7,275 research outputs found
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Hierarchies, such as the tree of shapes, are popular representations for
image simplification and segmentation thanks to their multiscale structures.
Selecting meaningful level lines (boundaries of shapes) yields to simplify
image while preserving intact salient structures. Many image simplification and
segmentation methods are driven by the optimization of an energy functional,
for instance the celebrated Mumford-Shah functional. In this paper, we propose
an efficient approach to hierarchical image simplification and segmentation
based on the minimization of the piecewise-constant Mumford-Shah functional.
This method conforms to the current trend that consists in producing
hierarchical results rather than a unique partition. Contrary to classical
approaches which compute optimal hierarchical segmentations from an input
hierarchy of segmentations, we rely on the tree of shapes, a unique and
well-defined representation equivalent to the image. Simply put, we compute for
each level line of the image an attribute function that characterizes its
persistence under the energy minimization. Then we stack the level lines from
meaningless ones to salient ones through a saliency map based on extinction
values defined on the tree-based shape space. Qualitative illustrations and
quantitative evaluation on Weizmann segmentation evaluation database
demonstrate the state-of-the-art performance of our method.Comment: Pattern Recognition Letters, Elsevier, 201
General Dynamic Scene Reconstruction from Multiple View Video
This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance
Markov mezĆk a kĂ©pmodellezĂ©sben, alkalmazĂĄsuk az automatikus kĂ©pszegmentĂĄlĂĄs terĂŒletĂ©n = Markovian Image Models: Applications in Unsupervised Image Segmentation
1) KifejlesztettĂŒnk egy olyan szĂn Ă©s textĂșra alapĂș szegmentĂĄlĂł MRF algoritmust, amely alkalmas egy kĂ©p automatikus szegmentĂĄlĂĄsĂĄt elvĂ©gezni. Az eredmĂ©nyeinket az Image and Vision Computing folyĂłiratban publikĂĄltuk. 2) KifejlesztettĂŒnk egy Reversible Jump Markov Chain Monte Carlo technikĂĄn alapulĂł automatikus kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, melyet sikeresen alkalmaztunk szĂnes kĂ©pek teljesen automatikus szegmentĂĄlĂĄsĂĄra. Az eredmĂ©nyeinket a BMVC 2004 konferenciĂĄn Ă©s az Image and Vision Computing folyĂłiratban publikĂĄltuk. 3) A modell többrĂ©tegƱ tovĂĄbbfejlesztĂ©sĂ©t alkalmaztuk video objektumok szĂn Ă©s mozgĂĄs alapĂș szegmentĂĄlĂĄsĂĄra, melynek eredmĂ©nyeit a HACIPPR 2005 illetve az ACCV 2006 nemzetközi konferenciĂĄkon publikĂĄltuk. SzintĂ©n ehhez az alapproblĂ©mĂĄhoz kapcsolĂłdik HorvĂĄth PĂ©ter hallgatĂłmmal az optic flow szamĂtĂĄsĂĄval illetve szĂn, textĂșra Ă©s mozgĂĄs alapĂș GVF aktĂv kontĂșrral kapcsoltos munkĂĄink. TDK dolgozata elsĆ helyezĂ©st Ă©rt el a 2004-es helyi versenyen, az eredmĂ©nyeinket pedig a KEPAF 2004 konferenciĂĄn publikĂĄltuk. 4) HorvĂĄth PĂ©ter PhD hallgatĂłmmal illetve az franciaorszĂĄgi INRIA Ariana csoportjĂĄval, kidolgoztunk egy olyan kĂ©pszegmentĂĄlĂł eljĂĄrĂĄst, amely a szegmentĂĄlandĂł objektum alakjĂĄt is figyelembe veszi. Az eredmĂ©nyeinket az ICPR 2006 illetve az ICCVGIP 2006 konferenciĂĄn foglaltuk össze. A modell elĆzmĂ©nyekĂ©nt kidolgoztunk tovĂĄbbĂĄ egy alakzat-momemntumokon alapulĂł aktĂv kontĂșr modellt, amelyet a HACIPPR 2005 konferenciĂĄn publikĂĄltunk. | 1) We have proposed a monogrid MRF model which is able to combine color and texture features in order to improve the quality of segmentation results. We have also solved the estimation of model parameters. This work has been published in the Image and Vision Computing journal. 2) We have proposed an RJMCMC sampling method which is able to identify multi-dimensional Gaussian mixtures. Using this technique, we have developed a fully automatic color image segmentation algorithm. Our results have been published at BMVC 2004 international conference and in the Image and Vision Computing journal. 3) A new multilayer MRF model has been proposed which is able to segment an image based on multiple cues (such as color, texture, or motion). This work has been published at HACIPPR 2005 and ACCV 2006 international conferences. The work on optic flow computation and color-, texture-, and motion-based GVF active contours doen with my student, Mr. Peter Horvath, won a first price at the local Student Research Competition in 2004. Results have been presented at KEPAF 2004 conference. 4) A new shape prior, called 'gas of circles' has been introduced using active contour models. This work is done in collaboration with the Ariana group of INRIA, France and my PhD student, Mr. Peter Horvath. Results are published at the ICPR 2006 and ICCVGIP 2006 conferences. A preliminary study on active contour models using shape-moments has also been done, these results are published at HACIPPR 2005
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and
cosegmentation with two key characteristics: first, we substitute ground truth
data with the semantic map output of a classifier; second, we combine this
output with population deformable registration to improve both alignment and
segmentation. Our approach deforms all volumes towards consensus, taking into
account image similarities and label consistency. Our pipeline can incorporate
any classifier and similarity metric. Results on two datasets, containing
annotations of challenging brain structures, demonstrate the potential of our
method.Comment: The first two authors contributed equall
ClassCut for Unsupervised Class Segmentation
Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].
Better Foreground Segmentation Through Graph Cuts
For many tracking and surveillance applications, background subtraction
provides an effective means of segmenting objects moving in front of a static
background. Researchers have traditionally used combinations of morphological
operations to remove the noise inherent in the background-subtracted result.
Such techniques can effectively isolate foreground objects, but tend to lose
fidelity around the borders of the segmentation, especially for noisy input.
This paper explores the use of a minimum graph cut algorithm to segment the
foreground, resulting in qualitatively and quantitiatively cleaner
segmentations. Experiments on both artificial and real data show that the
graph-based method reduces the error around segmented foreground objects. A
MATLAB code implementation is available at
http://www.cs.smith.edu/~nhowe/research/code/#fgsegComment: 8 pages, 110 figures. Revision: Added web link to downloadable Matlab
implementatio
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