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_id	e071
authors	Ward, G. and Shakespeare, R.
year	1998
title	Rendering with Radiance: The Art and Science of Lighting Visualization
source	Morgan Kaufman
summary	Radiance is a collection of approximately 50 programs that do everything from object modeling to point calculation, rendering, image processing and display. This is the definitive reference on the radiance lighting simulation and rendering system.
series	other
last changed	2003/04/23 15:14

_id	ga0026
id	ga0026
authors	Ransen, Owen F.
year	2000
title	Possible Futures in Computer Art Generation
source	International Conference on Generative Art
summary	Years of trying to create an "Image Idea Generator" program have convinced me that the perfect solution would be to have an artificial artistic person, a design slave. This paper describes how I came to that conclusion, realistic alternatives, and briefly, how it could possibly happen. 1. The history of Repligator and Gliftic 1.1 Repligator In 1996 I had the idea of creating an “image idea generator”. I wanted something which would create images out of nothing, but guided by the user. The biggest conceptual problem I had was “out of nothing”. What does that mean? So I put aside that problem and forced the user to give the program a starting image. This program eventually turned into Repligator, commercially described as an “easy to use graphical effects program”, but actually, to my mind, an Image Idea Generator. The first release came out in October 1997. In December 1998 I described Repligator V4 [1] and how I thought it could be developed away from simply being an effects program. In July 1999 Repligator V4 won the Shareware Industry Awards Foundation prize for "Best Graphics Program of 1999". Prize winners are never told why they won, but I am sure that it was because of two things: 1) Easy of use 2) Ease of experimentation "Ease of experimentation" means that Repligator does in fact come up with new graphics ideas. Once you have input your original image you can generate new versions of that image simply by pushing a single key. Repligator is currently at version 6, but, apart from adding many new effects and a few new features, is basically the same program as version 4. Following on from the ideas in [1] I started to develop Gliftic, which is closer to my original thoughts of an image idea generator which "starts from nothing". The Gliftic model of images was that they are composed of three components: 1. Layout or form, for example the outline of a mandala is a form. 2. Color scheme, for example colors selected from autumn leaves from an oak tree. 3. Interpretation, for example Van Gogh would paint a mandala with oak tree colors in a different way to Andy Warhol. There is a Van Gogh interpretation and an Andy Warhol interpretation. Further I wanted to be able to genetically breed images, for example crossing two layouts to produce a child layout. And the same with interpretations and color schemes. If I could achieve this then the program would be very powerful. 1.2 Getting to Gliftic Programming has an amazing way of crystalising ideas. If you want to put an idea into practice via a computer program you really have to understand the idea not only globally, but just as importantly, in detail. You have to make hard design decisions, there can be no vagueness, and so implementing what I had decribed above turned out to be a considerable challenge. I soon found out that the hardest thing to do would be the breeding of forms. What are the "genes" of a form? What are the genes of a circle, say, and how do they compare to the genes of the outline of the UK? I wanted the genotype representation (inside the computer program's data) to be directly linked to the phenotype representation (on the computer screen). This seemed to be the best way of making sure that bred-forms would bare some visual relationship to their parents. I also wanted symmetry to be preserved. For example if two symmetrical objects were bred then their children should be symmetrical. I decided to represent shapes as simply closed polygonal shapes, and the "genes" of these shapes were simply the list of points defining the polygon. Thus a circle would have to be represented by a regular polygon of, say, 100 sides. The outline of the UK could easily be represented as a list of points every 10 Kilometers along the coast line. Now for the important question: what do you get when you cross a circle with the outline of the UK? I tried various ways of combining the "genes" (i.e. coordinates) of the shapes, but none of them really ended up producing interesting shapes. And of the methods I used, many of them, applied over several "generations" simply resulted in amorphous blobs, with no distinct family characteristics. Or rather maybe I should say that no single method of breeding shapes gave decent results for all types of images. Figure 1 shows an example of breeding a mandala with 6 regular polygons: Figure 1 Mandala bred with array of regular polygons I did not try out all my ideas, and maybe in the future I will return to the problem, but it was clear to me that it is a non-trivial problem. And if the breeding of shapes is a non-trivial problem, then what about the breeding of interpretations? I abandoned the genetic (breeding) model of generating designs but retained the idea of the three components (form, color scheme, interpretation). 1.3 Gliftic today Gliftic Version 1.0 was released in May 2000. It allows the user to change a form, a color scheme and an interpretation. The user can experiment with combining different components together and can thus home in on an personally pleasing image. Just as in Repligator, pushing the F7 key make the program choose all the options. Unlike Repligator however the user can also easily experiment with the form (only) by pushing F4, the color scheme (only) by pushing F5 and the interpretation (only) by pushing F6. Figures 2, 3 and 4 show some example images created by Gliftic. Figure 2 Mandala interpreted with arabesques Figure 3 Trellis interpreted with "graphic ivy" Figure 4 Regular dots interpreted as "sparks" 1.4 Forms in Gliftic V1 Forms are simply collections of graphics primitives (points, lines, ellipses and polygons). The program generates these collections according to the user's instructions. Currently the forms are: Mandala, Regular Polygon, Random Dots, Random Sticks, Random Shapes, Grid Of Polygons, Trellis, Flying Leap, Sticks And Waves, Spoked Wheel, Biological Growth, Chequer Squares, Regular Dots, Single Line, Paisley, Random Circles, Chevrons. 1.5 Color Schemes in Gliftic V1 When combining a form with an interpretation (described later) the program needs to know what colors it can use. The range of colors is called a color scheme. Gliftic has three color scheme types: 1. Random colors: Colors for the various parts of the image are chosen purely at random. 2. Hue Saturation Value (HSV) colors: The user can choose the main hue (e.g. red or yellow), the saturation (purity) of the color scheme and the value (brightness/darkness) . The user also has to choose how much variation is allowed in the color scheme. A wide variation allows the various colors of the final image to depart a long way from the HSV settings. A smaller variation results in the final image using almost a single color. 3. Colors chosen from an image: The user can choose an image (for example a JPG file of a famous painting, or a digital photograph he took while on holiday in Greece) and Gliftic will select colors from that image. Only colors from the selected image will appear in the output image. 1.6 Interpretations in Gliftic V1 Interpretation in Gliftic is best decribed with a few examples. A pure geometric line could be interpreted as: 1) the branch of a tree 2) a long thin arabesque 3) a sequence of disks 4) a chain, 5) a row of diamonds. An pure geometric ellipse could be interpreted as 1) a lake, 2) a planet, 3) an eye. Gliftic V1 has the following interpretations: Standard, Circles, Flying Leap, Graphic Ivy, Diamond Bar, Sparkz, Ess Disk, Ribbons, George Haite, Arabesque, ZigZag. 1.7 Applications of Gliftic Currently Gliftic is mostly used for creating WEB graphics, often backgrounds as it has an option to enable "tiling" of the generated images. There is also a possibility that it will be used in the custom textile business sometime within the next year or two. The real application of Gliftic is that of generating new graphics ideas, and I suspect that, like Repligator, many users will only understand this later. 2. The future of Gliftic, 3 possibilties Completing Gliftic V1 gave me the experience to understand what problems and opportunities there will be in future development of the program. Here I divide my many ideas into three oversimplified possibilities, and the real result may be a mix of two or all three of them. 2.1 Continue the current development "linearly" Gliftic could grow simply by the addition of more forms and interpretations. In fact I am sure that initially it will grow like this. However this limits the possibilities to what is inside the program itself. These limits can be mitigated by allowing the user to add forms (as vector files). The user can already add color schemes (as images). The biggest problem with leaving the program in its current state is that there is no easy way to add interpretations. 2.2 Allow the artist to program Gliftic It would be interesting to add a language to Gliftic which allows the user to program his own form generators and interpreters. In this way Gliftic becomes a "platform" for the development of dynamic graphics styles by the artist. The advantage of not having to deal with the complexities of Windows programming could attract the more adventurous artists and designers. The choice of programming language of course needs to take into account the fact that the "programmer" is probably not be an expert computer scientist. I have seen how LISP (an not exactly easy artificial intelligence language) has become very popular among non programming users of AutoCAD. If, to complete a job which you do manually and repeatedly, you can write a LISP macro of only 5 lines, then you may be tempted to learn enough LISP to write those 5 lines. Imagine also the ability to publish (and/or sell) "style generators". An artist could develop a particular interpretation function, it creates images of a given character which others find appealing. The interpretation (which runs inside Gliftic as a routine) could be offered to interior designers (for example) to unify carpets, wallpaper, furniture coverings for single projects. As Adrian Ward [3] says on his WEB site: "Programming is no less an artform than painting is a technical process." Learning a computer language to create a single image is overkill and impractical. Learning a computer language to create your own artistic style which generates an infinite series of images in that style may well be attractive. 2.3 Add an artificial conciousness to Gliftic This is a wild science fiction idea which comes into my head regularly. Gliftic manages to surprise the users with the images it makes, but, currently, is limited by what gets programmed into it or by pure chance. How about adding a real artifical conciousness to the program? Creating an intelligent artificial designer? According to Igor Aleksander [1] conciousness is required for programs (computers) to really become usefully intelligent. Aleksander thinks that "the line has been drawn under the philosophical discussion of conciousness, and the way is open to sound scientific investigation". Without going into the details, and with great over-simplification, there are roughly two sorts of artificial intelligence: 1) Programmed intelligence, where, to all intents and purposes, the programmer is the "intelligence". The program may perform well (but often, in practice, doesn't) and any learning which is done is simply statistical and pre-programmed. There is no way that this type of program could become concious. 2) Neural network intelligence, where the programs are based roughly on a simple model of the brain, and the network learns how to do specific tasks. It is this sort of program which, according to Aleksander, could, in the future, become concious, and thus usefully intelligent. What could the advantages of an artificial artist be? 1) There would be no need for programming. Presumbably the human artist would dialog with the artificial artist, directing its development. 2) The artificial artist could be used as an apprentice, doing the "drudge" work of art, which needs intelligence, but is, anyway, monotonous for the human artist. 3) The human artist imagines "concepts", the artificial artist makes them concrete. 4) An concious artificial artist may come up with ideas of its own. Is this science fiction? Arthur C. Clarke's 1st Law: "If a famous scientist says that something can be done, then he is in all probability correct. If a famous scientist says that something cannot be done, then he is in all probability wrong". Arthur C Clarke's 2nd Law: "Only by trying to go beyond the current limits can you find out what the real limits are." One of Bertrand Russell's 10 commandments: "Do not fear to be eccentric in opinion, for every opinion now accepted was once eccentric" 3. References 1. "From Ramon Llull to Image Idea Generation". Ransen, Owen. Proceedings of the 1998 Milan First International Conference on Generative Art. 2. "How To Build A Mind" Aleksander, Igor. Wiedenfeld and Nicolson, 1999 3. "How I Drew One of My Pictures: or, The Authorship of Generative Art" by Adrian Ward and Geof Cox. Proceedings of the 1999 Milan 2nd International Conference on Generative Art.
series	other
email
more	http://www.generativeart.com/
last changed	2003/08/07 17:25

_id	50a1
authors	Hoffman, Donald
year	1998
title	Visual Intelligence
source	Norton Publishing, New York
summary	After his stroke, Mr. P still had outstanding memory and intelligence. He could still read and talk, and mixed well with the other patients on his ward. His vision was in most respects normal---with one notable exception: He couldn't recognize the faces of people or animals. As he put it himself, "I can see the eyes, nose, and mouth quite clearly, but they just don't add up. They all seem chalked in, like on a blackboard ... I have to tell by the clothes or by the voice whether it is a man or a woman ...The hair may help a lot, or if there is a mustache ... ." Even his own face, seen in a mirror, looked to him strange and unfamiliar. Mr. P had lost a critical aspect of his visual intelligence. We have long known about IQ and rational intelligence. And, due in part to recent advances in neuroscience and psychology, we have begun to appreciate the importance of emotional intelligence. But we are largely ignorant that there is even such a thing as visual intelligence---that is, until it is severely impaired, as in the case of Mr. P, by a stroke or other insult to visual cortex. The culprit in our ignorance is visual intelligence itself. Vision is normally so swift and sure, so dependable and informative, and apparently so effortless that we naturally assume that it is, indeed, effortless. But the swift ease of vision, like the graceful ease of an Olympic ice skater, is deceptive. Behind the graceful ease of the skater are years of rigorous training, and behind the swift ease of vision is an intelligence so great that it occupies nearly half of the brain's cortex. Our visual intelligence richly interacts with, and in many cases precedes and drives, our rational and emotional intelligence. To understand visual intelligence is to understand, in large part, who we are. It is also to understand much about our highly visual culture in which, as the saying goes, image is everything. Consider, for instance, our entertainment. Visual effects lure us into theaters, and propel films like Star Wars and Jurassic Park to record sales. Music videos usher us before surreal visual worlds, and spawn TV stations like MTV and VH-1. Video games swallow kids (and adults) for hours on end, and swell the bottom lines of companies like Sega and Nintendo. Virtual reality, popularized in movies like Disclosure and Lawnmower Man, can immerse us in visual worlds of unprecedented realism, and promises to transform not only entertainment but also architecture, education, manufacturing, and medicine. As a culture we vote with our time and wallets and, in the case of entertainment, our vote is clear. Just as we enjoy rich literature that stimulates our rational intelligence, or a moving story that engages our emotional intelligence, so we also seek out and enjoy new media that challenge our visual intelligence. Or consider marketing and advertisement, which daily manipulate our buying habits with sophisticated images. Corporations spend millions each year on billboards, packaging, magazine ads, and television commercials. Their images can so powerfully influence our behavior that they sometimes generate controversy---witness the uproar over Joe Camel. If you're out to sell something, understanding visual intelligence is, without question, critical to the design of effective visual marketing. And if you're out to buy something, understanding visual intelligence can help clue you in to what is being done to you as a consumer, and how it's being done. This book is a highly illustrated and accessible introduction to visual intelligence, informed by the latest breakthroughs in vision research. Perhaps the most surprising insight that has emerged from vision research is this: Vision is not merely a matter of passive perception, it is an intelligent process of active construction. What you see is, invariably, what your visual intelligence constructs. Just as scientists intelligently construct useful theories based on experimental evidence, so vision intelligently constructs useful visual worlds based on images at the eyes. The main difference is that the constructions of scientists are done consciously, but those of vision are done, for the most part, unconsciously.
series	other
last changed	2003/04/23 15:14

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