Estimation of linear deformations of 2D and 3D fuzzy objects

Tanács, Attila; Lindblad, Joakim; Sladoje, Nataša; Kató, Zoltán

doi:10.1016/j.patcog.2014.10.006

Cited by 10 publications

(9 citation statements)

References 33 publications

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“…In the following, we will build our methods upon the 2D registration frameworks introduced in [7,122]. Let us focus on the pair-wise registration of 2D image regions by estimating the transformation between a template and an observation patch.…”

Section: Registration Frameworkmentioning

confidence: 99%

“…Following [7,122], instead of extracting individual point-correspondences, let us integrate both sides of Equation (4.1):…”

Section: Registration Frameworkmentioning

confidence: 99%

“…In order to avoid generation of the transformed domain, we can apply the integral transformation to Equation (4.2), similarly to the 3D framework described in the previous chapter: Although, the equations of Equation (42) define two constraints on the parameters of ϕ, generally, the number of parameters is much higher than two. One way to overcome this problem is to follow [7,122] and apply a set of independent non-linear functions…”

Section: Chapter Robust Registration Of 2d Imagesmentioning

confidence: 99%

“…, ℓ. Using polynomial ω functions will eventually lead to a polynomial system of equations and according to [7,122], the framework is quite robust against several types of geometric noise and segmentation error [7,122].…”

Section: Chapter Robust Registration Of 2d Imagesmentioning

confidence: 99%

“…, ℓ. Similarly to the affine framework proposed in [122], we use simple power functions for the {ω i } ℓ i=1 set and choosing ℓ ≥ 6 yields an overdetermined system, which is then solved in the least-squares sense [Sánta and Kato, 2014].…”

Section: Affine Alignment Of Occluded Shapesmentioning

confidence: 99%

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Non-Rigid Registration of Visual Objects

Sánta

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Sample results on the Bosphorus dataset. In each row, we show the template, the observation, the warped template and the landmark locations, respectively. In the last column, green denotes the ground truth position and red is the estimated location. While the proposed approach achieved good results near nose and mouth areas, the highest errors are near the eyebrows.. .. .. .. . 53 LIST OF FIGURES v 3.20 Comparison between the proposed, the GMMREG [5] and the CPD [6] algorithms. On the left diagram, the D GT measure is presented, while on the right we show the running times of each algorithm. The GMMREG slightly outperformed the proposed approach and the CPD algorithm in terms of alignment accuracy, but the proposed approach has the best running time.. .. .. .. .

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Section: Registration Frameworkmentioning

confidence: 99%

“…Following [7,122], instead of extracting individual point-correspondences, let us integrate both sides of Equation (4.1):…”

Section: Registration Frameworkmentioning

confidence: 99%

Section: Chapter Robust Registration Of 2d Imagesmentioning

confidence: 99%

Section: Chapter Robust Registration Of 2d Imagesmentioning

confidence: 99%

Section: Affine Alignment Of Occluded Shapesmentioning

confidence: 99%

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Non-Rigid Registration of Visual Objects

Sánta

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Elastic Alignment of Triangular Surface Meshes

Sánta

Kató

2018

Int J Comput Vis

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Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

2020

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The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement.The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1-7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R 2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands.

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Estimation of linear deformations of 2D and 3D fuzzy objects

Cited by 10 publications

References 33 publications

Non-Rigid Registration of Visual Objects

Non-Rigid Registration of Visual Objects

Elastic Alignment of Triangular Surface Meshes

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

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