IPOLIPOLhttp://www.ipol.im/feed/IPOL Preprints — Latest public preprints from IPOL.ikiwiki2019-05-09T10:30:32ZA Parallel, O (n) Algorithm for an Unbiased, Thin Watershedhttp://www.ipol.im/pub/pre/215/Théodore Chabardès,
Petr Dokládal,
Matthieu Faessel,
Michel Bilodeau2019-05-09T10:30:32Z2019-05-09T10:30:32Z
The watershed transform is a powerful tool for morphological segmentation. Most common
implementations of this method involve a strict hierarchy on gray tones in processing the pixels
composing an image. This hierarchical dependency complexifies the efficient use of modern
computational architectures. This paper introduces a new way of computing the watershed
transform that alleviates the sequential nature of hierarchical queue propagation. It is shown
that this method can directly relate to the hierarchical flooding. Simultaneous and disorderly
growth can now be used to maximize performances on modern architectures. Higher speed is
reached, bigger data volume can be processed. Experimental results show increased performances
regarding execution speed and memory consumption.
An Implementation of the Mean Shift Algorithmhttp://www.ipol.im/pub/pre/255/Damir Demirović2019-05-09T10:23:49Z2019-05-09T10:23:49Z
In this paper we present an implementation and analysis of Mean shift algorithm in C++
language. Mean shift is a general non-parametric mode finding/clustering procedure widely
used in image processing and analysis and computer vision techniques such as image denosing,
image segmentation, motion tracking etc.
Watervoxelshttp://www.ipol.im/pub/pre/250/Pierre Cettour-Janet,
Clément Cazorla,
Vaia Machairas,
Quentin Delannoy,
Nathalie Bednarek,
François Rousseau,
Etienne Décencière2019-05-09T10:14:40Z2019-05-09T10:14:40Z
In this article, we present the n-dimensional version of the waterpixels, namely the
watervoxels. Waterpixels constitute a simple, yet efficient alternative to standard superpixel paradigms,
initially developed in the field of computer vision for reducing the space cost of input images
without altering the accuracy of further image processing / analysis procedures. Waterpixels
were initially proposed in a 2-dimensional version. Their extension to 3-dimensions—and more
generally n-dimensions—is however possible, in particular in the Cartesian grid. Indeed, water-
pixels mainly rely on a seeded watershed transformation applied on a saliency map defined as
the linear combination of a gradient map and a distance map. We propose a description of the
algorithmics of watervoxels in n-dimensional Cartesian grids. We also discuss its parameters
and its time cost. A source code for 2- and 3-dimensional versions of watervoxels is provided,
such as a 2-dimensional demonstrator. This article can be seen as the companion of the article
“Waterpixels”, published in 2015 in IEEE Transactions on Image Processing.
Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithmhttp://www.ipol.im/pub/pre/251/Ioannis Bargiotas,
Julien Audiffren,
Nicolas Vayatis,
Pierre-Paul Vidal,
Alain P. Yelnik,
Damien Ricard2019-05-01T07:51:12Z2019-04-22T17:10:49Z
In this work we discuss the multidimensional scoring approach proposed by Bargiotas and al.
[I. Bargiotas, J. Audiffren, N. Vayatis, P-P. Vidal, S. Buffat, A.P. Yelnik and Damien Ricard,
On the importance of local dynamics in statokinesigram: a multivariate approach for postural control evaluation in elderly,
PloS one, 13 (2018)]
which locally characterizes statokinesigrams - the trajectory of the center of pressure, which is highly correlated with static balance quality - on small time intervals, or blocks. This approach highlights the local dynamics of the trajectories, and we show that the resulting characterization can be used to provide a global score in order to evaluate the postural control. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly, and show that it provides an efficient tool to discriminate between fallers and non-fallers.
An Analysis and Implementation of Natural Image Enhancement Methodhttp://www.ipol.im/pub/pre/252/Dang Thanh Trung,
Shaohua Chenr,
Azeddine Beghdadi,
Nguyen thi Quynh Hoa2019-03-16T21:56:11Z2019-03-16T21:56:11Z
Digital image enhancement is to improve the appearance of images to human viewers. This is one of extremely difficult issues in image processing. The NECI algorithm (Natural Enhancement of Color Image) is a robust and effective approach for color image enhancement. The core of this algorithm is Retinex model, a simulation model of human color vision. A fully framework for enhancement process is composed of four main steps: global tone mapping, Retinex-based local contrast enhancement, histogram rescaling, and texture enhancement.
The experimental results on different types of natural images show that the proposed algorithm not only preserves the ambience of image but also avoid side effects such as light condition changes, color temperature alteration, or additional artifacts, etc which lead to unnaturally sharpened images or dramatic white balance changes. In the output color images, no additional light sources are added to the scene, or no halo effect and blocking effect are amplified due to over-enhancement. Moreover, all parameters are image-dependent so that the process requires no parameter tuning.
Implementation of a Denoising Algorithm based on High-Order Singular Value Decomposition of Tensorshttp://www.ipol.im/pub/pre/226/Fabien Feschet2019-04-11T10:46:49Z2018-05-30T09:57:10Z
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.
Comparison of Optical Flow Methods under Stereomatching with Short Baselineshttp://www.ipol.im/pub/pre/217/Tristan Dagobert,
Nelson Monzón,
Javier Sánchez2019-01-21T12:49:29Z2017-10-16T12:24:59Z
This article studies the effectiveness of optical flow methods applied to short
baseline image pairs under different noise levels. New metrics have been
developed to analyze the results because the usual metrics are inadequate in a
subpixel context. We have used the implementation of some standard optical flow
methods adapted to the stereo problem. Our experiments show that the Brox
et al. method produces the least errors, with a 60% success rate and a
relative precision at 1/100th of a pixel. On the other hand, our comparison
shows that a discontinuity preserving method, derived from Brox et al.,
also provides competitive results at the same time that it yields disparities
with more details and correct contours.
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.