1,782 research outputs found
Simultaneous multi-band detection of Low Surface Brightness galaxies with Markovian modelling
We present an algorithm for the detection of Low Surface Brightness (LSB)
galaxies in images, called MARSIAA (MARkovian Software for Image Analysis in
Astronomy), which is based on multi-scale Markovian modeling. MARSIAA can be
applied simultaneously to different bands. It segments an image into a
user-defined number of classes, according to their surface brightness and
surroundings - typically, one or two classes contain the LSB structures. We
have developed an algorithm, called DetectLSB, which allows the efficient
identification of LSB galaxies from among the candidate sources selected by
MARSIAA. To assess the robustness of our method, the method was applied to a
set of 18 B and I band images (covering 1.3 square degrees in total) of the
Virgo cluster. To further assess the completeness of the results of our method,
both MARSIAA, SExtractor, and DetectLSB were applied to search for (i) mock
Virgo LSB galaxies inserted into a set of deep Next Generation Virgo Survey
(NGVS) gri-band subimages and (ii) Virgo LSB galaxies identified by eye in a
full set of NGVS square degree gri images. MARSIAA/DetectLSB recovered ~20%
more mock LSB galaxies and ~40% more LSB galaxies identified by eye than
SExtractor/DetectLSB. With a 90% fraction of false positives from an entirely
unsupervised pipeline, a completeness of 90% is reached for sources with r_e >
3" at a mean surface brightness level of mu_g=27.7 mag/arcsec^2 and a central
surface brightness of mu^0 g=26.7 mag/arcsec^2. About 10% of the false
positives are artifacts, the rest being background galaxies. We have found our
method to be complementary to the application of matched filters and an
optimized use of SExtractor, and to have the following advantages: it is
scale-free, can be applied simultaneously to several bands, and is well adapted
for crowded regions on the sky.Comment: 39 pages, 18 figures, accepted for publication in A
Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations
Concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. Identifying and tracking the amount and severity of cracking is paramount to evaluating the current condition and predicting the future service life of a concrete asset. Concrete cracks can indicate reinforcement corrosion, the development of spalls or changing support conditions. Therefore, monitoring cracks during the life span of concrete structures has been an effective technique to evaluate the level of safety and preparing plans for future appropriate rehabilitation.
One growing technique are unmanned inspections using Unmanned Aerial Vehicles (UAV). UAVs are drones equipped with cameras, sensors, GPS, etc. RGB images (color images in Red, Green and Blue color space) are obtained from a camera mounted on a UAV flying around the structure, to detect cracks and other defects.
Each image captured by UAV needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the dimension of the cracks (length and width).
The report consists of the following sections: A literature review of image processing techniques used in structural health monitoring and other fields of interest is provided in chapter 2. The Proposed method to identify cracks is demonstrated in Chapter 3. Experimental results, conclusion and future work are presented in Chapter 4. Appendix A includes the processed images using the proposed method and Appendix B includes the comparison between Talabâs method and the proposed method. In Appendix C, a âreadmeâ file is given to run the program, and finally Appendix D shows the Matlab Code
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research
Image processing by region extraction using a clustering approach based on color
This thesis describes an image segmentation technique based on watersheds, a clustering technique which does not use spatial information, but relies on multispectral images. These are captured using a monochrome camera and narrow-band filters; we call this color segmentation, although it does not use color in a physiological sense. A major part of the work is testing the method developed using different color images.
Starting with a general discussion of image processing, the different techniques used in image segmentation are reviewed, and the application of mathematical morphology to image processing is discussed. The use of watersheds as a clustering technique in two- dimensional color space is discussed, and system performance illustrated. The method can be improved for industrial applications by using normalized color to eliminate the problem of shadows. These methods are extended to segment the image into regions recursively. Different types of color images including both man made color images, and natural color images have been used to illustrate performance. There is a brief discussion and a simple illustration showing how segmentation can be used in image compression, and of the application of pyramidal data structures in clustering for coarse segmentation.
The thesis concludes with an investigation of the methods which can be used to improve these segmentation results. This includes edge extraction, texture extraction, and recursive merging
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Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
Vision Science and Technology at NASA: Results of a Workshop
A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program
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Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems
A thesis submitted to the Faculty of Engineering and the Built Environment,
University of the Witwatersrand, Johannesburg, in ful lment of the requirements for
the degree of Doctor of Philosophy.
Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with
the resulting loss of speech. With recent advances in portable computing power,
automatic lip-reading (ALR) may become a viable approach to voice restoration. This
thesis addresses the image processing aspect of ALR, and focuses three contributions
to colour-based lip segmentation.
The rst contribution concerns the colour transform to enhance the contrast
between the lips and skin. This thesis presents the most comprehensive study to
date by measuring the overlap between lip and skin histograms for 33 di erent
colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%,
and results show that selecting the correct transform can increase the segmentation
accuracy by up to three times.
The second contribution is the development of a new lip segmentation algorithm
that utilises the best colour transforms from the comparative study. The algorithm
is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation
error (SE) of 7:39 %.
The third contribution focuses on the impact of the histogram threshold on the
segmentation accuracy, and introduces a novel technique called Adaptive Threshold
Optimisation (ATO) to select a better threshold value. The rst stage of ATO
incorporates -SVR to train the lip shape model. ATO then uses feedback of shape
information to validate and optimise the threshold. After applying ATO, the SE
decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp
or relative improvement of 15:1%. While this thesis concerns lip segmentation in
particular, ATO is a threshold selection technique that can be used in various
segmentation applications.MT201
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