477 research outputs found
Experimental Quantum Imaging exploiting multi-mode spatial correlation of twin beams
Properties of quantum states have disclosed new and revolutionary
technologies, ranging from quantum information to quantum imaging. This last
field is addressed to overcome limits of classical imaging by exploiting
specific properties of quantum states of light. One of the most interesting
proposed scheme exploits spatial quantum correlations between twin beams for
realizing sub-shot-noise imaging of the weak absorbing objects, leading ideally
to a noise-free imaging. Here we discuss in detail the experimental realization
of this scheme, showing its capability to reach a larger signal to noise ratio
with respect to classical imaging methods and, therefore, its interest for
future practical applications
Detection of multimode spatial correlation in PDC and application to the absolute calibration of a CCD camera
We propose and demonstrate experimentally a new method based on the spatial
entanglement for the absolute calibration of analog detector. The idea consists
on measuring the sub-shot-noise intensity correlation between two branches of
parametric down conversion, containing many pairwise correlated spatial modes.
We calibrate a scientific CCD camera and a preliminary evaluation of the
statistical uncertainty indicates the metrological interest of the method
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
Biofilm is a formation of microbial material on tooth substrata. Several
methods to quantify dental biofilm coverage have recently been reported in the
literature, but at best they provide a semi-automated approach to
quantification with significant input from a human grader that comes with the
graders bias of what are foreground, background, biofilm, and tooth.
Additionally, human assessment indices limit the resolution of the
quantification scale; most commercial scales use five levels of quantification
for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current
state-of-the-art techniques in automatic plaque quantification fail to make
their way into practical applications owing to their inability to incorporate
human input to handle misclassifications. This paper proposes a new interactive
method for biofilm quantification in Quantitative light-induced fluorescence
(QLF) images of canine teeth that is independent of the perceptual bias of the
grader. The method partitions a QLF image into segments of uniform texture and
intensity called superpixels; every superpixel is statistically modeled as a
realization of a single 2D Gaussian Markov random field (GMRF) whose parameters
are estimated; the superpixel is then assigned to one of three classes
(background, biofilm, tooth substratum) based on the training set of data. The
quantification results show a high degree of consistency and precision. At the
same time, the proposed method gives pathologists full control to post-process
the automatic quantification by flipping misclassified superpixels to a
different state (background, tooth, biofilm) with a single click, providing
greater usability than simply marking the boundaries of biofilm and tooth as
done by current state-of-the-art methods.Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics
2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image
segmentation;Manuals;Teeth},
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=636350
Visual Object Tracking: The Initialisation Problem
Model initialisation is an important component of object tracking. Tracking
algorithms are generally provided with the first frame of a sequence and a
bounding box (BB) indicating the location of the object. This BB may contain a
large number of background pixels in addition to the object and can lead to
parts-based tracking algorithms initialising their object models in background
regions of the BB. In this paper, we tackle this as a missing labels problem,
marking pixels sufficiently away from the BB as belonging to the background and
learning the labels of the unknown pixels. Three techniques, One-Class SVM
(OC-SVM), Sampled-Based Background Model (SBBM) (a novel background model based
on pixel samples), and Learning Based Digital Matting (LBDM), are adapted to
the problem. These are evaluated with leave-one-video-out cross-validation on
the VOT2016 tracking benchmark. Our evaluation shows both OC-SVMs and SBBM are
capable of providing a good level of segmentation accuracy but are too
parameter-dependent to be used in real-world scenarios. We show that LBDM
achieves significantly increased performance with parameters selected by cross
validation and we show that it is robust to parameter variation.Comment: 15th Conference on Computer and Robot Vision (CRV 2018). Source code
available at https://github.com/georgedeath/initialisation-proble
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
- …