Brain, Vision and AI

292 Pages·2008·21.78 MB·English

by  Cesare Rossi

Brain, Vision and AI

Brain, Vision and AI

Edited by
Cesare Rossi

I-Tech
IV

Published by In-Teh

In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria.

Abstracting and non-profit use of the material is permitted with credit to the source. Statements and
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First published August 2008
Printed in Croatia

A catalogue record for this book is available from the University Library Rijeka under no. 111220099
Brain, Vision and AI, Edited by Cesare Rossi
p. cm.
ISBN 978-953-7619-04-6
1. Artificial Intelligence. 2. Brain. I. Cesare Rossi
Preface

There is no doubt that Brain, Vision and Artificial Intelligence are among the new fron-
tiers of science and research; in addition these topics are particularly interesting both for the
young scientists and for the less young ones. Moreover, this field of research is very
multidisciplinary: the scientists that carry on researches on these topics belong to wide
number of different academic education since different aspects of physics, engineering and
also medicine are involved. It is really exciting to meet, at the conferences on this topic, such
a number of different knowledge all together.
It is possible to affirm that the results of the researches on Vision and AI will lead up to
considerable changes in many fields: researches on AI will improve, for instance, the interac-
tion between man and computer; Vision involves the robotic vision and automation, the
analysis of motion, the diagnostics, the cultural heritages conservation, security and surveil-
lance. Recent applications already permit to machines and other devices to interact with
man and with the environment; this had never happened in the history of man and, until
few decades ago, it seemed just science fiction.
The scientists that have contributed to this book have studied different aspects of these
disciplines, therefore it is also impossible to summarize the results of their researches.
Briefly, the main field of research concerned vision models, visual perception of motion,
neuron models, detection and restoration, models of cellular development, multiple image
detection and processing, 3D vision, human movement analysis, artificial vision applica-
tions to robotics, AI application to prediction of trip generation and distribution, applica-
tions to the replication process of the CBSRSAS systems, MOPT, ranking and extraction of
single words in a text.
The Authors that have contributed to this book work at Universities and Research Insti-
tutes, practically, all over the world and the results of their researches have been published
on international journals and appreciated in many international conferences.

Editor

Cesare Rossi
Full Professor of Applied Mechanics
University of Naples “Federico II” ITALY

VII

Contents

Preface V

1. Visual Perception of Semi-transparent Blotches: Detection and Restoration 001
V. Bruni, A. J. Crawford, A. Kokaram and D. Vitulano

2. Computing the Vulnerable Phase in a 2D Discrete Model 027
of the Hodgkin-Huxley Neuron
Dragos Calitoiu, B. John Oommen and Doron Nussbaum

3. Bio-inspired Connectionist Modelling: 057
An Application to Visual Perception of Motion
Claudio Castellanos Sánchez and Pedro Luis Sánchez

4. Cell Pattern Generation in Artificial Development 073
Arturo Chavoya

5. I’m Sorry to Say, But Your Understanding of Image Processing 095
Fundamentals Is Absolutely Wrong
Emanuel Diamant

6. Multiple Image Objects Detection, Tracking, and Classification 111
using Human Articulated Visual Perception Capability
HeungKyu Lee

7. Consideration of various Noise Types and Illumination 127
Effects for 3D shape recovery
Aamir Saeed Malik and Tae-Sun Choi

8. Cooperative intelligent agents for speeding up the Replication of 143
Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS)
Mostafa M. H. Ellabaan

9. Investigating the Performance of Rule-based Models with 167
Increasing Complexity on the Prediction of Trip Generation and Distribution
Elke Moons, Geert Wets and Marc Aerts
VIII

10. Laban Movement Analysis using a Bayesian model 183
and perspective projections
Joerg Rett, Jorge Dias and Juan-Manuel Ahuactzin

11. Video System in Robotic Applications 211
Vincenzo Niola, Cesare Rossi, Sergio Savino and Salvatore Strano

12. Multiple Object Permanence Tracking: Maintenance, Retrieval and 243
Transformation of Dynamic Object Representations
Jun Saiki

13. Ranking and Extraction of Relevant Single Words in Text 265
João Ventura and Joaquim Ferreira da Silva
1
Visual Perception of Semi-transparent Blotches:
Detection and Restoration
V. Bruni1, A. J. Crawford1, A. Kokaram2 and D. Vitulano1
1Istituto per le Applicazioni del Calcolo ”M. Picone”- C.N.R.
2Electronic and Electrical Engineering Department, University of Dublin, Trinity College
1Italy, 2Ireland
1. Introduction
Digital image restoration has become a popular area of research (Gonzalez & Woods,2002).
The increased demand for archived material and the increasing power of computers have
led to a need for digital methods for the restoration of degradation. Common degradation
includes noise, line-scratches, tear, moire, shake and flicker (see for instance (Bruni &
Vitulano, 2004; Corrigan & Kokaram, 2004; Kokaram, 1998; Kokaram, 2004; Roosmalen et al.,
1999)).
An important area in digital image processing, and in particular in digital restoration, is
human visual perception (Winkler, 2005). Perception laws are crucial in several image
processing models. They allow for the improvement of the visual quality of the result using
adaptive and automatic methods. The visibility of a given object over a background depends
on contrast sensitivity, luminance adaptation and contrast masking (Winkler, 2005; Damera-
Venkata et al,2000; Gutiérrez et al., 2006; Barba & Barba, 2002; Pappas & Safranek, 2000).
These measures are well defined for uniform objects over uniform backgrounds while they
may fail in presence of sinusoidal stimuli, i.e. highly detailed regions (Damera-Venkata et
al,2000; Nadenau et al., 2003). In this case, perception depends on the distance between the
observer and the object, according to the width of the analysed region and the frequency of
the stimulus. It turns out that point-wise contrast measures that do not take into account the
global visibility of an object in a given image, can fail in the presence of complicated
patterns.

Figure 1. Examples of semi-transparent blotches: Note the variation in intensity and colour
while the underlying detail remains
2 Frontiers in Brain, Vision and AI
This is the case for semi-transparent blotches which partially or completely obscure regions
of images (see Fig. 1). They are usually caused by dirt or moisture on archived material and
can be seen as complicated objects over more or less detailed patterns (Kokaram, 1998;
Stanco et al. 2005). They appear as irregular regions with variable shape and size, having a
slightly different colour from the original one. This is the reason why they can be easily
confused with scene components. The additional difficulty is their semi-transparency since
they do not completely hide the underlying original information. Classical restoration or
inpainting methods (Gonzalez & Woods, 2002; Bertalmio et al. 2000; Criminisi et al. 2004)
cannot be used as the original image content must be retained: this is an important issue
from an historical point of view. It turns out that their recognition and restoration require
the introduction of global measures, like the contrast of a region (group of pixels), instead of
pixel-wise measures.
This chapter introduces a generalized perception based model that mainly exploits two
global perception based measures oriented to the detection of the most visible object over a
given context. In order to be significant, they must be evaluated over an image component
(frequency, colour channel, etc.) where the blotches are more visible --- often the most
visible part. This component is used both for detecting and for modelling the overall blotch
shape. This is then used to guide the blotch removal in the remaining image components
according to human perception. The choice of the best component can be inferred by the
physical model that causes the degradation under study. The distortion measures account
for the variation of the visibility of a set of pixels over a changing background and the
variation of the visibility of a changing set of pixels over a fixed background. The ’changing’
function can be modified with respect to the analysed case. In the simplest case the clipping
operator can be used. Its aim is to separate two different regions, as is the case for the
detection and restoration of semi-transparent blotches. The two measures are based on the
hypothesis that blotches are detectable (by human observers) at a ‘first glance’ over the
image, since they are recognized as ”foreign” objects in different contexts over the same
image. The two measures can be used for both a global detection and a local refinement of
the result: the largest region where the blotch is the most visible object. The proposed
approach has been tested on two very frequent examples: scratches on old films (Kokaram,
1998) and water blotches on archived documents (Stanco et al., 2005). Extensive
experimental results have shown that the proposed model achieves high visual quality
results in very delicate situations, in a completely automatic manner and with a low
computational effort.
2. A perception based model
As already discussed in the previous section, semi-transparent blotches are very difficult to
manage because of their structure: they show the same frequency properties of the affected
image. Nonetheless, they are usually perceived by a human observer ’at first glance’. It is
very difficult to understand the mechanics of this process because of the high variability of
both the blotch appearance and the image context of the blotch. In fact, if on one hand their
intrinsic structure produces a sort of masking, on the other this peculiarity enhances its
visual detection by a human observer. The proposed model tries to exploit this
contradictory aspect in order to write a mathematical model that may fit this very singular
behaviour.