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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Artificial intelligence software can be stupid as all hell and still qualify as AI. Bumper sticker version:<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>AI need not be intelligent, only artificial.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>I have been having a lot of fun trying to figure out a car-recognition algorithm and I think I have one. It is a memory hog, it is stupid and slow, but at least it’s artificial.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>I have thought of an algorithm for car recognition for which I invite wise counsel from code hipsters and Stanford AI classers. We take 12 pictures of a chariot, every 30 degrees, with front view being defined as the 12 o’clock and the right side view being the 3 o’clock. Then we get a bunch of volunteers to take 12 photos of their ride and collect all. Then we separate them into a collection of 12s, 1s, 2s, etc, so we have 12 files, each with photos of various Detroits taken from a similar angle.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>That’s step 1. Then we take a photo of an arbitrarily chosen auto at an arbitrarily chosen angle. Then, I write an algorithm to recognize the wheels, more specifically the rim elipse. We take the ratio of the vertical apparent diameter to the horizontal (minor diameter) to estimate the angle at which the picture was taken. For instance, if the minor diameter is half the major diameter, then we know it is either a 1, 5, 7 or 11 o’clock photo. <o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Then we look to see if it has either two red or two white features above and to one side, indicating either headlights or tail lights. The white features to the right for instance indicates a 1 o’clock view, two red features to the left of the rear tire indicates it is a 5 o’clock view.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Then once we get an angle, we start comparing the test chariot with the collection of photos donated by volunteers. We JPG-ize both, then take horizontal rasters, looking for high contrast patterns in common. The high contrast areas would be the roof columns perhaps. The software would be simple enough, even I might be able to write it. It *<b>might</b>* be able to pick the one from the collection that is the best match.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Think that would work? <o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Have we any JPG gurus here?<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>spike<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p></div></body></html>