36,509 research outputs found
Automatic detection of arcs and arclets formed by gravitational lensing
We present an algorithm developed particularly to detect gravitationally
lensed arcs in clusters of galaxies. This algorithm is suited for automated
surveys as well as individual arc detections. New methods are used for image
smoothing and source detection. The smoothing is performed by so-called
anisotropic diffusion, which maintains the shape of the arcs and does not
disperse them. The algorithm is much more efficient in detecting arcs than
other source finding algorithms and the detection by eye.Comment: A&A in press, 12 pages, 16 figure
The Area Distribution of Solar Magnetic Bright Points
Magnetic Bright Points (MBPs) are among the smallest observable objects on
the solar photosphere. A combination of G-band observations and numerical
simulations is used to determine their area distribution. An automatic
detection algorithm, employing 1-dimensional intensity profiling, is utilized
to identify these structures in the observed and simulated datasets. Both
distributions peak at an area of 45000 km, with a sharp decrease
towards smaller areas. The distributions conform with log-normal statistics,
which suggests that flux fragmentation dominates over flux convergence.
Radiative magneto-convection simulations indicate an independence in the MBP
area distribution for differing magnetic flux densities. The most commonly
occurring bright point size corresponds to the typical width of intergranular
lanes.Comment: Astrophysical Journal, accepte
A systematic algorithm development for image processing feature extraction in automatic visual inspection : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in the Department of Production Technology, Massey University
Image processing techniques applied to modern quality control are described together with the development of feature extraction algorithms for automatic visual inspection. A real-time image processing hardware system already available in the Department of Production Technology is described and has been tested systematically for establishing an optimal threshold function. This systematic testing has been concerned with edge strength and system noise information. With the a priori information of system signal and noise, non-linear threshold functions have been established for real time edge detection. The performance of adaptive thresholding is described and the usefulness of this nonlinear approach is demonstrated from results using machined test samples. Examination and comparisons of thresholding techniques applied to several edge detection operators are presented. It is concluded that, the Roberts' operator with a non-linear thresholding function has the advantages of being simple, fast, accurate and cost effective in automatic visual inspection
A contour matching approach for accurate NOAA-AVHRR image navigation
Although different methods for NOAA AVHRR image navigation have already been established, the multitemporal and multi-satellite character of most studies requires automatic and accurate methods for navigation of satellite images. In the proposed method, a simple Kepplerian orbital model for the NOAA satellites is considered as reference model, and mean orbital elements are given as input to the model from ephemeris data. In order to correct the errors caused by these simplifications, errors resulting from inaccuracies in the positioning of the satellite and failures in the satellite internal clock, an automatic global contour matching approach has been adopted. First, the sensed image is preprocessed to obtain a gradient energy map of the reliable areas (sea-land contours) using a cloud detection algorithm and a morphological gradient operator. An initial estimation of the reliable contour positions is automatically obtained. The final positions of the contours are obtained by means of an iterative local minimization procedure that allows a contour to converge on an area of high image energy (edge). Global transformation parameters are estimated based on the initial and final positions of all reliable contour points. Finally, the performance of this approach is assessed using NOAA 14 AVHRR images from different geographic areas.Postprint (published version
Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology
The incidence of thyroid nodule is very high and generally increases with the
age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid
nodule can be completely cured if detected early. Fine needle aspiration
cytology is a recognized early diagnosis method of thyroid nodule. There are
still some limitations in the fine needle aspiration cytology, and the
ultrasound diagnosis of thyroid nodule has become the first choice for
auxiliary examination of thyroid nodular disease. If we could combine medical
imaging technology and fine needle aspiration cytology, the diagnostic rate of
thyroid nodule would be improved significantly. The properties of ultrasound
will degrade the image quality, which makes it difficult to recognize the edges
for physicians. Image segmentation technique based on graph theory has become a
research hotspot at present. Normalized cut (Ncut) is a representative one,
which is suitable for segmentation of feature parts of medical image. However,
how to solve the normalized cut has become a problem, which needs large memory
capacity and heavy calculation of weight matrix. It always generates over
segmentation or less segmentation which leads to inaccurate in the
segmentation. The speckle noise in B ultrasound image of thyroid tumor makes
the quality of the image deteriorate. In the light of this characteristic, we
combine the anisotropic diffusion model with the normalized cut in this paper.
After the enhancement of anisotropic diffusion model, it removes the noise in
the B ultrasound image while preserves the important edges and local details.
This reduces the amount of computation in constructing the weight matrix of the
improved normalized cut and improves the accuracy of the final segmentation
results. The feasibility of the method is proved by the experimental results.Comment: 15pages,13figure
Segmentation of Sedimentary Grain in Electron Microscopy Image
This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application
Arcfinder: An algorithm for the automatic detection of gravitational arcs
We present an efficient algorithm designed for and capable of detecting
elongated, thin features such as lines and curves in astronomical images, and
its application to the automatic detection of gravitational arcs. The algorithm
is sufficiently robust to detect such features even if their surface brightness
is near the pixel noise in the image, yet the amount of spurious detections is
low. The algorithm subdivides the image into a grid of overlapping cells which
are iteratively shifted towards a local centre of brightness in their immediate
neighbourhood. It then computes the ellipticity for each cell, and combines
cells with correlated ellipticities into objects. These are combined to graphs
in a next step, which are then further processed to determine properties of the
detected objects. We demonstrate the operation and the efficiency of the
algorithm applying it to HST images of galaxy clusters known to contain
gravitational arcs. The algorithm completes the analysis of an image with
3000x3000 pixels in about 4 seconds on an ordinary desktop PC. We discuss
further applications, the method's remaining problems and possible approaches
to their solution.Comment: 12 pages, 12 figure
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