|
Showing 1 - 4 of
4 matches in All Departments
Scale is a concept the antiquity of which can hardly be traced.
Certainly the familiar phenomena that accompany sc ale changes in
optical patterns are mentioned in the earliest written records. The
most obvious topological changes such as the creation or
annihilation of details have been a topic to philosophers, artists
and later scientists. This appears to of fascination be the case
for all cultures from which extensive written records exist. For th
instance, chinese 17 c artist manuals remark that "distant faces
have no eyes" . The merging of details is also obvious to many
authors, e. g. , Lucretius mentions the fact that distant islands
look like a single one. The one topo logical event that is (to the
best of my knowledge) mentioned only late (by th John Ruskin in his
"Elements of drawing" of the mid 19 c) is the splitting of a blob
on blurring. The change of images on a gradual increase of resolu
tion has been a recurring theme in the arts (e. g. , the poetic
description of the distant armada in Calderon's The Constant
Prince) and this "mystery" (as Ruskin calls it) is constantly
exploited by painters.
Many approaches have been proposed to solve the problem of finding
the optic flow field of an image sequence. Three major classes of
optic flow computation techniques can discriminated (see for a good
overview Beauchemin and Barron IBeauchemin19951): gradient based
(or differential) methods; phase based (or frequency domain)
methods; correlation based (or area) methods; feature point (or
sparse data) tracking methods; In this chapter we compute the optic
flow as a dense optic flow field with a multi scale differential
method. The method, originally proposed by Florack and Nielsen
[Florack1998a] is known as the Multiscale Optic Flow Constrain
Equation (MOFCE). This is a scale space version of the well known
computer vision implementation of the optic flow constraint
equation, as originally proposed by Horn and Schunck [Horn1981].
This scale space variation, as usual, consists of the introduction
of the aperture of the observation in the process. The application
to stereo has been described by Maas et al. [Maas 1995a, Maas
1996a]. Of course, difficulties arise when structure emerges or
disappears, such as with occlusion, cloud formation etc. Then
knowledge is needed about the processes and objects involved. In
this chapter we focus on the scale space approach to the local
measurement of optic flow, as we may expect the visual front end to
do. 17. 2 Motion detection with pairs of receptive fields As a
biologically motivated start, we begin with discussing some
neurophysiological findings in the visual system with respect to
motion detection.
Many approaches have been proposed to solve the problem of finding
the optic flow field of an image sequence. Three major classes of
optic flow computation techniques can discriminated (see for a good
overview Beauchemin and Barron IBeauchemin19951): gradient based
(or differential) methods; phase based (or frequency domain)
methods; correlation based (or area) methods; feature point (or
sparse data) tracking methods; In this chapter we compute the optic
flow as a dense optic flow field with a multi scale differential
method. The method, originally proposed by Florack and Nielsen
[Florack1998a] is known as the Multiscale Optic Flow Constrain
Equation (MOFCE). This is a scale space version of the well known
computer vision implementation of the optic flow constraint
equation, as originally proposed by Horn and Schunck [Horn1981].
This scale space variation, as usual, consists of the introduction
of the aperture of the observation in the process. The application
to stereo has been described by Maas et al. [Maas 1995a, Maas
1996a]. Of course, difficulties arise when structure emerges or
disappears, such as with occlusion, cloud formation etc. Then
knowledge is needed about the processes and objects involved. In
this chapter we focus on the scale space approach to the local
measurement of optic flow, as we may expect the visual front end to
do. 17. 2 Motion detection with pairs of receptive fields As a
biologically motivated start, we begin with discussing some
neurophysiological findings in the visual system with respect to
motion detection.
This seminal book is a primer on geometry-driven, nonlinear
diffusion as a promising new paradigm for vision, with an emphasis
on the tutorial. It gives a thorough overview of current linear and
nonlinear scale-space theory, presenting many viewpoints such as
the variational approach, curve evolution and nonlinear diffusion
equations. The book is meant for computer vision scientists and
students, with a computer science, mathematics or physics
background. Appendices explain the terminology. Many illustrated
applications are given, e.g. in medical imaging, vector valued (or
coupled) diffusion, general image enhancement (e.g. edge preserving
noise suppression) and modeling of the human front-end visual
system. Some examples are given to implement the methods in modern
computer-algebra systems. From the Preface by Jan J. Koenderink: I
have read through the manuscript of this book in fascination. Most
of the approaches that have been explored to tweak scale-space into
practical tools are represented here. It is easy to appreciate how
both the purist and the engineer find problems of great interest in
this area. The book is certainly unique in its scope and has
appeared at a time where this field is booming and newcomers can
still potentially leave their imprint on the core corpus of scale
related methods that still slowly emerge. As such the book is a
very timely one. It is quite evident that it would be out of the
question to compile anything like a textbook at this stage: this
book is a snapshot of the field that manages to capture its current
state very well and in a most lively fashion. I can heartily
recommend its reading to anyone interested in the issues of image
structure, scale andresolution.'
|
|