36,113 research outputs found
Fast Registration of Cardiac Perfusion MRI
This abstract presents a novel method for registration of cardiac perfusion MRI sequences. By performing complex analyses of variance and clustering in an annotated training set off-line, our method provides real-time segmentation in an on-line setting. This renders the method feasible for live motion-compensation in MR scanners. Changes in image intensity during the bolus passage are modelled by an Active Appearance Model augmented with a cluster analysis of the training set. Preliminary validation carried out using five subjects showed acceptable segmentation accuracy produced very rapidly (below 40 ms per image)
The Whole World in Your Hand: Active and Interactive Segmentation
Object segmentation is a fundamental problem
in computer vision and a powerful resource for
development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided
by the presence of a hand or arm in the proximity of the object to be segmented. The first
approach is suitable for a robotic system, where
the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's
perspective, with instrumentation to help detect
and segment objects that are held in the wearer's
hand. The third method operates when observing
a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it
as a seed for segmentation. We show that object segmentation can serve as a key resource for
development by demonstrating methods that exploit high-quality object segmentations to develop
both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities
(object recognition and localization)
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.Comment: A preliminary version of the material in this document was filed as
University of Texas technical report no. UT AI16-0
Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge
This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the heart using a priori information. From a fast marching initialisation, two fronts representing the endocardium and epicardium boundaries of the left ventricle are evolved as the zero level-set of a higher dimension function. We introduce a novel and robust stopping term using both gradient and region-based information. The segmentation is supervised both with a coupling function and using a probabilistic model built from training instances. The robustness of the segmentation scheme is evaluated by performing a segmentation on four unseen data-sets containing high variation and the performance of the segmentation is quantitatively assessed
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
3D Tracking Using Multi-view Based Particle Filters
Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naĂŻve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios
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