کلمات کلیدی مربوط به کتاب مدل سازی محاسباتی چشم انداز. نقش ترکیب: انفورماتیک و مهندسی کامپیوتر، گرافیک کامپیوتری
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توجه داشته باشید کتاب مدل سازی محاسباتی چشم انداز. نقش ترکیب نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Marcel Dekker, 1999. — 260 p.
This volume is a report of the
contributions of a group of us who have been working together
in the Arizona State University's Perception Laboratory since
1988. Earlier parts of our work were reported in a volume
entitled The Swimmer: An Integrated Computational Model of a
Perceptual-Motor System (Uttal, Bradshaw, Dayanand, Lovell,
Shepherd, Kakarala, Skifsted, and Tupper, 1992.)
In the years since we finished the manuscript of the first
volume describing our SWIMMER project, the work has continued
at a rapid pace. Our goal remains the same: to program our
model so that it is capable of visually sensing its
environment, interpreting that environment within the limits of
its own world model, deciding on a course of action, and then
behaving in an adaptive manner. As was our original intent,
what we have accomplished was not only an engineering tool but
also a theory of vision.
In general we have been guided by the same premises on which
the original report on the SWIMMER was founded, namely: (I)
There is much to be gained by approaching a modeling task from
a broader perspective than is usual in this field. Rather than
attempt to fine-tune a single-purpose algorithm, we sought to
integrate many different procedures and processes into a
comprehensive theory of an entire organism. B) From a purely
psychobiological point of view, a model that integrates and
combines many different weak and idiosyncratic processes into a
powerful and capable outcome is a more realistic expression of
the way the real organic perceptual-motor system works.
In recent years, these two premises have become more generally
accepted, not only as a psychophysical credo but also as a
physiological one. Two especially important books have been
published that review the substantial amount of information
supporting the notion of a separation of visual functions into
distinct modular processes as well as different anatomical
regions. The first of these is Zeki's (1993) extraordinary
review of the specialization of" function in the visual brain.
The second, Stein and Meredith's (1993) treatise on the merging
of the senses, is not limited to vision alone but deals with
the problem of how the different sense modalities interact on
both an anatomical and a behavioral level.
There is one grand idea emerging from all of the work in vision
science, including ours, these two books in particular, and the
efforts of many other scholars, scientists, and engineers in
many different fields of biology, psychology, and computer
science. That idea is that the brain cum mind is made up of a
number of nearly independent channels and specialized centers
that deal separately with the different parts of our sensory
input.
Our interest in this book is mainly with the visual processes.
The decomposable components of an image, as we elaborate in the
first chapter, are its attributes, or independently measurable
dimensions. The thesis of this book, and of an increasing
corpus of modern visual science, is that input information is
first analyzed into its component attributes and subsequently
synthesized, combined, or (in more modern terminologies) fused
or bound into a complete multiattributic perceptual experience.
Specifically, the last decade or so has seen an enormous change
in the way we believe the organic vision system operates.
Vision (with a capital V) now seems to be better described as a
collection of "visions" (with a small v).
To make this argument of a collection of "visions," scientists
in this field have traditionally used the tools of
neurophysiology or neuro-anatomy on the one hand, and of
psychophysical and perceptual data on the other. It is obvious,
however, as one surveys the literature, that in recent years
another research tool has evolved that helps to make this
argument. That new tool is the computational model, existing as
a series of program steps, operators, and algorithms.
Of course, each of these approaches is limited and incomplete.
The leap that is being attempted from the microscopic neuron to
the macroscopic perception, for example, is fraught with
conceptual and fundamental difficulties as well as with
technical ones. The case against an extreme reductionist
approach is detailed in an earlier book (Uttal, 1998). There it
was argued that neuro-reductionism is computationally
impossible and that molar psychophysics is totally incapable of
analyzing the constituent mechanisms that underlie the several
attributes. Similarly, computational modeling, it must be
appreciated, is also limited in what it can accomplish. It is
all too often ignored that excellent descriptions are not
adequate reductive explanations. Nevertheless, it is clear that
none of these approaches is, a priori, superior to any other.
Most of the difficulties of one approach are mirrored in those
of the others. The triumvirate of modeling, neuroscience, and
psychophysical approaches, when used collectively and in mutual
support, does, however, create a powerful synergism and produce
heuristics that can strongly suggest if not rigorously
prove.
Thus, this book merges several traditional approaches to
vision. The neuro-physiological, neuroanatomical, and
psychophysical literature are reviewed to identify the existing
empirical support for the notion of a modular vision system.
Then we report the details of our current modular computer
model of our "seeing" SWIMMER to elucidate further the
plausible and to eliminate the ridiculous.
Introduction: A Point of View
Neural and Psychophysical Foundations of a Vision System:
Channels and Centers, Interactions and Combinations
Models of Combination and Binding
A Vision System
A Particle System Model for Combining Edge Information from
Multiple Segmentation Modules
Combining Images for Three-Dimensional Vision
Object Recognition
Surface Reconstruction