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“There’s Plenty of Room at the Bottom“, ~ Richard Feynman (referring to NanoTechnology).
There are huge life scales in our world with which we are not acquainted to. While some prefer to wonder about “alien life” on movie theaters simultaneously eating popcorn, right here at Planet Earth, some lakes and rivers are full of them. What you see above is a tiny water flea ‘Crown Thorns‘ photographed by zoologist Jan Michels (Christian Albrecht University in Kiel, Germany). It was nominated as the best microscopic life image of 2009, last week, at BioScapes (short for Biological landscapes – a competition sponsored by Olympus in order to recognize microscope photos of plants, animals, and other life-forms that capture the “fascinating minutia of life”).
The snaking ridge at top left took top honors in the 2009 BioScapes microscope imaging contest. If water flea parents sense that their habitat is shared by their main predators, tadpole shrimp, the flea offspring sport these pointy crowns – which are unappetizing to the shrimp. Jan Michels, added a dye to reveal the tiny animal’s exoskeleton (green) and cellular nuclei (blue smudges). The blue-and-red dots are one of the animal’s compound eyes, like those of a fly.
This image, kind of remembers me of another one I used in the past for a series of Artificial Intelligence conferences I have held in the past, during 2004 (Budapest, Hungary), 2005 (Muroran, Japan) and 2006 (Jinan, China) (SIP workshop series – Swarm Intelligence and Patterns). This image below was used as the conference symbol; a termite head scanned trough SEM (Scanning Electron Microscope) taken by University of Toronto, Canada.
But probably one of the images I most love at this nano-scale is one of a red ant grabbing a tiny electronic circuit board (microchip) on his mouth (Science Museum, UK). Reason is simple. This image (below) could have several readings. By using SEM, image is formed by focusing an electron beam onto the sample surface. As the beam scans across the surface the sample emits secondary electrons which are then detected and used to modulate the image signal much like a television. More electrons is translated into a brighter image. As the beam scans the surface each point is mapped out just as the electron beam in a television maps the image onto the screen. Here we are able to see all the details of one of natures smallest denizens holding one of mankind’s smallest creations, a silicon microchip (the building blocks of digital electronics).
What’s funny is that ant colonies are known (among many other interesting features) for their remarkable cemetery organization capabilities, that is, their sequential clustering task of corpses and objects (as this microchip below). Ant colonies do show that the coordination and regulation of building activities do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating object configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony.
Ants do all this by simple manipulating objects using stigmergic capabilities. Ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. Initially, they deposit items at random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering action, organization, and brood sorting a type of self-organization and adaptive behavior. Some bio-inspired branches of computer science use this kind of behaviors to solve highly complex problems, such as Data Mining, Data analysis and classification, Data clustering, Image retrieval, among many others.
Indeed, life on its own is the ultimate science-fiction. And, as Richard Feynman mentioned once, there is plenty of room below!
With the current ongoing dramatic need of Africa to have contemporary maps (currently, Google promises to launch his first and exhaustive world-wide open-access digital cartography of the African continent very soon), back in 1999-2000 we envisioned a very simple idea into a research project (over my previous lab. – CVRM IST). Instead of producing new maps in the regular standard way, which are costly (specially for African continent countries) as well as time consuming (imagine the amount of money and time needed to cover the whole continent with high resolution aerial photos) the idea then was to hybridize trough an automatic procedure (with the help of Artificial Intelligence) new current data coming from satellites with old data coming from the computational analysis of images of old colonial maps. For instance, old roads segmented in old maps will help us finding the new ones coming from the current satellite images, as well as those that were lost. The same goes on for bridges, buildings, numbers, letters at the map, etc. However in order to do this, several preparatory steps were needed. One of those crucial steps was to obtain (segment – know to be one of the hardest procedures in image processing) the old roads, buildings, airports, at the old maps. Back in 1999-2000 while dealing with several tasks at this research project (AUTOCARTIS – Automatic Methods for Updating Cartographic Maps) I started to think of using evolutionary computation in order to tackle and surpass this precise problem, in what then later become one of the first usages of Genetic Algorithms in image analysis. The result could be checked below. Meanwhile, the experience gained with AUTOCARTIS was then later useful not only for digital old books (Visão Magazine, March 2002), as well as for helping us finding water in Mars (at the MARS EXPRESS European project – Expresso newspaper, May 2003) from which CVRM lab. was one of the European partners. Much often in life simple ideas (I owe it to Prof. Fernando Muge and Prof. Pedro Pina) are the best ones. This is particularly true in science.
Figure – One original image (left – Luanda, Angola map) and two segmentation examples, rivers and roads respectively obtained through the Genetic Algorithm proposed (low resolution images). [at the same time this precise Map of Luanda, was used by me along with the face of Einstein to benchmark several dynamic image adaptive perception versus memory experiments via ant-like artificial life systems over what I then entitled Digital Image Habitats]
 Vitorino Ramos, Fernando Muge, Map Segmentation by Colour Cube Genetic K-Mean Clustering, Proc. of ECDL´2000 – 4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.
Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or non-stationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation. Some examples in Colour Maps are presented and overall results discussed.
(to obtain the respective PDF file follow link above or visit chemoton.org)