ANN: 7 Online works on Swarm Intelligence, Pattern Recognition, Image Processing and Perception
Vitorino RAMOS
Five recent works hybridizing the novel Swarm Intelligence area with
applications in Pattern Recognition, Perception, Image Processing,
Image Analysis, Mathematical Morphology, Image Classification and
retrieval are now online. Keywords subject areas are: Swarm
Intelligence, Perception and Image Processing, Pattern Recognition,
Mathematical Morphology, Social Cognitive Maps, Social Foraging,
Self-Organization, Distributed Search, among others. For those
following research in similar areas or are somehow interest, here
follows specific links, abstracts, as well as direct PDF links:
Kind regards, v.
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# Carlos Fernandes, Vitorino Ramos, Agostinho C. Rosa, Self-Regulated
Artificial Ant Colonies on Digital Image Habitats, accepted in WCLC-05,
2nd World Congress on Lateral Computing, Springer-Verlag, LNCS Series,
Bangalore, India, 16-18 Dec., 2005.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_65.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-WCLC05b.pdf
ABSTRACT: Artificial life models, swarm intelligent and evolutionary
computation algorithms are usually built on fixed size populations.
Some studies indicate however that varying the population size can
increase the adaptability of these systems and their capability to
react to changing environments. In this paper we present an extended
model of an artificial ant colony system designed to evolve on digital
image habitats. We will show that the present swarm can adapt the size
of the population according to the type of image on which it is
evolving and reacting faster to changing images, thus converging more
rapidly to the new desired regions, regulating the number of his image
foraging agents. Finally, we will show evidences that the model can be
associated with the Mathematical Morphology Watershed algorithm to
improve the segmentation of digital grey-scale images.
# Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan
F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images,
accepted in WCLC-05, 2nd World Congress on Lateral Computing,
Springer-Verlag, LNCS Series, Bangalore, India, 16-18 Dec., 2005.
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Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_53.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-WCLC05a.pdf
ABSTRACT: The collection of wild larvae seed as a source of raw
material is a major sub industry of shellfish aquaculture. To predict
when, where and in what quantities wild seed will be available, it is
necessary to track the appearance and growth of planktonic larvae. One
of the most difficult groups to identify, particularly at the species
level are the Bivalvia. This difficulty arises from the fact that
fundamentally all bivalve larvae have a similar shape and colour.
Identification based on gross morphological appearance is limited by
the time-consuming nature of the microscopic examination and by the
limited availability of expertise in this field. Molecular and
immunological methods are also being studied. We describe the
application of computational pattern recognition methods to the
automated identification and size analysis of scallop larvae. For
identification, the shape features used are binary invariant moments;
that is, the features are invariant to shift (position within the
image), scale (induced either by growth or differential image
magnification) and rotation. Images of a sample of scallop and
non-scallop larvae covering a range of maturities have been analysed.
In order to overcome the automatic identification, as well as to allow
the system to receive new unknown samples at any moment, a
self-organized and unsupervised ant-like clustering algorithm based on
Swarm Intelligence is proposed, followed by simple k-NNR nearest
neighbour classification on the final map. Results achieve a full
recognition rate of 100% under several situations (k =1 or 3).
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# Vitorino Ramos, Fernando Muge, Pedro Pina, Self-Organized Data and
Image Retrieval as a Consequence of Inter-Dynamic Synergistic
Relationships in Artificial Ant Colonies, in Javier Ruiz-del-Solar,
Ajith Abraham and Mario Koeppen (Eds.), Frontiers in Artificial
Intelligence and Applications, Soft Computing Systems - Design,
Management and Applications, 2nd Int. Conf. on Hybrid Intelligent
Systems, IOS Press, Vol. 87, ISBN 1 5860 32976, pp. 500-509, Santiago,
Chile, Dec. 2002.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_39.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-HIS02.pdf
ABSTRACT: Social insects provide us with a powerful metaphor to create
decentralized systems of simple interacting, and often mobile, agents.
The emergent collective intelligence of social insects "swarm
intelligence" resides not in complex individual abilities but rather in
networks of interactions that exist among individuals and between
individuals and their environment. The study of ant colonies behavior
and of their self-organizing capabilities is of interest to knowledge
retrieval/ management and decision support systems sciences, because it
provides models of distributed adaptive organization which are useful
to solve difficult optimization, classification, and distributed
control problems, among others. In the present work we overview some
models derived from the observation of real ants, emphasizing the role
played by stigmergy as distributed communication paradigm, and we
present a novel strategy (ACLUSTER) to tackle unsupervised data
exploratory analysis as well as data retrieval problems. Moreover and
according to our knowledge, this is also the first application of ant
systems into digital image retrieval problems. Nevertheless, the
present algorithm could be applied to any type of numeric data.
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# Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive
Maps, Swarm Collective Perception and Distributed Search on Dynamic
Landscapes, submitted to Brains, Minds & Media, Journal of New Media in
Neural and Cognitive Science, NRW, Germany (2006).
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-BMM.pdf
ABSTRACT: Swarm Intelligence (SI) is the property of a system whereby
the collective behaviors of (unsophisticated) entities interacting
locally with their environment cause coherent functional global
patterns to emerge. SI provides a basis with wich it is possible to
explore collective (or distributed) problem solving without centralized
control or the provision of a global model. To tackle the formation of
a coherent social collective intelligence from individual behaviors, we
discuss several concepts related to Self-Organization, Stigmergy and
Social Foraging in animals. Then, in a more abstract level we suggest
and stress the role played not only by the environmental media as a
driving force for societal learning, as well as by positive and
negative feedbacks produced by the many interactions among agents.
Finally, presenting a simple model based on the above features, we will
adress the collective adaptation of a social community to a cultural
(environmenatl, contextual) or media informational dynamical landscape,
represented here - for the purpose of different experiments - by
several three-dimensional mathematical functions that suddenly change
over time. Results indicate that the collective intelligence is able to
cope and quickly adapt to unforseen situations even when over the same
cooperative foraging period, the community is requested to deal with
two different and contradictory purposes.
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# Vitorino Ramos, Self-Organizing the Abstract: Canvas as a Swarm
Habitat for Collective Memory, Perception and Cooperative Distributed
Creativity, in 1st Art & Science Symposium - Models to Know Reality, J.
Rekalde, R. Ibanez and A. Simo (Eds.), pp. 59, Facultad de Bellas
Artes EHU/UPV, Universidad del Pais Vasco, 11-12 Dec., Bilbao, Spain,
2003.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_47.html
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/aswarm.html
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# Vitorino Ramos, On the Implicit and on the Artificial - Morphogenesis
and Emergent Aesthetics in Autonomous Collective Systems, in ARCHITOPIA
Book, Art, Architecture and Science, INSTITUT D'ART CONTEMPORAIN, J.L.
Maubant et al. (Eds.), pp. 25-57, Chapter 2, ISBN 2905985631 - EAN
9782905985637, France, Feb. 2002.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_37.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ARCHITOPIA.pdf
ABSTRACT: Imagine a "machine" where there is no pre-commitment to any
particular representational scheme: the desired behaviour is
distributed and roughly specified simultaneously among many parts, but
there is minimal specification of the mechanism required to generate
that behaviour, i.e. the global behaviour evolves from the many
relations of multiple simple behaviours. A machine that lives to and
from/with Synergy. An artificial super-organism that avoids specific
constraints and emerges within multiple low-level implicit bio-inspired
mechanisms.
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# Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital
Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition,
Proceedings of ANTS'2000 - 2nd International Workshop on Ant
Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin
Middendorf & Thomas Stuezle (Eds.), pp. 113-116, Brussels, Belgium, 7-9
Sep. 2000.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_29.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ANTS00.pdf
ABSTRACT: Some recent studies have pointed that, the self-organization
of neurons into brain-like structures, and the self-organization of
ants into a swarm are similar in many respects. If possible to
implement, these features could lead to important developments in
pattern recognition systems, where perceptive capabilities can emerge
and evolve from the interaction of many simple local rules. The
principle of the method is inspired by the work of Chialvo and Millonas
who developed the first numerical simulation in which swarm cognitive
map formation could be explained. From this point, an extended model is
presented in order to deal with digital image habitats, in which
artificial ants could be able to react to the environment and perceive
it. Evolution of pheromone fields point that artificial ant colonies
could react and adapt appropriately to any type of digital habitat.
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