BurningbirdHow do you make i86 Applesauce? You take an Apple, carve out the PowerPC core, replacing it with Intel sugar and a hint of cinnamon and nutmeg. Then you hold a press conference, throw the Apple to the developers, buyers, and vendors, and let their agitation smash it all to hell.
What’s left is a tasty, albeit lumpy, confection.
Serve in a futurist, white china dish, with plenty of bubbly on the side. Invite Dell and Gateway over, ask if they’d like a taste.
I’ve been incorporating the semantic data my application gathers into weblog posts. You can see it in operation over at Burningbird, in the individual posts (see references example below and the photo example).
During this, I ran into a wall on the topic of tags. I wanted to record tag-like information as RDF statements, but then I realized that I don’t necessarily know what tags are.
According to de.licio.us and furl, tags are ways of publishing bookmarks to a broader audience, in addition to categorizing your links. Since the sites are for bookmarking, adding links to your own work is frowned on.
In Flickr, tags are ways of categorizing your work, pure and simple. You may specifically use certain tags to participate in a community, but the majority of use is to classify one’s own work.
In Technorati, though, which is the one I’m most closely examining, a tag is a way of classifying your work for some purpose. According to the Technorati Tag instructions, you can link it to a Technorati page, but you can also link it to a Wikipedia or other page. However, according to the Technorati Wiki a tag is meant to reference a page that will aggregate the results (this is a wiki, note that text just quoted is subject to change). And therein lies the confusion about ‘purpose’.
If tags used in the sense that Technorati uses them are meant to help aggregate content actively, then yes, there needs to be specific pages and/or sites for the target URI–ones that actively gather and than republish incoming links.
However, if tags are meant to be more passively consumed, with bots going out and gathering the information, than as long as an agreed on format is used, any page can be linked (well, as long as you don’t link the same URL twice in the same document — Technorati sees this as spam).
I can’t map ‘tag’ into the semantic webspace using RDF if I can’t find a common meaning between all these distinct uses of the concept of ‘tag’. I spent time last night with this, and again this morning, but nothing fits.
I noticed that Norm Walsh used the relMeta wiki page as a namespace for a tiny self-contained schema reflecting ‘tag’ he uses in his taxonomy. That’s an option, I guess. But then, does that mean the Technorati namespaced schema doesn’t apply to de.licio.us, furl, and Flickr?
Pat Patterson used the Planet software to create a new aggregation of feeds from weblogs, this one surrounding the topic of ‘identity’ and called Planet Identity.
As with Planet RDF, these groupings help us keep up with what’s happening within the specific community of interest.
Interesting that Microsoft’s InfoCards has not had much pushback. Julian Bond is about the only person I’ve read, so far, who seems adamantly against it.
I’m reaching a burn out point in trying to enhance and support Wordform (and get it ready for a release) and do enough work to pay the bills. At this time, I’m working 15+ hours a day, and it’s taking a toll.
What I’ll most likely do is release bits of the tool as extensions to WordPress. That way more people can use the functionality, and I’ll be able to focus on specific pieces of development.
If things lighten up, and I feel comfortable I have enough time to provide decent support, I’ll release the application.
One of the many advantages of digital photography is that you can see what a photo looks like immediately. What happens to that great scenic when flattened out into two dimensions? And is that butterfly lost against the floral background? With this, you still have a chance to re-take the photo, if needed.
Still, there’s always room for the odd and even serendipitous image…
These images, as with most I show in my pages, reflect more or less the original digital image. I’ll usually crop the shots, and I may adjust the shadows and highlights if the picture is too contrasty. Or if there’s dust on the lens, I’ll use the fill tool in Photoshop to ‘erase’ it. In addition, I almost always sharpen an image after making a compressed version of the picture, and always before making a print.
I’m trying out a new Photoshop plugin set, Reindeer’s Optipix, which is said to have superior edge detection and sharpening. When I opened the edge enhancing filter, I was given options to set noise removing radius, and then edge sharpening radius before picking the amount of emphasis. Curious about the impact of these adjustments, I searched around for more detail. In one forum entry about the plugin, the reviewer mentioned that rather than an unsharp mask, which is a Laplacian of the Gaussian, the Optipix filter takes a Difference of Gaussians to enhance edges.
What a blast from the past this was. I remember studying about the Laplacian of the Gaussian (LoG) and Difference of Gaussians (DoG) when I did a college paper on David Marr’s landmark book on computational models of visual neurophysiology, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. This book had such a profound impact on me that when I graduated from college, I wanted to work with computer vision (in addition to computational linguistics). Of course, when one only has a BS rather than a PhD, one goes to work with databases and FORTRAN rather than computational models of vision.
Still, I have my copy of the book today, and I think I even have that old paper somewhere. I remember when I wrote it out, on an old typerwriter, I had to hand letter in the formulae, and hand draw in the diagrams. My, haven’t we come far? Of course back then, I still had a fair hand and my writing was legible. Now, if it weren’t for my graphics tablet and pen, I’m not sure I’d remember how to hold a pencil.
To return to Vision, in his book, Marr explored various aspects of vision and with each discussed the physiology behind the act, modeling it mathematically, and then deriving a computational representation.
Consider edge detection. Our eyes have ganglion cells that respond differently to the presence or absence of light. ‘On’ centered cells respond when light is first introduced, and continue to fire as long as the light is maintained. Other ‘off’ centered cells respond only when the light is removed. Both actions are necessary to detect an ‘edge’ , which is really nothing more than a light area next to a dark or darker area–a difference of intensity of light.
(Think about tearing open a bag: one hand moves toward you, the other hand moves away and the action results in an effect–the bag opens, the aroma of potato chips fills the air. If both hands moved in the same direction, you’d starve.)
So how does a computer detect an edge? After all it doesn’t have cells.
Well, according to Marr and others of the time, one way to detect edges is to blur the image, and then subtract this from the original in order to determine the changes of intensity or zero crossings; leaving what Marr termed the zero-crossing segments–edges whose slope denotes the level of contrast of a an edge. Computationally, this is equivalent to taking a transform of the aforementioned Difference of Gaussians , and if you view a graph of this equation, it actually physically approximates how one would think of an edge if one imagined it one dimensionally–two steep hills with a deep divide between them.
For someone who alternated between love of mathematics, and terror of the same, Marr’s book was the first time I’d seen a real, rather than ‘accepted’ correlation between complex mathematics and the physical world. What made it ironic is that I didn’t meet this epiphany while studying physics or computer science. No, I was studying about the human visual system in the process of getting a degree in psychology
(A zero-crossing drawing, generated by Photoshop.)
Thirty years ago Marr envisioned a time when computers could see and wrote a book called Vision, published after his death of leukemia in 1980 at the age of 35. Five years later, I sat under a tree on campus making notes, and stopping from time to time to just stare at the bark, the birds against the sky, and the shadows the buildings made–crude hand drawings of which would make their way into my report on his research.
Five years later, in 1990, while I wrestled large databases for Boeing, a small company released a product called Photoshop that would incorporate work of Marr and others.
Fifteen years later, I sit with a thin, titanium computer on my lap, Marr’s book turned upside down on the chair’s arm, while I try a plugin downloaded over the Internet that does what I only dreamed could be possible twenty years ago.