19,022 research outputs found
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors
This paper presents a sensor-control method for choosing the best next state
of the sensor(s), that provide(s) accurate estimation results in a multi-target
tracking application. The proposed solution is formulated for a multi-Bernoulli
filter and works via minimization of a new estimation error-based cost
function. Simulation results demonstrate that the proposed method can
outperform the state-of-the-art methods in terms of computation time and
robustness to clutter while delivering similar accuracy
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
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