig.2011.88"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Nonlinear shape prior from Kernel space for geometric active contours

Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum, Edward R. Dougherty, Jaakko T. Astola, Karen O. Egiazarian, Nasser M. Nasrabadi, Syed A. Rizvi
<span title="2006-02-02">2006</span> <i title="SPIE"> Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning </i> &nbsp;
The Geometric Active Contour (GAC) framework, which utilizes image information, has proven to be quite valuable for performing segmentation.  ...  In the present work, we derive the steps for using Kernel PCA to in the GAC framework to introduce prior shape knowledge. Several experiments were performed using different training-sets of shapes.  ...  CONCLUSION In this paper, we presented a framework to introduce shape priors for geometric active contours, which relies on a powerful and unsupervised statistical learning technique.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.641708">doi:10.1117/12.641708</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/de3a4gaqdrb5be4ekekan2vcqu">fatcat:de3a4gaqdrb5be4ekekan2vcqu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100620120051/http://www.bme.gatech.edu/groups/minerva/publications/papers/dambreville06-spie.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0c/f0/0cf02da493dbf723d0d695237bbb306c83740106.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.641708"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Implicit Active Shape Models for 3D Segmentation in MR Imaging [chapter]

Mikaël Rousson, Nikos Paragios, Rachid Deriche
<span title="">2004</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper we re-visit active shape models and introduce a level set variant of them.  ...  In parallel, level set methods [8] define a powerful optimization framework, that can be used to recover objects of interest by the propagation of curves or surfaces.  ...  For this purpose a variational formulation incorporating two terms is used: E(φ, A, λ) = b E shape (φ, A, λ) + (1 − b) E data (φ) where E shape is the shape prior and E data is the Geodesic Active Region  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-30135-6_26">doi:10.1007/978-3-540-30135-6_26</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/reej45jrpnh65er2d5gmsa5ml4">fatcat:reej45jrpnh65er2d5gmsa5ml4</a> </span>
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Using Prior Shape and Points in Medical Image Segmentation [chapter]

Yunmei Chen, Weihong Guo, Feng Huang, David Wilson, Edward A. Geiser
<span title="">2003</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper we propose a new variational framework for image segmentation that incorporates the information of expected shape and a few points on the boundary into geodesic active contours.  ...  We also report experimental results on synthetic images and ultrasound images, and compare them with the results of using the model in [C] that only incorporates shape prior into active contours.  ...  Conclusion In this paper we proposed the addition of prior points to an active conotu with shape in a variational framework and in level set formulation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-45063-4_19">doi:10.1007/978-3-540-45063-4_19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5i7q47szyzb7virkuuor62bsva">fatcat:5i7q47szyzb7virkuuor62bsva</a> </span>
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Fast Finsler Active Contours and Shape Prior Descriptor [chapter]

Foued Derraz, Abdelmalik Taleb-Ahmed, Laurent Peyrodie, Gerard Forzy, Christina Boydev
<span title="">2011</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The FFAC is formulated in the Total Variation (TV) framework incorporating both region and shape descriptors.  ...  We prove the existence of a solution to the new binary Finsler active contours model and we propose a fast and easy algorithm in characteristic function framework.  ...  In the figure 2, the object shape is segmented using 20 learned shapes and the segmentation is done using statistical and geometric shape prior.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-25085-9_22">doi:10.1007/978-3-642-25085-9_22</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c7e4y4inkvextak7bikvprzdlu">fatcat:c7e4y4inkvextak7bikvprzdlu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505120352/https://link.springer.com/content/pdf/10.1007%2F978-3-642-25085-9_22.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f8/2a/f82a40f2e94a5d62982a4f21a2011d8c7150437b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-25085-9_22"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

LOCALLY ADAPTIVE AUTOREGRESSIVE ACTIVE MODELS FOR SEGMENTATION OF 3D ANATOMICAL STRUCTURES

Charles Florin, Nikos Paragios, Gareth Funka-Lea, James Williams
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qcepwfkflvg5toaa6fh2alj3b4" style="color: black;">2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro</a> </i> &nbsp;
In this paper, we introduce a novel family of shape prior models that aim to capture such varying support.  ...  A quantitative comparative study with 3D Active Shape Models demonstrate the potential of the method.  ...  In this paper, we model the trajectory of the contour in the feature space, and use it as a prior for segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi.2007.357072">doi:10.1109/isbi.2007.357072</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/isbi/FlorinPFW07.html">dblp:conf/isbi/FlorinPFW07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kbjugueamjhjphbuvg4dkdzzhe">fatcat:kbjugueamjhjphbuvg4dkdzzhe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20070820114414/http://vision.mas.ecp.fr/pub/isbi07-02.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/32/0e/320ea6f98f4355dad9b882466998f9c1b2d88c11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi.2007.357072"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Level set segmentation with robust image gradient energy and statistical shape prior

S. Y. Yeo, X. Xie, I. Sazonov, P. Nithiarasu
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/anlh4tvwprcrtoxv5d4h6a7rye" style="color: black;">2011 18th IEEE International Conference on Image Processing</a> </i> &nbsp;
We propose a new level set segmentation method with statistical shape prior using a variational approach. The image energy is derived from a robust image gradient feature.  ...  This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations.  ...  INTRODUCTION Active contours provide an effective framework for object segmentation as they can easily adapt to shape variations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icip.2011.6116439">doi:10.1109/icip.2011.6116439</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icip/YeoXSN11.html">dblp:conf/icip/YeoXSN11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xzowbzdkgzhvfolcv47zvtv6fq">fatcat:xzowbzdkgzhvfolcv47zvtv6fq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808215149/http://csvision.swan.ac.uk/uploads/Site/Publication/sy-icip2011.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/03/e2/03e27cde12a3dda681d2dead916183b991b13f05.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icip.2011.6116439"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Tracking deformable objects with unscented Kalman filtering and geometric active contours

S. Dambreville, Y. Rathi, A. Tannenbaum
<span title="">2006</span> <i title="IEEE"> 2006 American Control Conference </i> &nbsp;
In the present work, we propose to use the unscented Kalman filter together with geometric active contours to track deformable objects in a computationally efficient manner.  ...  Geometric active contours represented as the zero level sets of the graph of a surface have been used very successfully to segment static images.  ...  In [19] the authors use particle filters in combination with geometric active contours for tracking deformable objects.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/acc.2006.1657152">doi:10.1109/acc.2006.1657152</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ei77d6ykcrh2tlqg4nw6oumd2u">fatcat:ei77d6ykcrh2tlqg4nw6oumd2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20060901213920/http://www.bme.gatech.edu/groups/minerva/publications/papers/dambreville06-acc.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/64/ab64586c26dcce9e19209facb3111ebdb9b0b6ed.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/acc.2006.1657152"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Segmentation of biomedical images using active contour model with robust image feature and shape prior

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu
<span title="2013-10-28">2013</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wy5icxf3uzd53k62u67jkxqtki" style="color: black;">International Journal for Numerical Methods in Biomedical Engineering</a> </i> &nbsp;
The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing  ...  Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations.  ...  Active contours or deformable models provide an effective framework for object segmentation and has been widely used in biomedical image segmentation, as they can easily adapt to shape variations [17]  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cnm.2600">doi:10.1002/cnm.2600</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24493403">pmid:24493403</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4204158/">pmcid:PMC4204158</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j7inloucrzaozjeena7ep2xta4">fatcat:j7inloucrzaozjeena7ep2xta4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170705062724/http://csvision.swan.ac.uk/uploads/Site/Publication/S.Yeo.IJNMBE.2014.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a0/45/a0458cab54233486f58a8670b60b25198e667727.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cnm.2600"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> wiley.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4204158" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Generalized Level Set Formulation of the Mumford-Shah Functional with Shape Prior for Medical Image Segmentation [chapter]

Lishui Cheng, Xian Fan, Jie Yang, Yun Zhu
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The shape prior is represented by the zero-level set of signed distance maps of images and is well consistent with level set based variational framework.  ...  The variational flow is implemented in level set framework and thus implicit and intrinsic.  ...  One can see that the new active contour model provides a general framework that unifies the region and boundary features (gradient and the shape prior) of an image for medical segmentation problems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11569541_8">doi:10.1007/11569541_8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rkg3cweh6rfp5ddb5dsk34egky">fatcat:rkg3cweh6rfp5ddb5dsk34egky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811071222/http://www.pami.sjtu.edu.cn/people/fanxian/Homepage/iccv_cvbia_A%20Generalized%20Level%20Set%20Formulation%20of%20%20the%20Mumford-Shah%20Functional%20with%20Shape%20Prior%20for%20Medical%20Image%20Segmentation.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f0/0f/f00f4cc0a200e3b9048badaa93790c2f44d53fc8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11569541_8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Shape-Based Approach to Robust Image Segmentation [chapter]

Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We propose a novel segmentation approach for introducing shape priors in the geometric active contour framework.  ...  Following the work of Leventon, we propose to revisit the use of linear principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner.  ...  In this paper, we propose to revisit the use of linear PCA to introduce prior knowledge about shapes into the geometric active contour framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11867586_17">doi:10.1007/11867586_17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5f7fqwwl3fasfheq2y3u7saqsm">fatcat:5f7fqwwl3fasfheq2y3u7saqsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809072324/http://pnl.bwh.harvard.edu/pub/pdfs/Dambreville-ICIAR2006.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/1e/ca1ea2eb11ac30a1702b5958a10fdab72120660d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11867586_17"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Curve Propagation, Level Set Methods and Grouping [chapter]

N. Paragios
<span title="">2015</span> <i title="Springer US"> Handbook of Biomedical Imaging </i> &nbsp;
In this chapter we present a comprehensive tutorial of level sets towards a flexible frame partition paradigm that could integrate edge-drive, regional-based and prior knowledge to object extraction.  ...  To this end, an objective function that aims to account for the expected visual properties of the object, impose certain smoothness constraints and encode prior knowledge on the geometric form of the object  ...  Once such a model was recovered, it was used [6] within the geodesic active contour model [4] to impose prior knowledge in the level set space: E(φ, A) = Ω δ α (φ) g(|∇I|)|∇φ| + λφ 2 M (A(ω)) dω (1.20  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-0-387-09749-7_3">doi:10.1007/978-0-387-09749-7_3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7xcytn4ajrd27ajg4ch6gslzsm">fatcat:7xcytn4ajrd27ajg4ch6gslzsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20061126152532/http://www.mas.ecp.fr/Personnel/nikos/pub/mmcv05.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/e1/2de17bdfae2a61899776fa3425448559ca2835d2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-0-387-09749-7_3"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Level set segmentation with both shape and intensity priors

Siqi Chen, Richard J Radke
<span title="">2009</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/753trptklbb4nj6jquqadzwwdu" style="color: black;">2009 IEEE 12th International Conference on Computer Vision</a> </i> &nbsp;
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set.  ...  The proposed variational level set segmentation framework has two main advantages.  ...  Such geometric shape priors are still widely used in general image segmentation when further shape prior information is not available.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccv.2009.5459290">doi:10.1109/iccv.2009.5459290</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iccv/ChenR09.html">dblp:conf/iccv/ChenR09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jle65jg2x5ffva3cm7myxpztv4">fatcat:jle65jg2x5ffva3cm7myxpztv4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809090047/http://www.ecse.rpiscrews.us/~rjradke/papers/chen-radke-iccv09.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f5/16/f5168348ae73cb13af7003cde95948b150fe0ae8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccv.2009.5459290"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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