An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds

Loss, Leandro A.; Bebis, George; Nicolescu, Mircea; Skurikhin, Alexei N.

doi:10.1016/j.cviu.2008.07.011

Cited by 31 publications

(26 citation statements)

References 26 publications

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“…Although our assumption is rather strong, recent techniques in blob and texel detection enable us to extract blobs in images efficiently [12]- [14]. Also, research in perceptual grouping revealed clues to find the connection of blobs in images [15], [16]. Although these technologies have not been established yet, the blob detection and grouping may be available in near future.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Sequential Patterns without Knowing Image Correspondences

Miyan

Sato

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a method for reconstructing 3D sequential patterns from multiple images without knowing exact image correspondences and without calibrating linear camera sensitivity parameters on intensity. The sequential pattern is defined as a series of colored 3D points. We assume that the series of the points are obtained in multiple images, but the correspondence of individual points is not known among multiple images. For reconstructing sequential patterns, we consider a camera projection model which combines geometric and photometric information of objects. Furthermore, we consider camera projections in the frequency space. By considering the multi-view relationship on the new projection model, we show that the 3D sequential patterns can be reconstructed without knowing exact correspondence of individual image points in the sequential patterns; moreover, the recovered 3D patterns do not suffer from changes in linear camera sensitivity parameters. The efficiency of the proposed method is tested using real images.

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Section: Introductionmentioning

confidence: 99%

Reconstructing Sequential Patterns without Knowing Image Correspondences

Miyan

Sato

2015

IEICE Trans. Inf. & Syst.

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“…Therefore, every token is a location where an orientation is defined. The representation is in the form of a second order, symmetric, non-negative definite tensor which essentially indicates the saliency of each type of perceptual structure (curve, junction or region in 2-D) the token may belong to its preferred normal and tangent orientations [71,72]: T = λ 1 − λ 2 e 1 e 1 T + λ 2 (e 1 e 1 T + e 2 e 2 T )…”

Section: Tensor Voting Frameworkmentioning

confidence: 99%

“…The magnitude of the stick component λ 1 − λ 2 indicates curve saliency. Tensor voting was implemented using tensor fields to pre-compute and store the votes from both stick and ball voters in receivers at various distances and angles ( Figure 5) [70][71][72]. Vote orientation corresponds to the smoothest local curve continuation from voter to the recipient, while vote strength decays exponentially with distance and curvature.…”

Section: Tensor Voting Frameworkmentioning

confidence: 99%

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

Rahnama

Gloaguen

2014

Remote Sensing

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Abstract:Geological structures, such as faults and fractures, appear as image discontinuities or lineaments in remote sensing data. Geologic lineament mapping is a very important issue in geo-engineering, especially for construction site selection, seismic, and risk assessment, mineral exploration and hydrogeological research. Classical methods of lineaments extraction are based on semi-automated (or visual) interpretation of optical data and digital elevation models. We developed a freely available Matlab based toolbox TecLines (Tectonic Lineament Analysis) for locating and quantifying lineament patterns using satellite data and digital elevation models. TecLines consists of a set of functions including frequency filtering, spatial filtering, tensor voting, Hough transformation, and polynomial fitting. Due to differences in the mathematical background of the edge detection and edge linking procedure as well as the breadth of the methods, we introduce the approach in two-parts. In this first study, we present the steps that lead to edge detection. We introduce the data pre-processing using selected filters in spatial and frequency domains. We then describe the application of the tensor-voting framework to improve position and length accuracies of the detected lineaments. We demonstrate the robustness of the approach in a complex area in the northeast of Afghanistan using a panchromatic QUICKBIRD-2 image with 1-meter resolution. Finally, we compare the results of TecLines with manual lineament extraction, and other lineament extraction algorithms, as well as a published fault map of the study area.

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“…Second, σ directly depends on the spatial length, since it is a scale parameter. Finally, b has been chosen in the literature either as a dimensionless constant (e.g., [3], [16]) or as a variable that (mainly) depends on the spatial length (e.g., [17], [4], [8]). It is easy to check that (9) is not scale-invariant under these conditions.…”

Section: A Scale-invariant Stick 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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An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds

Cited by 31 publications

References 26 publications

Reconstructing Sequential Patterns without Knowing Image Correspondences

Reconstructing Sequential Patterns without Knowing Image Correspondences

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

On Improving the Efficiency of Tensor Voting

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