Human and Machine Vision: Analogies and Divergencies

399 Pages·1994·16.58 MB·English

by  Silvana Vallerga (auth.)

Human and
Machine Vision
Analogies and Divergencies
Human and
Machine Vision
Analogies and Divergencies
Edited by
Virginio Cantoni
University of Pavia
Pavia, Italy
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data
On file
Proceedings of the Third International Workshop on Perception,
held September 27-30, 1993, in Pavia, Italy
ISBN 978-1-4899-1006-6 ISBN 978-1-4899-1004-2 (eBook)
DOI 10.1007/978-1-4899-1004-2
© Springer Science+Business Media New York 1994
Originally published by Plenum Press, New York in 1994
Softcover reprint of the hardcover 1st edition 1994
All rights reserved
No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form
or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise,
without written permission from the Publisher
In honor of the memory of
Prof. Eduardo Caianiello
PREFACE
The following are the proceedings of the Third International Workshop on Perception held
in Pavia, Italy, on September 27-30, 1993, under the auspices of four institutions: the Group of
Cybernetic and Biophysics (GNCB)s of the National Research Council (CNR), the Italian
Association for Artificial Intelligence (AI * IA), the Italian Association of Psychology (AlP),
and the Italian Chapter of the International Association for Pattern Recognition (IAPR).
The theme of this third workshop was: "Human and Machine Vision: Analogies and
Divergencies."
A wide spectrum of topics was covered, ranging from neurophysiology, to computer
architecture, to psychology, to image understanding, etc. For this reason the structure of this
workshop was quite different from those of the first two held in Parma (1991), and Trieste
(1992). This time the workshop was composed ofj ust eight modules, each one consisting of two
invited lectures (dealing with vision in nature and machines, respectively) and a common panel
discussion (including the two lecturers and three invited panellists).
The lectures aimed at presenting the state-of-the-art and outlining open questions. In
particular, they were intended to stress links, suggesting possible synergies between different
cultural areas. The panel discussion was conceived as a forum for an open debate, briefly
introduced by each panellist, and mainly aimed at a deeper investigation of "analogies and
divergencies" between the different approaches to vision, including the search for possible new
solutions to problems in the different fields. The panellists were asked to prepare a few
statements on hot-points as a guide for discussion. These statements were delivered to the
participants together with the final program, for more qualified discussion.
The number of participants to the workshop was limited to 60. Besides the 40 invited
lecturers and panellists, 20 more participants were admitted. Priority for these positions was
given to young researchers who made important contributions to the open discussions. The
lectures and the contents of the panel discussion are included in the present proceedings.
Table 1 shows the scheduled program with the eight modules. Minor variations were made
to the workshop presentations, and in the reports, to accommodate the positions that emerged from
the debates. In the following paragraphs each subject is briefly introduced and commented upon.
Vision Architectures
The first module sought to compare a taxonomy of the bio-vision systems developed over
eons of evolution, with the zoology of the vision machines that have been built or proposed over
the last 40 years. Obviously, as was pointed out in the discussion, this comparison is promising,
but mandates precaution.
During evolution, biological vision developed through a number of functional steps, each
of which has supported the survival and development of the animal world. The lowest level of
phylogenetic development, the simple structure of an insect compound eye, is able to detect
vii
viii V. Cantoni
Table 1. The Eight Subjects
Subject Nature Machine Panel
Vision The phylogenetic Machine vision One model for vision
Architectures evolution of the architectures system?
visual system
Retinae Neurophysiology On chip vision Logmap and multi-
of the retina scale approaches
Visual Primitives Neurophysiology Visual forms and Looking for visual
of the retina data structures primitives
Camera and Eye The oculomotor Active vision Allocation of attention
Control System system in vision
Motion Perception and Time-flow and Motion analysis Dynamic perception of
Interpretation movement in hu. the environment
man vision
Visual Representations Visual cognition Representations Icons and words: meta-
and cognitive maps and models phors and symbolism
Visual Reasoning Mental models and Spatial reasoning Image interpretation
spatial reasoning and ambiguities
Imagination and Imagery and ima- Learning and neu- Perceptual learning
Learning gination ral nets and discovery
polarised light and the whole visible spectrum from IR to UV. At higher levels, a decrease in the
capacity for light detection, retinal processing, adaptation in response to environmental and/or
behavioural stimuli, has been accompanied by a corresponding increase in central processing.
Machine vision developments have been driven to a large extent by the evolution of
processing power in the computer era. The history of computer vision architectures is the history
of computer parallelism, with a last period in which VLSI design facilities have become accessible
to most researchers, supporting efforts towards the integration of sensing and analog and digital
processing, and guiding the development of smart vision sensors and vision front ends.
Broadly speaking, it looks as if the natural trend is towards an enrichment of high-level
central processing (often to the detriment of periphery capability). In contrast, the present trend
in computer vision is towards a higher, and more powerful, allocation of processing resources
in periphery. Nevertheless, if we return to the panel, the hot points, despite this situation,look
very attractive for both computer scientists and bio-experts. The panel highlighted several
common fields of investigation, such as high-level control and hierarchies for sensor signal
manipulations, flexibility and adaptability of vision systems, exploitation of illusions and
drawbacks, basic hardware and biological material capabilities. In summary, the panel on
architectures for vision has given the richest of contributions and position statements from a
very large spectrum of competence.
Retinae
Neuroscientists have shown that the retina of vertebrates is, in origin and by functional
organisation, a parallel computational unit similar to a portion of the central nervous system.
Preface ix
Indeed, its peripheral location, the possibility of using precisely controlled natural stimuli
(without artificial electrical stimulation) and of analysing the resulting signals delivered
through the optic nerve, makes the retina one of the best understood components of the nervous
system.
The approximately 120 million retina photoreceptors have a decreasing distribution
density going from the fovea (close to the visual axis, and composed of uniformly and densely
distributed cones) towards the periphery (composed mainly of highly interagent rods). The
retina between the receptors and the optic nerve follows a "vertical" pathway formed by the
receptor-bipolar-ganglion cells, which is integrated with a two level 'horizontal' organisation
consisting respectively of the horizontal and amacrine cells. The ratio between the number of
receptors, bipolar and ganglion cells in the fovea is close to I : 1 : 1, whereas in the periphery it
is of hierarchical nature: the ratio between rods, bipolar and ganglion cells is 100 : 17: 1. In the
two vertical layers downstream, there is a concentration of data with a 100 + 50 ratio between
the receptors and fibres of the optical nerve (ganglion cells). Finally, it is worthwhile
mentioning that the retina receives a retrograde pathway "from cortical centres through a
system of fibres that spread widely within its layers."
As was mentioned above, from an architectural viewpoint, an increasingly common
solution for machine vision is based on an intelligent sensor combined with a control
mechanism capable of re-directing the sensor to "just'" the right data needed to accomplish the
required tasks. In particular, this mechanism must be able to switch "attention" in a pre-defined,
or shortly determined, sequence between the relevant areas of the scene. The retina of
vertebrates allows this with minimum effort (cost). Many ongoing research efforts in micro
electronics and computer vision try to exploit silicon technology to embed a retina into a single
chip, drawing an analogy from the fovea solution. Altemati ve approaches to supporting sensing
and processing capabilities on the same device have been investigated. One example is by
integrating an array of light sensors with an associated array of simple Boolean processors by
adopting wholly analog, mixed analog-digital, or wholly digital processing modalities; hence
CCD versus CMOS silicon technology; ASIC or general purpose implementations; log-polar,
space variant or using uniformly distributed photoreceptor arrays.
The panel confirmed that to achieve real-time performances on a large field of view, with
restricted computational resources, a vision system must be capable of: rapidly selecting the
regions of interest in space and time; changing, as required, the data resolution, to enable
operations with local and global (or regional) features; rapidly updating the strategies to the
partial results achieved step by step. The retina-foveation solution is very attractive from the
cost/benefit viewpoint, but choice of its implementation and interpretation is problematic. From
the computational viewpoint other proposals have been made which offer different benefits.
Amongst others we debated: log-polar representations reproducing the retino-cortical mapping
and supporting some invariance for scale and rotation; multi-scale, quad-tree and pyramidal
approaches that support hardware implementation and planning strategies in varying degrees;
wave-let transformation characterised by a compact support and a localisation in space and
frequency components.
Visual Primitives
The primary visual cortex performs an "intermediate" stage of analysis: information
coming from the retina is examined for various parameters of the visual stimuli (e.g. orientation,
colour, velocity, etc.), and transmitted along neural pathways to higher-order visual areas. The
analysis occurring in the visual cortex is performed by interacting cells that playa detector role
with receptive fields of various sizes, selectively sensitive to the orientation of lines or edges or
even textures. The cells also act as spatial frequency filters, and, whilst covering the range of
x V. Cantoni
spatial frequencies at which the animal visual system is sensitive, different cells are tuned to
different spatial frequencies. The cells are architecturally arranged into columns perpendicular
to the cortical surface, according to similar stimulus selectivity, ocular dominance, and spatial
frequency. There is evidence, however, for horizontal long-range connections assuring com
munication between columns of similar orientation preference and integrating information over
a large part of the visual field. In primates, colour information is processed by classes of cortical
neurons with chromatically opposing receptive fields. Moreover, there is evidence of nonvisual
input to the striate visual cortex, e.g. the interaction between sensory and oculomotor informa
tion to process binocular disparity that is modulated by the changes in ocular vergence and
accommodation.
In machine vision much work has been done to devise a "complete" and "open" set of feature
detectors that can be applied to solve a generic vision task. Completeness means that all the basic
features have been taken into consideration, in the sense that it is possible to represent a generic
visual form (sometimes in a hierarchical manner, following the principle of recursive decomposi
tion, and/or approximate representations). Openness means that new, "ad hoc" detectors can be
easily added if the peculiarities of the current data are not exploited in the set. These basic modules
(or primitives) measure internal and external form characteristics, by including photometric,
geometric, statistic, and morphologic, etc., operators. Knowledge of the task at hand can provide
criteria for the selection of a profitable subset of primitives. Some attempts at building an expert
system that automatically discriminates the subset, were also made.
If retina behaviour is easily analysed, the cortical areas are not (at least with equal effort)
for physiological reasons (e.g. interfaces and interactions, higher connectivity and plasticity,
neuronal information coding). Perhaps mainly for this motive, as for the machine vision case,
many common questions still remain open: what are the suitable shape primitives? What role do
visual primitives play subservient to the task? What is the role of knowledge and context when
looking for primitives? To what extent is the use offeature descriptors necessary or effective?
Camera and Eye Control Systems
Visual sensing in man and primates is performed through a sophisticated control system for
eye movement which is composed of different agents. Phylogenetic evolution has led to reflexes
such as the vestibulo-ocular and the optokinetic ones, which prevent the image of the environment
from slipping on the retina; to voluntary or involuntary fast position-resetting mechanisms for
gaze orienting and visual scanning of the wide field of view (through saccadic movements); to eye
rotation for focusing (by vergence or version movements in frontal-eyed animals), and velocity
servomechanism for pursuit of the selected targets. The conflicts between these different agents,
and interaction with other sensors and/or between the different activity subsystems (like head
movements that may either be synergistic, as during pursuit of moving targets by means of
combined eye and head movements, or antagonistic, as during fixation of stationary targets while
the head is moving) are solved through a hierarchical control structure.
During the last decade the computer vision community introduced a paradigm called
active vision to model the capability for dynamic and purposeful modification of the parameters
of the vision sensory apparatus in order to simplify the visual task. "Smart" sensors have since
been designed containing control loops which implement alerting mechanisms (pre-attentive,
diffused attention, unpurposive scanning); windowing processes for foveation and attention
focusing on a small region of interests; target tracking, trajectory detection, and collision
predictions. These achievements have led to the visual-front-end, which, combined with a high
level reasoning system that guides the observation strategy and selection between concurrent
stimuli, can arrive at the interpretation of what is going on in the scene.
Preface xi
Both the camera and eye control systems introduced so far gain efficiency when combined
with the capacity to rapidly select the regions of interest (both in space and time), to allocate
resources following data resolution changes, and to update the strategies to the partial results thus
achieved. The key role in the implementation of these processes is played by the "allocation of
attention strategy." Note that attention processes can work at different levels: from the purposive
or focused or conscious or voluntary level to the diffused or pre-attentive or covert or implicit
level. Following the panel coordinator: "Solving the attention problem (understanding thoroughly
how it works) seems tantamount to solving the vision problem itself."
Motion Perception and Interpretation
There is evidence that visual perception in humans is "intrinsically dynamic," in the sense
that motion is one of the dominant primitives. The superiority of dynamic to static primitives is
shown in the capacity to recover the spatial 3D structure (which is a well-known ill-posed
problem in a static frame) and in the direct perception of the environment. Proximity is
considered, for the detection of the reference frame, at the higher control levels of the
hierarchical structure of perception for motion as well. Moreover, "the motion organization
reflects a structure of relationships between objects in term of 'actions', not simply of
displacements": dynamic events are rich in information (for instance, the kinematic pattern
after a collision furnishes material on the ratio between the masses of the objects). To this end,
the motor-perceptual interaction has been of great importance for human motion perception. It
has been stated (at least in the case of stimuli originating from external motor activities) that
knowledge of the rules governing the human motor-control-system guides the visual recogni
tion of biological movement.
The computational approaches for machine detection and estimation of motion parameters
are distinguished into two categories: those following the correspondence scheme in which
motion is conceived as a set of displacements, and those following the gradient scheme that
follows a velocity-based formulation. In the first case, a set of significant tokens (usually
relatively sparse) is extracted from the image segments and tracked along the frame sequence;
in the second case, the two-dimensional field of instantaneous pixel velocities, called optical
flow, is estimated without considering correspondences of noticeable features. For both
approaches, the monocular technique gives ambiguous solutions from motion: at least one
scaling factor is unknown; instead the stereo pair approach can provide absolute values.
In order to properly interpret the motion parameters it is necessary to process the motion
components (uniformly distributed or sparse) thus far detected. Besides the reliable estimation
of the primitive motion parameters, events and motion perception require taking into account
what is "known" about the properties that the stimuli should satisfy, such as inverse optics,
transformations, invariants, salient features and functional relationships of the perceived 3D
world. In dynamic perception some degree of ambiguity exists in all the quoted subjects. The
panel represented an attempt to elucidate the relevant ambiguities.
Visual Representations
This module consists of a cascade of subjects: how visual mental imagery may be relevant
to hypothesis formation; what representation formalisms of visual information may be effective
in machine perception and artificial intelligence; the role and the efficiency of visual metaphors
in the conceptualisation of new solutions.
We use imagery to remember, to anticipate, to answer a question, or to invent something
new. A few meaningful statements on visual representations and thoughts are presented by