IPOLIPOLhttp://www.ipol.im/feed/IPOL Preprints — Latest public preprints from IPOL.ikiwiki2018-12-04T12:44:52ZAn Analysis and Speedup of the FALDOI Method for Optical Flow Estimationhttp://www.ipol.im/pub/pre/238/Ferran P. Gamonal,
Coloma Ballester,
Gloria Haro,
Enric Meinhardt-Llopis,
Roberto P. Palomares2018-12-04T12:44:52Z2018-11-27T13:06:53Z
We present a detailed analysis of FALDOI, a large displacement optical flow
method proposed by P.Palomares et al. This method requires a set of discrete
matches, which can be extremely sparse, and an energy functional which
locally guides the interpolation from the matches. It follows a two-step
minimization method at the finest scale which is very robust to the outliers of the
sparse matcher and can capture large displacements of small objects. The results
shown in the original paper consistently outperformed the coarse-to-fine
approaches and achieved good qualitative and quantitative performance on
the standard optical flow benchmarks. In this paper we revise the proposed
method and the changes done to significantly reduce its execution time while
reporting nearly the same accuracy. Finally, we also compare it against the
current state-of-the-art to assess its performance.
TriplClust: An Algorithm for Curve Detection in 3D Point Cloudshttp://www.ipol.im/pub/pre/234/Christoph Dalitz,
Jens Wilberg,
Lukas Aymans2018-11-23T18:31:09Z2018-11-23T18:31:09Z
In this article, we describe an algorithm for detecting and separating curves in 3D point clouds without making a priori assumptions about their parametric shape. The algorithm is called 'TriplClust' because it is based on the idea of clustering point triplets instead of the original points. We define a distance measure on point triplets and then apply a single-link hierarchical clustering on the triplets. The clustering process can be controlled by several parameters, which are described in detail, and suggestions for reasonable choices for these parameters based on the input data are made. Moreover, we suggest a simple criterion for stopping the single link clustering automatically.
An Analysis and Implementation of the FFDNet Image Denoising Methodhttp://www.ipol.im/pub/pre/231/Matias Tassano,
Julie Delon,
Thomas Veit2018-09-13T23:34:24Z2018-08-17T15:40:09Z
FFDNet is a recent image denoising method based on a convolutional neural network
architecture. In contrast to other existing neural network denoisers, FFDNet exhibits several
desirable properties such as faster execution time and smaller memory footprint, and the ability
to handle a wide range of noise levels effectively with a single network model. The combination
between its denoising performance and lower computational load makes this algorithm attractive
for practical denoising applications. In this paper we propose an open-source implementation
of the method based on PyTorch, a popular machine learning library for Python. Code for the
training of the network is also provided. We also discuss the characteristics of the architecture
of this algorithm and we compare it to other similar methods.
Implementation of a Denoising Algorithm based on High-Order Singular Value Decomposition of Tensorshttp://www.ipol.im/pub/pre/226/Fabien Feschet2018-09-13T23:34:24Z2018-05-30T09:57:10Z
This article presents an implementation of a denoising algorithm based on High-Order Singular
Value Decomposition (HOSVD) of tensors. It belongs to the class of patch-based methods such
as BM3D and NL-Bayes. It exploits the grouping of similar patches in a local neighbourhood
into a 3D matrix also called a third order tensor. Instead of performing different processing
in different dimension, as in BM3D for instance, it is based on the decomposition of a tensor
simultaneously in all dimensions reducing it to a core tensor in a similar way as SVD does for
matrices in computing the diagonal matrix of singular values. The core tensor is filtered and
a tensor is reconstructed by inverting the HOSVD. As common in patch-based algorithms, all
tensors containing a pixel are then merged to produce an output image.
Improvements of the Inverse Compositional Algorithm for Parametric Motion Estimationhttp://www.ipol.im/pub/pre/222/Thibaud Briand,
Gabriele Facciolo,
Javier Sánchez2018-09-13T23:34:24Z2018-04-19T08:34:16Z
In this work, we propose several improvements of the inverse compositional algorithm for parametric registration. We propose an improved handling of boundary pixels, a different color handling and gradient estimation, and the possibility to skip scales in the multiscale coarse-to-fine scheme. In an experimental part, we analyze the influence of the modifications. The estimation accuracy is at least improved by a factor 1.3 while the computation time is at least reduced by a factor 2.2 for color images.
Comparison of Optical Flow Methods under Stereomatching with Short Baselineshttp://www.ipol.im/pub/pre/217/Tristan Dagobert,
Nelson Monzón,
Javier Sánchez2018-07-22T11:40:36Z2017-10-16T12:24:59Z
This article studies the effectiveness of optical flow methods employed in the case of short baselines and different noise levels. New metrics have been developed to analyze the evaluation results because the usual metrics are inadequate in a subpixel context. Experiments conducted on the adequate Middlebury and CMLA dataset pairs show that the Brox et al. method produces the best errors, with a 60% success rate in relative precision at 1/100 th of a pixel. On the other hand, our comparison shows that the Monzón et al. method also provides competitive results at the same time that it yields disparities with more details and correct contours.
An Affine Invariant Patch Similarityhttp://www.ipol.im/pub/pre/202/Vadim Fedorov,
Coloma Ballester2018-07-22T11:40:36Z2017-07-14T10:09:29Z
Image and video comparison is often approached by comparing patches of visual information. In this work we present a detailed description and implementation of an affine invariant patch similarity measure that performs an appropriate patch comparison by automatically and intrinsically adapting the size and shape of the patches. We also describe the complete implementation of the proposed iterative algorithm for computation of those shape-adaptive patches around each point in the image domain.
Joint Large-Scale Motion Estimation and Image Reconstructionhttp://www.ipol.im/pub/pre/193/Hendrik Dirks2018-07-22T11:40:36Z2016-11-24T13:13:55Z
This article describes the implementation of the joint motion estimation and image reconstruction framework presented by Burger, Dirks and Schönlieb and extends this framework to large-scale motion between consecutive image frames. The variational framework uses displacements between consecutive frames based on the optical
flow approach to improve the image reconstruction quality on the one hand and the motion estimation quality on the other. The energy functional consists of a
data-fidelity term with a general operator that connects the input sequence to the solution, it has a total variation term for the image sequence and is connected to the underlying flow using an optical flow term. Additional spatial regularity for the flow is modeled by a total variation regularizer for both components of the flow. The numerical minimization is performed in an alternating manner using
primal-dual techniques. The resulting schemes are presented as pseudo-code together with a short numerical evaluation.
Joint TV-L1 Optical Flow and Occlusion Estimationhttp://www.ipol.im/pub/pre/118/Juan Francisco Garamendi Bragado,
Coloma Ballester,
Lluís Garrido,
Vanel Lazcano,
Vicent Caselles2018-07-22T11:40:36Z2015-02-05T12:37:40Z
This document describes an implementation of the energy functional minimization proposed by Ballester, Garrido, Lazcano and Caselles for joint optical flow and occlusion estimation. The method is based on the TV-L1 approach introduced Zach, Pock and Bischof in 2007 but with the particularity of detecting occlusions. The energy functional is composed by a regularization term (over the optical flow and the occlusion fields) using the total variation, a data term using the L1 norm, and a term, which is based on the divergence of the flow, for dealing with the occlusions.