99,167 research outputs found
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
Optimization-based interactive segmentation interface for multiregion problems.
Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality
Learning an Interactive Segmentation System
Many successful applications of computer vision to image or video
manipulation are interactive by nature. However, parameters of such systems are
often trained neglecting the user. Traditionally, interactive systems have been
treated in the same manner as their fully automatic counterparts. Their
performance is evaluated by computing the accuracy of their solutions under
some fixed set of user interactions. This paper proposes a new evaluation and
learning method which brings the user in the loop. It is based on the use of an
active robot user - a simulated model of a human user. We show how this
approach can be used to evaluate and learn parameters of state-of-the-art
interactive segmentation systems. We also show how simulated user models can be
integrated into the popular max-margin method for parameter learning and
propose an algorithm to solve the resulting optimisation problem.Comment: 11 pages, 7 figures, 4 table
- …