Iterated tensor voting and curvature improvement

Fischer, Sylvain; Bayerl, Pierre; Neumann, Heiko; Redondo, Rafael; Cristóbal, Gabriel

doi:10.1016/j.sigpro.2007.03.019

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…In our case, the sources of magnetic anomalies are calculated from cross‐sections along four directions (Figure c) and therefore, directional information is used. Fischer, Bayerl, Neumann, Redondo, and Cristóbal () describe an iterative voting framework, where in each iteration step the stick saliency map is calculated and saliency values below a threshold τ σ are rejected. In the next voting step, the rejected tokens no longer cast votes, thus enhancing those belonging to structures.…”

Section: Enhancing Significant Archaeological Structuresmentioning

confidence: 99%

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

Jungmann

Dolenz

Clauser

et al. 2017

Archaeological Prospection

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In this paper, a processing chain is proposed for enhancing larger‐scale building structures in magnetometer data using the continuous wavelet transform for extracting the horizontal location, depth, and homogeneity (or shape) of subsurface objects. Even though the values estimated from our data do not clearly describe the archaeological site, they are used as a filter step for removing objects whose depth and homogeneity fall outside a predefined range. This yields a binary map, where valid points represent the horizontal source positions of magnetic anomalies. In a second step, curvilinear subsets of points are identified on a larger scale by the tensor voting framework indicating archaeological relevant structures, such as arcs and lines. Here, a point casts votes on neighbouring points and their values depend on the distance, and relative position of the points. All votes are summed up and the final value discriminates, whether a certain point (and therefore a magnetic source) is part of a salient curve (e.g. arc, line). Thresholding removes points with lower values while keeping those with larger values. Although the result shows many geometric features, it still needs to be combined with other data, e.g. from aerial photography or excavations. In combination, the extracted features continue those structures in the aerial photograph, thus expanding the knowledge about the archaeological site.

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Section: Enhancing Significant Archaeological Structuresmentioning

confidence: 99%

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

Jungmann

Dolenz

Clauser

et al. 2017

Archaeological Prospection

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“…This comes out of the observation that an input ball is used to encode a junction, and junctions are not close to each other. However, this can be useful when tensor voting is used iteratively [14][15][16] in order to induce uncertainty when tokens are not initialised with accurate values.…”

Section: Computing Ball and Plate Votesmentioning

confidence: 99%

Perceptual grouping by tensor voting: a comparative survey of recent approaches

Maggiori¹,

Manterola

Fresno³

2015

IET Computer Vision

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Tensor voting is a computational framework that addresses the problem of perceptual organisation. It was designed to convey human perception principles into a unified framework that can be adapted to extract visually salient elements from possibly noisy or corrupted images. The original formulation featured some concerns that made it difficult or impractical to be applied directly. Therefore, several partial or total theoretical reformulations or augmentations have been proposed. These almost parallel publication were presented in different directions, with different priorities and even in a different notation. Thus, the authors observed the need for a coherent description and comparison of the different proposals. This work, after describing the original approach of tensor voting, reviews and explains a number of high impact theoretical modifications in a self‐contained manner and including possible future directions of work. The authors have selected and organised a number of formulations and unified the way the problem is addressed across the different proposals. The aim of this study is to contribute with a modern comprehensive source of information on the theoretical aspects of tensor voting.

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“…Unlike the plate component, it is difficult to justify from the perceptual point of view the existence of the ball component of the ball vote since junctions are not usually close to each other. However, it could be useful in iterative schemes e.g., [24], [16], in order to induce uncertainty for those cases in which the tensors are initialized with not too accurate values.…”

Section: Ball Tensor Votingmentioning

confidence: 99%

On Improving the Efficiency of Tensor Voting

Moreno

García

Puig

et al. 2011

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: the stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue, since they have a complexity of order O 1 . Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.

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Iterated tensor voting and curvature improvement

Cited by 9 publications

References 28 publications

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

Perceptual grouping by tensor voting: a comparative survey of recent approaches

On Improving the Efficiency of Tensor Voting

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