12,668 research outputs found
Object Edge Contour Localisation Based on HexBinary Feature Matching
This paper addresses the issue of localising object
edge contours in cluttered backgrounds to support robotics
tasks such as grasping and manipulation and also to improve
the potential perceptual capabilities of robot vision systems. Our
approach is based on coarse-to-fine matching of a new recursively
constructed hierarchical, dense, edge-localised descriptor,
the HexBinary, based on the HexHog descriptor structure first
proposed in [1]. Since Binary String image descriptors [2]–
[5] require much lower computational resources, but provide
similar or even better matching performance than Histogram
of Orientated Gradient (HoG) descriptors, we have replaced
the HoG base descriptor fields used in HexHog with Binary
Strings generated from first and second order polar derivative
approximations. The ALOI [6] dataset is used to evaluate
the HexBinary descriptors which we demonstrate to achieve
a superior performance to that of HexHoG [1] for pose
refinement. The validation of our object contour localisation
system shows promising results with correctly labelling ~86% of edgel positions and mis-labelling ~3%
Crystal image analysis using synchrosqueezed transforms
We propose efficient algorithms based on a band-limited version of 2D
synchrosqueezed transforms to extract mesoscopic and microscopic information
from atomic crystal images. The methods analyze atomic crystal images as an
assemblage of non-overlapping segments of 2D general intrinsic mode type
functions, which are superpositions of non-linear wave-like components. In
particular, crystal defects are interpreted as the irregularity of local
energy; crystal rotations are described as the angle deviation of local wave
vectors from their references; the gradient of a crystal elastic deformation
can be obtained by a linear system generated by local wave vectors. Several
numerical examples of synthetic and real crystal images are provided to
illustrate the efficiency, robustness, and reliability of our methods.Comment: 27 pages, 17 figure
Using data visualization to deduce faces expressions
Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio
An automatic and efficient foreground object extraction scheme
This paper presents a method to differentiate the foreground objects from the
background of a color image. Firstly a color image of any size is input for
processing. The algorithm converts it to a grayscale image. Next we apply canny
edge detector to find the boundary of the foreground object. We concentrate to
find the maximum distance between each boundary pixel column wise and row wise
and we fill the region that is bound by the edges. Thus we are able to extract
the grayscale values of pixels that are in the bounded region and convert the
grayscale image back to original color image containing only the foreground
object
The SED Machine: a robotic spectrograph for fast transient classification
Current time domain facilities are finding several hundreds of transient
astronomical events a year. The discovery rate is expected to increase in the
future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and
the Large Synoptic Sky Survey (LSST) come on line. At the present time, the
rate at which transients are classified is approximately one order or magnitude
lower than the discovery rate, leading to an increasing "follow-up drought".
Existing telescopes with moderate aperture can help address this deficit when
equipped with spectrographs optimized for spectral classification. Here, we
provide an overview of the design, operations and first results of the Spectral
Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope
(P60). The instrument is optimized for classification and high observing
efficiency. It combines a low-resolution (R100) integral field unit (IFU)
spectrograph with "Rainbow Camera" (RC), a multi-band field acquisition camera
which also serves as multi-band (ugri) photometer. The SEDM was commissioned
during the operation of the intermediate Palomar Transient Factory (iPTF) and
has already proved lived up to its promise. The success of the SEDM
demonstrates the value of spectrographs optimized to spectral classification.
Introduction of similar spectrographs on existing telescopes will help
alleviate the follow-up drought and thereby accelerate the rate of discoveries.Comment: 21 pages, 20 figure
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