572 research outputs found
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Segmentation of Dynamic PET Images with Kinetic Spectral Clustering
International audienceSegmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks
Event-based contact angle measurements inside porous media using time-resolved micro-computed tomography
Hypothesis: Capillary-dominated multiphase flow in porous materials is strongly affected by the pore walls' wettability. Recent micro-computed tomography (mCT) studies found unexpectedly wide contact angle distributions measured on static fluid distributions inside the pores. We hypothesize that analysis on time-resolved mCT data of fluid invasion events may be more directly relevant to the fluid dynamics.
Experiment: We approximated receding contact angles locally in time and space on time-resolved mCT datasets of drainage in a glass bead pack and a limestone. Whenever a meniscus suddenly entered one or more pores, geometric and thermodynamically consistent contact angles in the surrounding pores were measured in the time step just prior to the displacement event. We introduced a new force-based contact angle, defined to recover the measured capillary pressure in the invaded pore throat prior to interface movement.
Findings: Unlike the classical method, the new geometric and force-based contact angles followed plausible, narrower distributions and were mutually consistent. We were unable to obtain credible results with the thermodynamically consistent method, likely because of sensitivity to common imaging artifacts and neglecting dissipation. Time-resolved mCT analysis can yield a more appropriate wettability characterization for pore scale models, despite the need to further reduce image analysis uncertainties. (C) 2020 The Authors. Published by Elsevier Inc
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.894244Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and backgroun
Piecewise Linear Patch Reconstruction for Segmentation and Description of Non-smooth Image Structures
In this paper, we propose a unified energy minimization model for the
segmentation of non-smooth image structures. The energy of piecewise linear
patch reconstruction is considered as an objective measure of the quality of
the segmentation of non-smooth structures. The segmentation is achieved by
minimizing the single energy without any separate process of feature
extraction. We also prove that the error of segmentation is bounded by the
proposed energy functional, meaning that minimizing the proposed energy leads
to reducing the error of segmentation. As a by-product, our method produces a
dictionary of optimized orthonormal descriptors for each segmented region. The
unique feature of our method is that it achieves the simultaneous segmentation
and description for non-smooth image structures under the same optimization
framework. The experiments validate our theoretical claims and show the clear
superior performance of our methods over other related methods for segmentation
of various image textures. We show that our model can be coupled with the
piecewise smooth model to handle both smooth and non-smooth structures, and we
demonstrate that the proposed model is capable of coping with multiple
different regions through the one-against-all strategy
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