You are currently browsing the tag archive for the ‘Image Analysis’ tag.
Images – Portugal (1A – top left, original input satellite image below), geodesically stretched by one of my Mathematical Morphology algorithms, in order to represent real travel times from each of the 18 regional districts in Portugal, to the rest of the territory. From the 18, three capital districts are represented here. As departing from Lisbon (1B – top right), from Faro (1C – South of Portugal, bottom left), and from Bragança (1D – North-East region, bottom right). [World Exposition, Lisbon, Territory pavilion, 1998].
Recently one of my colleagues who knows I love maps, pointed me to an old TV show “Câmara Clara“, a cultural TV show by RTP2, at one of the main public Portuguese TV stations. Main reason for my interest was his current theme: Maps. My second reason was their guests: Joaquim Ferreira do Amaral (an ex-Minister with a passion for maps) and Manuel Lima, which wonderful work on information visualization I know for a long time (on one of my past posts I referred to one of his ongoing working sites: visualcomplexity).
For my complete and positive surprise, their interview ended with some new examples, being one of my old works referred (from 57m 12s up to 60m 26s on http://camaraclara.rtp.pt/#/arquivo/131 ). It’s a long story on how I ended doing these kind of maps. Part of it, it’s here. During 1998, the World Exposition was in Portugal, and I got invited to present a set of 18 different maps from the Portuguese territory. So I decided to geodesically stretch the travel distances from any of the 18 different capital districts, to the rest of the territory, in order to represent travel Time not Distance, or Distance as time. For that, I have coded new algorithms based on Mathematical Morphology (MM), taking in account every road (from main roads to regional, check some images below), from which I applied different MM operators.
Unfortunately, many of those maps are now lost. I did tried hard to find them from my old digital archives, but only found those above, which represent the departure from Lisbon (the Capital), Faro and Bragança. So, if by any reason you happen to have some photos from the 1998’s World Exposition in Lisbon, inside the Territory pavilion, I would love to receive them.
Video (LINK) – “Câmara Clara” TV show by journalist Paula Moura Pinheiro dedicated to maps (nº 131), at one of the main public Portuguese TV stations (RTP2), broadcasted on May 3 2009, in Portuguese.
A sketchy summary of this TV program went on something like this (the poor translation is mine): At the year Google promises to launch his first and exhaustive world-wide open-access digital cartography of the African continent, Joaquim Ferreira do Amaral, passioned by the Portuguese World Discover History and collector of historical maps, joins as guest with Manuel Lima, the Portuguese information designer that recently Creativity magazine has considered one of the top bright minds along with Google and Amazon founders, debating the importance of “navigating” reality with a map. From the Portuguese cartographic history, know to be the best in the XV and XVI centuries, up to the actual state-of-the-art in this area, from which Manuel Lima is considered to be one of the top researchers at global scale.
No, not the Grand Caynon neither the Epstein & Axtell Sugarscape (link) this time, instead a soundscape. A landscape made of sounds or grooves. Look at this as an ancient form of encapsulating data. Taken by Chris Supranowitz, a researcher at The Insitute of Optics at the University of Rochester (US), the image depicts a single groove on a vinyl record magnified 1000 times, using electron microscopy. Dark bits are the top of the grooves, i.e. the uncut vinyl, while even darker little bumps are dust on the record (e.g. centre right). For more images check SynthGear, and found out (image link) what have they discovered if we keep magnifying that image further still!
Photo – Rover’s Self Portrait (link): this Picasso-like self portrait of NASA’s Curiosity rover was taken by its Navigation cameras, located on the now-upright mast. The camera snapped pictures 360-degrees around the rover, while pointing down at the rover deck, up and straight ahead. Those images are shown here in a polar projection. Most of the tiles are thumbnails, or small copies of the full-resolution images that have not been sent back to Earth yet. Two of the tiles are full-resolution. Image credit: NASA/JPL-Caltech (August, 9, 2012). [6000 x 4500 full size link].
Figure – Application of Mathematical Morphology openings and closing operators of increasing size on different digital images (from Fig. 2, page 5).
[] Vitorino Ramos, Pedro Pina, Exploiting and Evolving R{n} Mathematical Morphology Feature Spaces, in Ronse Ch., Najman L., Decencière E. (Eds.), Mathematical Morphology: 40 Years On, pp. 465-474, Springer Verlag, Dordrecht, The Netherlands, 2005.
(abstract) A multidisciplinary methodology that goes from the extraction of features till the classification of a set of different Portuguese granites is presented in this paper. The set of tools to extract the features that characterize the polished surfaces of granites is mainly based on mathematical morphology. The classification methodology is based on a genetic algorithm capable of search for the input feature space used by the nearest neighbor rule classifier. Results show that is adequate to perform feature reduction and simultaneous improve the recognition rate. Moreover, the present methodology represents a robust strategy to understand the proper nature of the textures studied and their discriminant features.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Journalism is dying, they say. I do agree. And while the argue continues, many interested on the issue are now debating what really is the reason. The question is…, there is no reason at all, there are many. Intricate ones. Do ponder on this: while newspapers are facing the immense omnipresent and real-time competition from TV channels, TV on itself is dying also (while unexpectedly, … Radio is surging). On many broadcasted programs, TV anchors are now more important than the invited people who, on that subject (supposedly) worked hardly over years to provide that precise innovative content. As in large supermarkets and great malls, package by these means have turned more important than the content in itself. This related business editorial pressure for news quickness have become so intensive and aggressive, that contents are replaced every second without judge and once in the air hardly described, discussed, opposed or dessicated. So at large, TV CEO’s producers think that people are no longer waiting for a new interesting content to appear, they are instead waiting for the anchor which passes them down as they were peanuts. Peanuts are good, but in excess – we all agree – are damn awful. And many do so, as an old passive addiction. Which means that in the long run, nothing remains (fact for both sides); … And if they give me no opportunity at all to check content carefully, if I happen to be on the mood to, … So, I move on. Buy this precise simple way, media cannibalizes itself.
We all know that attention spam is getting narrower these days, and, e.g., yes… greater literature classics are no longer read. So, Media CEO’s say – “they have no time“. But, really … do mind that gap. Think twice. If the whole environment suddenly recognizes (being this one of the major questions – see below) that they are getting enough of peanuts (and they really are), they will urge for beef-steaks. In fact, eating 1000 void peanuts takes more time to consume than one large good beef! And there is a difference, … the beef remains on our body for several hours, not seconds.
It’s promptly becoming a paradox, since Media CEO’s on their blindness competition refuge on saying that they – us readers – have no time (when in mediocrity no solution is found, easiest way is to repeat a mantra), and we (mostly of us) keep zapping news as never before. However, they never realized that we keep zapping it, because no news – by these means – are of interest. They really all have become the same. And once they appear all the same, they all soon disappear from our minds. … We all in some aspects all wonder, what really happened to research journalism, stories about new complex issues, strong content, explained in detail but still provided in simple eloquent ways? Come on, this long-tailed huge market niche, once yours, is now void!
Newspapers do have this wonderful singularity. They still have journalists (at least some, if they had enough vision to nourish them). They could provide insightful detailed backup stories, open questions, or debating new ones as no one can in public space. Moreover, they have time from their consumers. That, at least, is what I am feed-backing to Guardian every Sunday when I put my money over the news bench in change for this newspaper, along others like The Economist. But in face of these overall great news-without-sense turmoil cascade, probably one of these days, people will instead desire silence… or listening to their grandfathers knowledge, good-sense, and long-lived emotion (which keeps increasing believe me). They will relate to him, as never before. Not newspapers. At least, he do provides content.
But once the media is set (and in some way, not all the way, medium is the message, as postulated by Marshall McLuhan), the great gold-run will be on, … guess what, … content. And on relationships among content! Journalism will be no longer under atomization. Or crystallized.
Fig. – Spatial distribution of 931 items (words taken from an article at ABC Spanish newspaper) on a 61 x 61 non-parametric toroidal grid, at t=106. 91 ants used type 2 probability response functions, with k1=0.1 and k2=0.3. Some independent clusters examples are: (A) anunció, bilbao, embargo, titulos, entre, hacer, necesídad, tras, vida, lider, cualquier, derechos, medida.(B) dirigentes, prensa, ciu. (C) discos, amigos, grandes. (D) hechos, piloto, miedo, tipo, cd, informes. (E) dificil, gobierno, justicia, crisis, voluntad, creó, elección, horas, frente, técnica, unas, tarde, familia, sargento, necesídad, red, obra … (among other word semantic clusters; check paper article below).
For long, media decided to do nothing, while new media including social media was coming in to the plateu, stronger as never before. Let me give you one example. In order to understand how relations between item news could enhnace newspaper reading and social awareness, back in 2002 I decided to make an experiment. Together with a colleague, we took one article of the Spanish ABC magazine (photo above). The article was about spanish political parties and corruption. It contained 931words (snapshot above). In order to extract semantic meaning from it as a pre-processing computer analysis, we started by applying Latent Semantic Analysis (LSA). Then, Swarm Intelligent algorithms were developed in order to have a glimpse on the relations among all those words on the newspaper article. Guess what? Some words like “big”, friends” and “music discs” were segmented from the rest of the political related article (segregated it on a remote semantic “island”), that is, not only a whole conceptual semantic atlas of that entire news section was possible, as well as finding unrelated issues (which were uncorrelated semantic “islands”). Now, just imagine if this happens within a newspaper social network, live, 24 hours a day, while people grab for strong co-related content and discuss it as it happens. One strong journal article, could in facto, evolve to social collective knowledge and awareness as never before. That, in reality is something that classic journalism could use as and edge for their (nowadays awful) market approach. Providing not only good content, but along with it, an extra service not available anyware (which is in some way, priceless): The chance to provide co-related real-time meta-content. Not one view, but many aggregated views. Edited real-world real-time good quality journalism which has the potential of an “endless” price, namely these days. On the other hand, what we now see is that news CEO’s along with some editors still keep their minds on 19th century journalism. For worse, due to their legitimic panic. However, meanwhile, the world has indeed evolved.
[] Vitorino Ramos, Juan J. Merelo, Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning, in AEB´2002 – 1st Spanish Conference on Evolutionary and Bio-Inspired Algorithms, E. Alba, F. Herrera, J.J. Merelo et al. (Eds.), pp. 284-293, Centro Univ. de Mérida, Mérida, Spain, 6-8 Feb. 2002.
Social insect societies and more specifically ant colonies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases exceed the individual capabilities of a single ant. 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 to tackle unsupervised clustering as well as data retrieval problems. The present ant clustering system (ACLUSTER) avoids not only short-term memory based strategies, as well as the use of several artificial ant types (using different speeds), present in some recent approaches. Moreover and according to our knowledge, this is also the first application of ant systems into textual document clustering.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Pranav Mistry and SixthSense technology – Part 1 of 2
Pranav Mistry and SixthSense technology – Part 2 of 2
Video – ABB FlexPicker Robots (Source: http://www.botjunkie.com/ + http://www.abb.com/)
As well as, something at the lower pre-processing engineering level involving also Pattern Recognition, Image Analysis and Classification. Not for brownies, cookies or sausages. Since this is summer time, it relates with clams and bivalve in general. From the video, everything appears to be rather easy. But, they are not.
Fig. – Illusion created by Prof. Akiyoshi Kitaoka (Dep. of Psychology, Ritsumeikan Univ., Kyoto, Japan). If you don’t see any illusion at all, don’t worry. That’s exactly why this optical illusion is so great. The illusion is not there, or is it?! Meanwhile over his page, Akiyoshi warns: This page contains some works of “anomalous motion illusion”, which might make sensitive observers dizzy or sick. Should you feel dizzy, you had better leave this page immediately (more).
Where’s the illusion, right? Well,… what if I just tell you that no blue at all is used over this picture! No matter how strongly you want to believe you are seeing blue and green spirals here, there is no blue color in this image. There is only green, red and orange. What you think is blue is actually green. Don’t worry, … you are not daltonic. I mean, I’m a little bit but, you could check this out through Paint Shop Pro or Photoshop, if you need an affirmation. Indeed, these are just “Vain speculation undeceived by the senses” (1670’s Scilla’s treatise) .
In fact, Relations here, between different colors (green, red and orange), are more important than each color by itself. Relations plus context are the key (more here over Generative Art, and here over Swarm Intelligence based Pattern Recognition). Through these relations, much probably using Gestalt‘s principles (the German word Gestalt could be translated into “configuration or pattern”), here Akiyoshi manages to emerge us the blue color over our perception. This does not cheat a computer of course, however could cheat our own eyes. In other areas the opposite could also be found. For instance, Humans can easily recognize a car over background trees (segment it, in just tiny lapses of a second), while this natural task could be extremely painful for computers over some cases (here is one example).
Born in Prague (inspired by 1890’s works of Christian von Ehrenfels, Austrian philosopher), then later absorbed by a great and tremendous intellectual period occurred from Germany back to Austria (Bauhaus), the Gestalt Laws of Organization have guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. I would say that most certainly some Wertheimer’s gestaltic principles were used in here: Figure and Ground, Similarity, Proximity or Contiguity, Continuity, Closure, Area, and Symmetry (check Gestalt Theory of Visual Perception). We could see this happening also in other areas, … in Music for instance:
[…] Gestalt theory first arose in 1890 as a reaction to the prevalent psychological theory of the time – atomism. Atomism examined parts of things with the idea that these parts could then be put back together to make wholes. Atomists believed the nature of things to be absolute and not dependent on context. Gestalt theorists, on the other hand, were intrigued by the way our mind perceives wholes out of incomplete elements [1, 2]. “To the Gestaltists, things are affected by where they are and by what surrounds them…so that things are better described as “more than the sum of their parts.” [1, p. 49]. Gestaltists believed that context was very important in perception. An essay by Christian von Ehrenfels discussed this belief using a musical example. Take a 12 note melody. Play it in one key, say the key of C. Now change to another key, say the key of A flat. There might not be any notes the same in the two songs, yet a person listening to it knows that it is the same tune. It is the relationships between the notes that give us the tune, the whole, not which notes make up the tune. […], from “Gestalt Principles of Perception“, Bonnie Skaalid, Univ. of Saskatchewan, Canada, 1999.
Care for an contemporary example? Well, … the first thing that comes to my mind is DUB music genre. In fact, I do have several albums from different musicians over my house. Dub music evolved in Jamaica (1968) from early rastafarian instrumental reggae music and versions that incorporated fairly primitive reverbs and echo sound effects, being found by accident (engineer Byron Smith left the vocal track out by accident). Over decades, it inspired immense groups of musicians from well-known bands such as The Police, The Clash, UB40 up to reputed musicians such as Bill Laswell. Of course !, it was not far from what John Cage have made for the solo piano Music of Changes, to determine which notes should be used and when they should sound. In the fifty’s, Cage start it to use the mechanism of the I Ching (Chinese “Book of Changes”) in the composition of his music in order to provide a framework for his uses of chance.
Other most recent bands include, Leftfield, Massive Attack, Bauhaus, The Beastie Boys, Asian Dub Foundation, Underworld, Thievery Corporation, Gorillaz, Kruder & Dorfmeister, and DJ Spooky. But what is then so special about Dub? Well, one of this genre’s most striking features is the fact that some if not all musical sentences are incomplete. Those special sentences (Gestaltic, let me add), are normally followed by a pause. The most amazing thing however, is that us, Humans could perceive the entire sentence being formed on the back of our minds! So the music is not there, and at the same time, we are listening to two adjacent simultaneous melodies, as we were a composer. By just using relations among a few notes, we soon start to emerge a perception for the whole sentence, as if they were self-organizing! Being it extremely rhythmic, this often could lead us to a sweet soft state of overwhelming emotion, or exalted organic feel to the music .
As you will probably know by now, the same could happen over misplaced letters over an entire phrase. Even if some letters are not at their right proper place, at each word, we could still perceive the whole sentence meaning. Up to your gestaltic neurons to decipher.
Next time you go to a rave party (I never did, neither pretend to), do think about the title of this post, the figure above, as well as on all those great past musicians, along with – unfortunately – awkward current DJ’s, who pass on for hours strident music mixes without knowing at all what Gestalt is all about! Oh, … by the way, should you feel extremely dizzy, do follow Akiyoshi’s advice: If you start feeling unwell when using this website (rave party), immediately cover one eye with your hand and then leave the page (leave the party). Do not close your both eyes because that can make the attack worse!
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)
Back in 2003 I was photographed by João Bracourt, a friend and professional photograph which among other things (web design + painting) travels around the world within big professional surf events (he is right now on it’s way to Indonesia), covering it for main surf magazines. Back then (Sept. 2003) we were enjoying ourselves with a big group late nigth at Bairro Alto, the main bar and restaurant district in Lisbon.
The t-shirt I’m wearing here is from COSI – Complexity in Social Sciences Summer School. One month earlier have been invited among other people to give a lecture in Spain about my work, there at COSI (Baeza, Andaluzia). After all these years the PPT file (Stigmergy as a possible exploratory walk up to collective life-like complexity and behaviour) is still available. As well as those from Gerard Weisbuch (Research Director of the Complex Networks and Cognitive Systems Team within the Statistical Physics Laboratory of the l’Ecole Normale Supérieure in Paris, France) and Rosaria Conte (head of the Division of Artificial Intelligence, Cognitive Modelling & Interaction at the Institute of Psychology of the Italian National Research Council), among others. Many other research materials concerning complexity and social sciences are still available at COSI’s 2003 main site.
(at Bairro Alto, Lisbon, Sept. 2003 – taken by João Bracourt)
(at Bairro Alto, Lisbon, Sept. 2003 – taken by João Bracourt)
Figure – A sequential clustering task of corpses performed by a real ant colony. In here 1500 corpses are randomly located in a circular arena with radius = 25 cm, where Messor Sancta workers are present. The figure shows the initial state (above), 2 hours, 6 hours and 26 hours (below) after the beginning of the experiment (from: Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999).
The following research paper exploits precisely this phenomena into digital data.
[] 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 Köppen (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.
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.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Transition behavior of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images (face of Einstein and a Map). Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels [in, V. Ramos, F. Almeida, “Artificial Ant Colonies in Digital Image Habitats – a mass behavior effect study on Pattern Recognition“, ANTS’00 Conf., Brussels, Belgium, 2000].
After “Einstein face” is injected as a substrate at t=0, 100 iterations occur. At this point you could recognize the face. Then, a new substrate (a new “environmental condition”) is imposed (Map image). The colony then adapts quickly to this new situation, losing their collective memory of past contours.
In white, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trough out the trails). It’s exactly this artificial evaporation and the computational ant collective group synergy reallocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and “perception” of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the successive alteration that they found on the Habitat. If you however, inject Einstein image again as a substrate, the whole ant society will converge again to it, but much faster than the first time, due to the residual memory distributed in the environment.
As a whole, the system is constantly trying to establish a proper compromise between memory (past solutions – via pheromone reinforcement) and novel ones in order to adapt (new conditions on the habitat, through pheromone evaporation). The right compromise, ables the system to tackle two contradictory situations: keeping some memory while learning something radically new. Antagonist features such as exploration and exploitation are tackled this way.
Image Classification of Shellfish Larvae Digital Images using Swarm Intelligence. On the left a compendium of 9 raw images (out of 20 samples) used in the present project. Respective segmented images on the rigth.
[] Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India, 16-18 Dec., 2005.
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).
(to obtain the respective PDF file follow link above or visit chemoton.org)
[] 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 Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.
Figure - Transition behaviour of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images. Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels. At the second row, "Kafka" image is replaced as a substrate, by "Red Ant". In black, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trought out the trails). It’s exactly this artificial evaporation and the computational ant collective group sinergy realocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and "perception" of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the sucessive alteration that they found on the Habitat.
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.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Chemoton § Vitorino Ramos' research notebook 
Recent Comments