7,208 research outputs found
Restoration of the cantilever bowing distortion in Atomic Force Microscopy
Due to the mechanics of the Atomic Force Microscope (AFM),
there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. In this paper, an automated algorithm to flatten lines from AFM images is presented. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNACNTs) and synthetic experiments are presented, demonstrating the
effectiveness of the proposed algorithm in increasing the resolution of the surface topography. In addition a link between the flattening problem and MRI inhomogeneity (shading) is given and the proposed method is compared to an entropy based MRI inhomogeniety correction method
Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds
This manuscript provides a collection of new methods for the automated
detection of non-overlapping ellipses from edge points. The methods introduce
new developments in: (i) robust Monte Carlo-based ellipse fitting to
2-dimensional (2D) points in the presence of outliers; (ii) detection of
non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder
from 3D point clouds. The proposed methods were thoroughly compared with
established state-of-the-art methods, using simulated and real-world datasets,
through the design of four sets of original experiments. It was found that the
proposed robust ellipse detection was superior to four reliable robust methods,
including the popular least median of squares, in both simulated and real-world
datasets. The proposed process for detecting non-overlapping ellipses achieved
F-measure of 99.3% on real images, compared to F-measures of 42.4%, 65.6%, and
59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis,
respectively. The proposed cylinder extraction method identified all detectable
mechanical pipes in two real-world point clouds, obtained under laboratory, and
industrial construction site conditions. The results of this investigation show
promise for the application of the proposed methods for automatic extraction of
circular targets from images and pipes from point clouds
Reconstruction of industrial piping installations from laser point clouds using profiling techniques
Includes abstract.Includes bibliographical references (leaves 143-152).As-built models of industrial piping installations are essential for planning applications in industry. Laser scanning has emerged as the preferred data acquisition method of as built information for creating these three dimensional (3D) models. The product of the scanning process is a cloud of points representing scanned surfaces. From this point cloud, 3D models of the surfaces are reconstructed. Most surfaces are of piping elements e.g. straight pipes, t-junctions, elbows, spheres. The automatic detection of these piping elements in point clouds has the greatest impact on the reconstructed model. Various algorithms have been proposed for detecting piping elements in point clouds. However, most algorithms detect cylinders (straight pipes) and planes which make up a small percentage of piping elements found in industrial installations. In addition, these algorithms do not allow for deformation detection in pipes. Therefore, the work in this research is aimed at the detection of piping elements (straight pipes, elbows, t-junctions and flange) in point clouds including deformation detection
A review of sensor technology and sensor fusion methods for map-based localization of service robot
Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robotâs own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications
The development and implementation of a reverse engineering method for near net shape parts
This research presents a new method for the reverse engineering of Near Net Shape (NNS) parts that bridge the current 3D scanning and Rapid Prototyping technologies. Near Net Shape is a group of manufacturing technologies that includes forging, casting, hot isostatic pressing, and additive manufacturing. This research focuses on casting process and provides a software tool along with the new method for reverse engineering a legacy casting design to the âas wasâ casted state instead of the âas isâ current state, and at the same time, reducing the cost and time for repairing a legacy casting part.
The three main objective for this research is to 1.Create a new reverse engineering method 2.Develop a software tool that is designed for feature free model editing 3.Validate the process through metal casting.
The Point Cloud Library is applied for assisting point cloud processing and feature free model editing. A series of algorithms is developed for draft adding and pattern generation for the process of casting. The Rapid Pattern Manufacturing system developed in Iowa State University, Rapid Manufacturing and Prototyping Lab is applied for pattern manufacturing.
This method is validated to be correct and able to reverse engineer legacy casting parts rapidly and economically through a metal casting process.
The layout of this thesis is as follows: Chapter 1: provides introduction, background, research problem statement and objective of this research. Chapter 2: a literature review for the current reverse engineering method and introduces the modules of point cloud library that are used in this research. Chapter 3: presents the overview of method and algorithms that developed for this method in detail. Chapter 4: presents the implementation of this method and gives the analysis of the demo metal casting process. Chapter 5: provides future work and conclusions
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbourâs security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
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