3,205 research outputs found
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
We present a global optimization approach to optical flow estimation. The
approach optimizes a classical optical flow objective over the full space of
mappings between discrete grids. No descriptor matching is used. The highly
regular structure of the space of mappings enables optimizations that reduce
the computational complexity of the algorithm's inner loop from quadratic to
linear and support efficient matching of tens of thousands of nodes to tens of
thousands of displacements. We show that one-shot global optimization of a
classical Horn-Schunck-type objective over regular grids at a single resolution
is sufficient to initialize continuous interpolation and achieve
state-of-the-art performance on challenging modern benchmarks.Comment: To be presented at CVPR 201
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
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Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation
Video segmentation is a stepping stone to understanding video context. Video
segmentation enables one to represent a video by decomposing it into coherent
regions which comprise whole or parts of objects. However, the challenge
originates from the fact that most of the video segmentation algorithms are
based on unsupervised learning due to expensive cost of pixelwise video
annotation and intra-class variability within similar unconstrained video
classes. We propose a Markov Random Field model for unconstrained video
segmentation that relies on tight integration of multiple cues: vertices are
defined from contour based superpixels, unary potentials from temporal smooth
label likelihood and pairwise potentials from global structure of a video.
Multi-cue structure is a breakthrough to extracting coherent object regions for
unconstrained videos in absence of supervision. Our experiments on VSB100
dataset show that the proposed model significantly outperforms competing
state-of-the-art algorithms. Qualitative analysis illustrates that video
segmentation result of the proposed model is consistent with human perception
of objects
A discriminative view of MRF pre-processing algorithms
While Markov Random Fields (MRFs) are widely used in computer vision, they
present a quite challenging inference problem. MRF inference can be accelerated
by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based
approaches which compute the optimal labeling of a subset of variables. These
techniques are guaranteed to never wrongly label a variable but they often
leave a large number of variables unlabeled. We address this shortcoming by
interpreting pre-processing as a classification problem, which allows us to
trade off false positives (i.e., giving a variable an incorrect label) versus
false negatives (i.e., failing to label a variable). We describe an efficient
discriminative rule that finds optimal solutions for a subset of variables. Our
technique provides both per-instance and worst-case guarantees concerning the
quality of the solution. Empirical studies were conducted over several
benchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moves
without preprocessing, and on difficult non-submodular energy functions produce
slightly lower energy.Comment: ICCV 201
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
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
We propose a new multi-frame method for efficiently computing scene flow
(dense depth and optical flow) and camera ego-motion for a dynamic scene
observed from a moving stereo camera rig. Our technique also segments out
moving objects from the rigid scene. In our method, we first estimate the
disparity map and the 6-DOF camera motion using stereo matching and visual
odometry. We then identify regions inconsistent with the estimated camera
motion and compute per-pixel optical flow only at these regions. This flow
proposal is fused with the camera motion-based flow proposal using fusion moves
to obtain the final optical flow and motion segmentation. This unified
framework benefits all four tasks - stereo, optical flow, visual odometry and
motion segmentation leading to overall higher accuracy and efficiency. Our
method is currently ranked third on the KITTI 2015 scene flow benchmark.
Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3
orders of magnitude faster than the top six methods. We also report a thorough
evaluation on challenging Sintel sequences with fast camera and object motion,
where our method consistently outperforms OSF [Menze and Geiger, 2015], which
is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo
Scene Flow Benchmark in November 201
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