7,848 research outputs found
Cleaning sky survey databases using Hough Transform and Renewal String approaches
Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming
from artefactual effects of the telescope, satellites and junk objects in orbit
around earth and physical defects on the photographic plate or CCD. Though
relatively small in number these spurious records present a significant problem
in many situations where they can become a large proportion of the records
potentially of interest to a given astronomer. Accurate and robust techniques
are needed for locating and flagging such spurious objects, and we are
undertaking a programme investigating the use of machine learning techniques in
this context. In this paper we focus on the four most common causes of unwanted
records in the SSS: satellite or aeroplane tracks, scratches, fibres and other
linear phenomena introduced to the plate, circular halos around bright stars
due to internal reflections within the telescope and diffraction spikes near to
bright stars. Appropriate techniques are developed for the detection of each of
these. The methods are applied to the SSS data to develop a dataset of spurious
object detections, along with confidence measures, which can allow these
unwanted data to be removed from consideration. These methods are general and
can be adapted to other astronomical survey data.Comment: Accepted for MNRAS. 17 pages, latex2e, uses mn2e.bst, mn2e.cls,
md706.bbl, shortbold.sty (all included). All figures included here as low
resolution jpegs. A version of this paper including the figures can be
downloaded from http://www.anc.ed.ac.uk/~amos/publications.html and more
details on this project can be found at
http://www.anc.ed.ac.uk/~amos/sattrackres.htm
Image feature analysis using the Multiresolution Fourier Transform
The problem of identifying boundary contours or line structures is widely recognised
as an important component in many applications of image analysis and computer
vision. Typical solutions to the problem employ some form of edge detection
followed by line following or, more commonly in recent years, Hough transforms.
Because of the processing requirements of such methods and to try to improve the
robustness of the algorithms, a number of authors have explored the use of multiresolution
approaches to the problem. Non-parametric, iterative approaches such as
relaxation labelling and "Snakes" have also been used.
This thesis presents a boundary detection algorithm based on a multiresolution
image representation, the Multiresolution Fourier Transform (MFT), which represents
an image over a range of spatial/spatial-frequency resolutions. A quadtree based
image model is described in which each leaf is a region which can be modelled using
one of a set of feature classes. Consideration is given to using linear and circular arc
features for this modelling, and frequency domain models are developed for them.
A general model based decision process is presented and shown to be applicable
to detecting local image features, selecting the most appropriate scale for modelling
each region of the image and linking the local features into the region boundary
structures of the image. The use of a consistent inference process for all of the subtasks
used in the boundary detection represents a significant improvement over the adhoc
assemblies of estimation and detection that have been common in previous work.
Although the process is applied using a restricted set of local features, the framework
presented allows for expansion of the number of boundary feature models and the
possible inclusion of models of region properties. Results are presented demonstrating
the effective application of these procedures to a number of synthetic and natural
images
Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
In this paper, we suggest a new neural network architecture for vanishing
point detection in images. The key element is the use of the direct and
transposed Fast Hough Transforms separated by convolutional layer blocks with
standard activation functions. It allows us to get the answer in the
coordinates of the input image at the output of the network and thus to
calculate the coordinates of the vanishing point by simply selecting the
maximum. Besides, it was proved that calculation of the transposed Fast Hough
Transform can be performed using the direct one. The use of integral operators
enables the neural network to rely on global rectilinear features in the image,
and so it is ideal for detecting vanishing points. To demonstrate the
effectiveness of the proposed architecture, we use a set of images from a DVR
and show its superiority over existing methods. Note, in addition, that the
proposed neural network architecture essentially repeats the process of direct
and back projection used, for example, in computed tomography.Comment: 9 pages, 9 figures, submitted to "Computer Optics"; extra experiment
added, new theorem proof added, references added; typos correcte
A Comparative study of Arabic handwritten characters invariant feature
This paper is practically interested in the unchangeable feature of Arabic
handwritten character. It presents results of comparative study achieved on
certain features extraction techniques of handwritten character, based on Hough
transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained
results show that Hough Transform and Gabor filter are insensible to the
rotation and translation, Fourier Transform is sensible to the rotation but
insensible to the translation, in contrast to Hough Transform and Gabor filter,
Wavelets Transform is sensitive to the rotation as well as to the translation
Ship Wake Detection in SAR Images via Sparse Regularization
In order to analyse synthetic aperture radar (SAR) images of the sea surface,
ship wake detection is essential for extracting information on the wake
generating vessels. One possibility is to assume a linear model for wakes, in
which case detection approaches are based on transforms such as Radon and
Hough. These express the bright (dark) lines as peak (trough) points in the
transform domain. In this paper, ship wake detection is posed as an inverse
problem, which the associated cost function including a sparsity enforcing
penalty, i.e. the generalized minimax concave (GMC) function. Despite being a
non-convex regularizer, the GMC penalty enforces the overall cost function to
be convex. The proposed solution is based on a Bayesian formulation, whereby
the point estimates are recovered using maximum a posteriori (MAP) estimation.
To quantify the performance of the proposed method, various types of SAR images
are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The
performance of various priors in solving the proposed inverse problem is first
studied by investigating the GMC along with the L1, Lp, nuclear and total
variation (TV) norms. We show that the GMC achieves the best results and we
subsequently study the merits of the corresponding method in comparison to two
state-of-the-art approaches for ship wake detection. The results show that our
proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
In this paper we present a simple and robust method for self-correction of
camera distortion using single images of scenes which contain straight lines.
Since the most common distortion can be modelled as radial distortion, we
illustrate the method using the Harris radial distortion model, but the method
is applicable to any distortion model. The method is based on transforming the
edgels of the distorted image to a 1-D angular Hough space, and optimizing the
distortion correction parameters which minimize the entropy of the
corresponding normalized histogram. Properly corrected imagery will have fewer
curved lines, and therefore less spread in Hough space. Since the method does
not rely on any image structure beyond the existence of edgels sharing some
common orientations and does not use edge fitting, it is applicable to a wide
variety of image types. For instance, it can be applied equally well to images
of texture with weak but dominant orientations, or images with strong vanishing
points. Finally, the method is performed on both synthetic and real data
revealing that it is particularly robust to noise.Comment: 9 pages, 5 figures Corrected errors in equation 1
On The Continuous Steering of the Scale of Tight Wavelet Frames
In analogy with steerable wavelets, we present a general construction of
adaptable tight wavelet frames, with an emphasis on scaling operations. In
particular, the derived wavelets can be "dilated" by a procedure comparable to
the operation of steering steerable wavelets. The fundamental aspects of the
construction are the same: an admissible collection of Fourier multipliers is
used to extend a tight wavelet frame, and the "scale" of the wavelets is
adapted by scaling the multipliers. As an application, the proposed wavelets
can be used to improve the frequency localization. Importantly, the localized
frequency bands specified by this construction can be scaled efficiently using
matrix multiplication
- …