2,844 research outputs found
Optimum graph cuts for pruning binary partition trees of polarimetric SAR images
This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.Peer ReviewedPostprint (author's final draft
Processing of microCT implant-bone systems images using Fuzzy Mathematical Morphology
The relationship between a metallic implant and the existing bone in a surgical permanent prosthesis is of great importance since the fixation and osseointegration of the system leads to the failure or success of the surgery. Micro Computed Tomography is atechnique that helps to visualize the structure of the bone. In this study, the microCT is used to analyze implant-bone systems images. However, one of the problems presented in the reconstruction of these images is the effect of the iron based implants, with a halo or fluorescence scattering distorting the micro CT image and leading to bad 3D reconstructions.In this work we introduce an automatic method for eliminate the effect of AISI 316L iron materials in the implant-b one system based on the application of Compensatory Fuzzy Mathematical Morphology for future investigate about the structural and mechanical properties of bone and cancellous materials.Fil: Bouchet, Agustina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional de Mar del Plata; ArgentinaFil: Colabella, Lucas. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Omar, Sheila AyelĂ©n. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Ballarre, Josefina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Pastore, Juan Ignacio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional de Mar del Plata; Argentin
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
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