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MORPHOLOGICAL IMAGE PROCESSING
Morphological image processing is a type of processing in which the spatial form or structure of objects within an image are modified. Dilation, erosion, and skeletonization are three fundamental morphological operations. With dilation, an object grows uniformly in spatial extent, whereas with erosion an object shrinks uniformly. Skeletonization results in a stick figure representation of an object.
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EDGE DETECTION
Changes or discontinuities in an image amplitude attribute such as luminance or tristimulus value are fundamentally important primitive characteristics of an image because they often provide an indication of the physical extent of objects within the image. Local discontinuities in image luminance from one level to another are called luminance edges. Global luminance discontinuities, called luminance boundary segments, are considered in Section 17.4. In this chapter the definition of a luminance edge is limited to image amplitude discontinuities between reasonably smooth regions. Discontinuity detection between textured regions is considered in Section ...
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IMAGE FEATURE EXTRACTION
An image feature is a distinguishing primitive characteristic or attribute of an image. Some features are natural in the sense that such features are defined by the visual appearance of an image, while other, artificial features result from specific manipulations of an image. Natural features include the luminance of a region of pixels and gray scale textural regions. Image amplitude histograms and spatial frequency spectra are examples of artificial features.
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IMAGE SEGMENTATION
Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Image edges and texture are also useful attributes for segmentation. The definition of segmentation adopted in this chapter is deliberately restrictive; no contextual information is utilized in the segmentation. Furthermore, segmentation does not involve classifying each segment....
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SHAPE ANALYSIS
Several qualitative and quantitative techniques have been developed for characterizing the shape of objects within an image. These techniques are useful for classifying objects in a pattern recognition system and for symbolically describing objects in an image understanding system. Some of the techniques apply only to binary-valued images; others can be extended to gray level images.
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IMAGE DETECTION AND REGISTRATION
This chapter covers two related image analysis tasks: detection and registration. Image detection is concerned with the determination of the presence or absence of objects suspected of being in an image. Image registration involves the spatial alignment of a pair of views of a scene.
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PIKS IMAGE PROCESSING SOFTWARE
PIKS contains a rich set of operators that perform manipulations of multidimensional images or of data objects extracted from images in order to enhance, restore, or assist in the extraction of information from images. This chapter presents a functional overview of the PIKS standard and a more detailed definition of a functional subset of the standard called PIKS Core.
colour instead of color). For consistency with the PIKS standard, the British spelling convention has been adopted for this chapter....
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PIKS IMAGE PROCESSING PROGRAMMING EXERCISES
Digital image processing is best learned by writing and executing software programs that implement image processing algorithms. Toward this end, the compact disk affixed to the back cover of this book provides executable versions of the PIKS Core Application Program Interface C programming language library, which can be used to implement exercises described in this chapter. The compact disk contains the following items: A Solaris operating system executable version of the PIKS Core API. A Windows 2000 and Windows NT operating system executable version of the PIKS Core API....
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The areas of adaptive filtering and change (fault) detection are quite active
fields. both in research and applications . Some central keywords of the book
are listed in Table 1.1, and the figures. illustrated in Figure 1.1, give an idea
of the relative activity in the different areas . For comparison. the two related
and well established areas of adaptive control and system identification are
included in the table
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This chapter provides background information and problem descriptions
of the applications treated in this book . Most of the applications include
real data and many of them are used as case studies examined throughout the
book with different algorithms
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The basic assumption in this part. signal estimationis. that them easurements
gt consist of a deterministic component Ot ~ the signal ~ and additive white
noise et.
yt = Ot + et .
Adaptive Filtering and Change Detection
Fredrik Gustafsson
Copyright © 2000 John Wiley & Sons, Ltd
ISBNs: 0-471-49287-6 (Hardback); 0-470-84161-3 (Electronic)
58 On-line amroaches
For change detection, this will be labeled as a change in the mean model.
The task of determining Bt from yt will be referred to as estimation, and
change detection or alarming is the task of finding abrupt, or rapid, changes
in dt, which is assumed to start at time L, referred to as the change...
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This chapter surveys off-line formulations of single and multiple change point
estimation . Although the problem formulation yields algorithms that process
data batch.wise, many important algorithms have natural on-line implementations
and recursive approximations . This chapter is basically a projection of
the more general results in Chapter 7 to the case of signal estimation . There
are, however. some dedicated algorithms for estimating one change point offline
that apply to the current case of a scalar signal model . In the literature
of mathematical statistics. this area is known as change point estimation ....
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The noise is here assumed white with variance X, and will sometimes be restricted
to be Gaussian. The last expression is in a polynomial form, whereas
G, H are filters. Time-variability is modeled by time-varying parameters Bt.
The adaptive filtering problem is to estimate these parametersb y an adaptive
filter,
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Model validation is the problem of deciding whether observed data are consistent
with a nominal model . Change detection based on model validation aims
at applying a consistency test in one of the following ways:
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Let us start with considering change detection in linear regressions as an offline
problem. which will be referred to as segmentation . The goal is to find a
Adaptive Filtering and Change Detection
Fredrik Gustafsson
Copyright © 2000 John Wiley & Sons, Ltd
ISBNs: 0-471-49287-6 (Hardback); 0-470-84161-3 (Electronic)
232 Chanae detection b baanskes df il toenr
sequence of time indices kn = (kl, k2, .., kn), where both the number n and the
locations ki are unknown, such that a linear regression model with piecewise
constant parameters,...
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The goal in this section is to explain the fundamentals of Kalman filter theory
by a few illustrative examples.
The Kalman filter requires a state space model for describing the signal
dynamics. To describe its role, we need a concrete example, so let us return to
the target tracking example from Chapter 1. Assume that we want a model
with the states z1 = X , x2 = Y, x3 = X och x4 = Y . This is the simplest
possible case of state vector used in practice. Before we derive a model in the
next section, a few remarks will be given on what role...
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This chapter is devoted to the problem of detecting additive abrupt changes
in linear state space models . Sensor and actuator faults as a sudden offset
or drift can all be modeled as additive changes . In addition. disturbances are
traditionally modeled as additive state changes . The likelihood ratio formulation
provides a general framework for detecting such changes. and to isolate
the fault/disturbance .
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This chapter addresses the most general problem formulation of detection in
linear systems. Basically, all problem formulations that have been discussed
so far are included in the framework considered. The main purpose is to
survey multiple model algorithms, and a secondary purpose is to overview
and compare the state of the art in different application areas for reducing
complexity, where similar algorithms have been developed independently.
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Consider a batch of data over a sliding window, collected in a measurement
vector Y and input vector U. As in Chapter 6, the idea of a consistency
test is to apply a linear transformation to a batch of data, AiY + BiU + ci.
The matrices Ai, Bi and vector G are chosen so that the norm of the linear
transformation is small when there is no change/fault according to hypothesis
Hi, and large when fault Hi has appeared.
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It might also be the signal component st of the measurement yt = st +ut. The
measurements zt consist of the measured outputs yt and, when appropriate,
the inputs ut.
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The purpose of this section is to get a geometric understanding of linear estimation
. First. we outline how projections are computed in linear algebra
for finite dimensional vectors . Functional analysis generalizes this procedure
to some infinite-dimensional spaces (so-called Hilbert spaces). and finally. we
point out that linear estimation is a special case of an infinite-dimensional
space
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EVOLVING MOBILE NETWORKS
While the history of mobile communications is long [1–3], and the background of mo bile networks therebyx is also long, in this chapter we focus on the historic evolution in terms of network architecture and services starting with 2nd generation (2G) mobile systems. In particular we consider the development of the architecture of Global Systems for Mobile Communications (GSM), since it is by far the most widespread mobile system in the world today.
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SYSTEM ANALYSIS FUNDAMENTALS
FUNDAMENTALS OF SYSTEM ANALYSIS
Third generation systems focus on providing a universal platform to afford multifarious communications options at all levels, i.e. the radio as well as the core network sides. This implies the application of optimum techniques in multiple access and interworking protocols for the physical and upper layers, respectively.
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THE UMTS DEVELOPMENT PLATFORM
ARCHITECTURE AND DEPLOYMENT SCENARIOS
The architecture at the domain and functional levels, as well as the deployment scenarios are presented based on the 3GPP (ETSI) specifications noted in [1,2]. The terminology and basic principles are kept for consistency with a simplified approach in some cases, and for a pragmatic representation of the subject in others.
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THE UTRA PHYSICAL LAYER DESIGN
The UTRA design is comprised basically of three parts, i.e. radio aspects corresponding primarily to the physical layer, radio interface aspects incorporating layers two and three, and network aspects inter-working directly with the core network. This chapter describes the UTRA physical layer including both FDD and TDD modes, as well as spreading and modulation, multiplexing and channel coding, and physical layer procedures.
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THE UTRA1 TRANSMISSION SYSTEM
UMTS SPECTRUM ALLOCATION
The UMTS frequency ranges are part of the world wide spectrum allocation for 3rd or evolving 2nd generation systems. Figure 5.1 illustrates the representation of the spectrum from major regions (e.g. Europe, Japan, Korea, and USA).
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SERVICE COMPONENTS IN UMTS
BACKGROUND
UMTS services will not only offer mobile services supported by 2nd generation systems such as GSM, but will also expand these services to higher rates and greater flexibility. The services evolving in the GSM platform through its Circuit Switched (CS) and Packet Switched (PS) services will continue in UMTS while new services are introduced.
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Logically deploying 3G networks implies dimensioning and implementing corresponding elements within a geographical area, where an operator would desire to offer advanced mobile communications services, e.g. voice, mobile Internet, video-telephony, etc. In the preceding chapters we have outlined the service requirements and technical specifications of the UMTS solution. In this chapter we aim to describe the application of the proposed solutions and go through the process of designing a network to provide UMTS services. ...
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RESOURCE AND NETWORK MANAGEMENT
INTRODUCTION
Operating a 3G network involves managing resources and Network Elements (NE). This chapter covers these two aspects to complete the deployment issues started in Chapter 7. Resources here refer primarily to the radio resources and NE refers to the 3G building blocks, i.e. elements in the CS, PS and radio access networks.
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TOWARDS IP BASED NETWORKS
BACKGROUND
In the preceding chapters we covered UMTS in the context of the 3GPP Release 99 specifications. This chapter covers the forthcoming releases of UMTS, primarily Release 4 and 5 formerly Release 00. However, before we describe the reference architecture we outline the vision of the UMTS technical specification evolution from Ref. [1].
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