Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Serradell, Eduard; Romero, Adriana; Leta, Rubén; Gatta, Carlo; Moreno-Noguer, Francesc

doi:10.1109/iccv.2011.6126325

Cited by 29 publications

(18 citation statements)

References 26 publications

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“…The final category involves methods that simultaneously search for correspondences and estimate the transformation parameters using a Kalman filter-like approach [6], [10], [21], [28], [32], [33]. As soon as a few initial correspondences have been established, the set of remaining potential matches is rapidly reduced, making the search complexity manageable.…”

Section: Related Workmentioning

confidence: 99%

Non-Rigid Graph Registration Using Active Testing Search

Serradell

Pinheiro

Sznitman

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We present a new approach for matching sets of branching curvilinear structures that form graphs embedded in R 2 or R 3 and may be subject to deformations. Unlike earlier methods, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformations, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian Processes to represent 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 considered at each iteration is not selected at random as in many RANSAC-based algorithms. Instead, we introduce a so-called Active Testing Search strategy that performs a priority search to favor the most likely matches and speed-up the process. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.

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Section: Related Workmentioning

confidence: 99%

Non-Rigid Graph Registration Using Active Testing Search

Serradell

Pinheiro

Sznitman

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Along these lines we will take advantage of recent descriptors such as [24] or the DaLI [11]. The latter, is specially interesting because it will let us bring our approach from a rigid to a deformable domain, like in [18,20], but without the strong assumption of having to know the camera calibration parameters in advance.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous Pose, Focal Length and 2D-to-3D Correspondences from Noisy Observations

Peñate-Sánchez

Serradell

Andrade‐Cetto

et al. 2013

Procedings of the British Machine Vision Conference 2013

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Simultaneously recovering the camera pose and correspondences between a set of 2D-image and 3D-model points is a difficult problem, especially when the 2D-3D matches cannot be established based on appearance only. The problem becomes even more challenging when input images are acquired with an uncalibrated camera with varying zoom, which yields strong ambiguities between translation and focal length. We present a solution to this problem using only geometrical information. Our approach owes its robustness to an initial stage in which the joint pose and focal length solution space is split into several Gaussian regions. At runtime, each of these regions is explored using an hypothesize-and-test approach, in which the potential number of 2D-3D matches is progressively reduced using informed search through Kalman updates, iteratively refining the pose and focal length parameters. The technique is exhaustive but efficient, significantly improving previous methods in terms of robustness to outliers and noise.

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“…The proposed algorithm, however, is in principle "matcher-agnostic" and can be employed with other template matching approaches. In particular, part of our future work consists in exploiting this methodology to build priors for non-rigid template matching algorithms and reduce the inherent ambiguities of problems like those presented in [27,28].…”

Section: Resultsmentioning

confidence: 99%

Matchability Prediction for Full-Search Template Matching Algorithms

Peñate-Sánchez

Porzi

Moreno-Noguer

2015

2015 International Conference on 3D Vision

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While recent approaches have shown that it is possible to do template matching by exhaustively scanning the parameter space, the resulting algorithms are still quite demanding. In this paper we alleviate the computational load of these algorithms by proposing an efficient approach for predicting the matchability of a template, before it is actually performed. This avoids large amounts of unnecessary computations. We learn the matchability of templates by using dense convolutional neural network descriptors that do not require ad-hoc criteria to characterize a template. By using deep learning descriptions of patches we are able to predict matchability over the whole image quite reliably. We will also show how no specific training data is required to solve problems like panorama stitching in which you usually require data from the scene in question. Due to the highly parallelizable nature of this tasks we offer an efficient technique with a negligible computational cost at test time.

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Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Cited by 29 publications

References 26 publications

Non-Rigid Graph Registration Using Active Testing Search

Non-Rigid Graph Registration Using Active Testing Search

Simultaneous Pose, Focal Length and 2D-to-3D Correspondences from Noisy Observations

Matchability Prediction for Full-Search Template Matching Algorithms

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