3,078 research outputs found
Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems
Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
Tangent-ball techniques for shape processing
Shape processing defines a set of theoretical and algorithmic tools for creating, measuring and modifying digital representations of shapes. Â Such tools are of paramount importance to many disciplines of computer graphics, including modeling, animation, visualization, and image processing. Â Many applications of shape processing can be found in the entertainment and medical industries.
In an attempt to improve upon many previous shape processing techniques, the present thesis explores the theoretical and algorithmic aspects of a difference measure, which involves fitting a ball (disk in 2D and sphere in 3D) so that it has at least one tangential contact with each shape and the ball interior is disjoint from both shapes.
We propose a set of ball-based operators and discuss their properties, implementations, and applications. Â We divide the group of ball-based operations into unary and binary as follows:
Unary operators include:
* Identifying details (sharp, salient features, constrictions)
* Smoothing shapes by removing such details, replacing them by fillets and roundings
* Segmentation (recognition, abstract modelization via centerline and radius variation) of tubular structures
Binary operators include:
* Measuring the local discrepancy between two shapes
* Computing the average of two shapes
* Computing point-to-point correspondence between two shapes
* Computing circular trajectories between corresponding points that meet both shapes at right angles
* Using these trajectories to support smooth morphing (inbetweening)
* Using a curve morph to construct surfaces that interpolate between contours on consecutive slices
The technical contributions of this thesis focus on the implementation of these tangent-ball operators and their usefulness in applications of shape processing. We show specific applications in the areas of animation and computer-aided medical diagnosis. Â These algorithms are simple to implement, mathematically elegant, and fast to execute.Ph.D.Committee Chair: Jarek Rossignac; Committee Member: Greg Slabaugh; Committee Member: Greg Turk; Committee Member: Karen Liu; Committee Member: Maryann Simmon
The Affine Uncertainty Principle, Associated Frames and Applications in Signal Processing
Uncertainty relations play a prominent role in signal processing, stating that a signal can not be simultaneously concentrated in the two related domains of the corresponding phase space. In particular, a new uncertainty principle for the affine group, which is directly related to the wavelet transform has lead to a new minimizing waveform. In this thesis, a frame construction is proposed which leads to approximately tight frames based on this minimizing waveform. Frame properties such as the diagonality of the frame operator as well as lower and upper frame bounds are analyzed. Additionally, three applications of such frame constructions are introduced: inpainting of missing audio data, detection of neuronal spikes in extracellular recorded data and peak detection in MALDI imaging data
The solutions to uncertainty problem of urban fractal dimension calculation
Fractal geometry provides a powerful tool for scale-free spatial analysis of
cities, but the fractal dimension calculation results always depend on methods
and scopes of study area. This phenomenon has been puzzling many researchers.
This paper is devoted to discussing the problem of uncertainty of fractal
dimension estimation and the potential solutions to it. Using regular fractals
as archetypes, we can reveal the causes and effects of the diversity of fractal
dimension estimation results by analogy. The main factors influencing fractal
dimension values of cities include prefractal structure, multi-scaling fractal
patterns, and self-affine fractal growth. The solution to the problem is to
substitute the real fractal dimension values with comparable fractal
dimensions. The main measures are as follows: First, select a proper method for
a special fractal study. Second, define a proper study area for a city
according to a study aim, or define comparable study areas for different
cities. These suggestions may be helpful for the students who takes interest in
or even have already participated in the studies of fractal cities.Comment: 27 pages, 3 figures, 8 table
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