888 research outputs found
Fingerprint Orientation Refinement Through Iterative Smoothing
We propose a new gradient-based method for the extraction of the orientation field associated to a
fingerprint, and a regularisation procedure to improve the orientation field computed from noisy
fingerprint images. The regularisation algorithm is based on three new integral operators, introduced and
discussed in this paper. A pre-processing technique is also proposed to achieve better performances of the
algorithm. The results of a numerical experiment are reported to give an evidence of the efficiency of the
proposed algorithm
Surface Modeling and Analysis Using Range Images: Smoothing, Registration, Integration, and Segmentation
This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images.
The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation.
Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme.
Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison.
Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid. Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method.
The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics
Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures
This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison
Feature-preserving image restoration and its application in biological fluorescence microscopy
This thesis presents a new investigation of image restoration and its application to
fluorescence cell microscopy. The first part of the work is to develop advanced image
denoising algorithms to restore images from noisy observations by using a novel featurepreserving
diffusion approach. I have applied these algorithms to different types of
images, including biometric, biological and natural images, and demonstrated their
superior performance for noise removal and feature preservation, compared to several
state of the art methods. In the second part of my work, I explore a novel, simple and
inexpensive super-resolution restoration method for quantitative microscopy in cell
biology. In this method, a super-resolution image is restored, through an inverse process,
by using multiple diffraction-limited (low) resolution observations, which are acquired
from conventional microscopes whilst translating the sample parallel to the image plane,
so referred to as translation microscopy (TRAM). A key to this new development is the
integration of a robust feature detector, developed in the first part, to the inverse process
to restore high resolution images well above the diffraction limit in the presence of strong
noise. TRAM is a post-image acquisition computational method and can be implemented
with any microscope. Experiments show a nearly 7-fold increase in lateral spatial
resolution in noisy biological environments, delivering multi-colour image resolution of
~30 nm
An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways
Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h
Carried baggage detection and recognition in video surveillance with foreground segmentation
Security cameras installed in public spaces or in private organizations continuously
record video data with the aim of detecting and preventing crime. For that reason,
video content analysis applications, either for real time (i.e. analytic) or post-event
(i.e. forensic) analysis, have gained high interest in recent years. In this thesis,
the primary focus is on two key aspects of video analysis, reliable moving object
segmentation and carried object detection & identification.
A novel moving object segmentation scheme by background subtraction is presented
in this thesis. The scheme relies on background modelling which is based
on multi-directional gradient and phase congruency. As a post processing step,
the detected foreground contours are refined by classifying the edge segments as
either belonging to the foreground or background. Further contour completion
technique by anisotropic diffusion is first introduced in this area. The proposed
method targets cast shadow removal, gradual illumination change invariance, and
closed contour extraction.
A state of the art carried object detection method is employed as a benchmark
algorithm. This method includes silhouette analysis by comparing human temporal
templates with unencumbered human models. The implementation aspects of
the algorithm are improved by automatically estimating the viewing direction of
the pedestrian and are extended by a carried luggage identification module. As
the temporal template is a frequency template and the information that it provides
is not sufficient, a colour temporal template is introduced. The standard
steps followed by the state of the art algorithm are approached from a different
extended (by colour information) perspective, resulting in more accurate carried
object segmentation.
The experiments conducted in this research show that the proposed closed
foreground segmentation technique attains all the aforementioned goals. The incremental
improvements applied to the state of the art carried object detection
algorithm revealed the full potential of the scheme. The experiments demonstrate
the ability of the proposed carried object detection algorithm to supersede the
state of the art method
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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