On Improving the Efficiency of Tensor Voting

Moreno, Rodrigo; García, Miguel Ángel; Puig, Domènec; Pizarro, Luis; Burgeth, Bernhard; Weickert, Joachim

doi:10.1109/tpami.2011.23

Cited by 24 publications

(44 citation statements)

References 18 publications

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“…Different modifications have been proposed to tackle the time complexity of TV. Generally, most of these propositions try to find a closed solution for the problem [58], which was proven incorrect in [59], or optimizing the calculations of the plate and ball voting fields as the most time consuming parts of TV [56]. Therefore, applying TV to road gap filling requires further purpose-dependent customization.…”

Section: Tensor Votingmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

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Section: Tensor Votingmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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“…A common approach is to define each voxel in the image as a token, and to assign a ball-shaped tensor, i.e no preferred initial orientation, to each of the tokens [14]. In our case, we make use of a priori information obtained from the analysis of the Hessian matrix to 1) reduce the number of tokens to be processed and 2) initialise each token with a first estimation of its orientation.…”

Section: Methodsmentioning

confidence: 99%

“…Votes are combined at every token to infer the type of structure going through it. More formally, the tensor voting at p is given by [14]:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

SEEG Trajectory Planning: Combining Stability, Structure and Scale in Vessel Extraction

Zuluaga

Rodionov

Nowell

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity [2]. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88±0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC=0.78±0.02).

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“…This is highly relevant considering that over fifteen years passed from the original publication of the technique, and there had been no success at finding a closedform solution to the problem. A number of reformulations have also been recently presented [2], [4], [5], though none of them features the simplicity of CFTV nor provides an analytic general solution.…”

Section: Introductionmentioning

confidence: 99%

Comments on “A Closed-Form Solution to Tensor Voting: Theory and Applications”

Maggiori

Lotito

Manterola

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We comment on a paper that describes a closed-form formulation to Tensor Voting, a technique to perceptually group clouds of points, usually applied to infer features in images. The authors proved an analytic solution to the technique, a highly relevant contribution considering that the original formulation required numerical integration, a time-consuming task. Their work constitutes the first closed-form expression for the Tensor Voting framework. In this work we first observe that the proposed formulation leads to unexpected results which do not satisfy the constraints for a Tensor Voting output, hence they cannot be interpreted. Given that the closed-form expression is said to be an analytic equivalent solution, unexpected outputs should not be encountered unless there are flaws in the proof. We analyzed the underlying math to find which were the causes of these unexpected results. In this commentary we show that their proposal does not in fact provide a proper analytic solution to Tensor Voting and we indicate the flaws in the proof.

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On Improving the Efficiency of Tensor Voting

Cited by 24 publications

References 18 publications

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

SEEG Trajectory Planning: Combining Stability, Structure and Scale in Vessel Extraction

Comments on “A Closed-Form Solution to Tensor Voting: Theory and Applications”

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