34,490 research outputs found
A Fisher-Rao metric for paracatadioptric images of lines
In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but
the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied.
The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and
based on the Hough transform
Ganalyzer: A tool for automatic galaxy image analysis
We describe Ganalyzer, a model-based tool that can automatically analyze and
classify galaxy images. Ganalyzer works by separating the galaxy pixels from
the background pixels, finding the center and radius of the galaxy, generating
the radial intensity plot, and then computing the slopes of the peaks detected
in the radial intensity plot to measure the spirality of the galaxy and
determine its morphological class. Unlike algorithms that are based on machine
learning, Ganalyzer is based on measuring the spirality of the galaxy, a task
that is difficult to perform manually, and in many cases can provide a more
accurate analysis compared to manual observation. Ganalyzer is simple to use,
and can be easily embedded into other image analysis applications. Another
advantage is its speed, which allows it to analyze ~10,000,000 galaxy images in
five days using a standard modern desktop computer. These capabilities can make
Ganalyzer a useful tool in analyzing large datasets of galaxy images collected
by autonomous sky surveys such as SDSS, LSST or DES. The software is available
for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and
the data used in the experiment are available at
http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.Comment: ApJ, accepte
Image processing for plastic surgery planning
This thesis presents some image processing tools for plastic surgery planning. In particular,
it presents a novel method that combines local and global context in a probabilistic
relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic
surgery. It also uses a method that utilises global and local symmetry to identify abnormalities
in CT frontal images of the human body. The proposed methodologies are
evaluated with the help of several clinical data supplied by collaborating plastic surgeons
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
Principled Design and Implementation of Steerable Detectors
We provide a complete pipeline for the detection of patterns of interest in
an image. In our approach, the patterns are assumed to be adequately modeled by
a known template, and are located at unknown position and orientation. We
propose a continuous-domain additive image model, where the analyzed image is
the sum of the template and an isotropic background signal with self-similar
isotropic power-spectrum. The method is able to learn an optimal steerable
filter fulfilling the SNR criterion based on one single template and background
pair, that therefore strongly responds to the template, while optimally
decoupling from the background model. The proposed filter then allows for a
fast detection process, with the unknown orientation estimation through the use
of steerability properties. In practice, the implementation requires to
discretize the continuous-domain formulation on polar grids, which is performed
using radial B-splines. We demonstrate the practical usefulness of our method
on a variety of template approximation and pattern detection experiments
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