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Computer Vision Techniques forInteractive ArtUsing Processing and Max/JitterInteractive Art Doctoral Programhttp://www.artes.ucp.pt/si/doutoramento/index.html28-05-2011Jorge Cardoso1
TopicsTechniquesFramedifferencing(for motiondetection)Color detection(whereintheimage, does a given color appear – can beused for trackingobjects)Brightnessdetection(whatisthepositionofthebrightestpixels)Background subtraction(for objectsegmentation) Blobextraction(applied to theprevioustechniques to extractshapeparametersandvectorizingsilhuettes)Movementestimation (whatdirection are theobjectsmoving)Face detection(isthere a face intheimage? where?)28-05-2011Jorge Cardoso2
HowGeneral overviewofhowthose CV techniquesworkExamplesofwhat can beaccomplishedwiththetechniquesVídeosLive demosCode examples of these techniques in Processing and JitterSome – very simple(istic) – examples of how they can be used for interactive work28-05-2011Jorge Cardoso3
Computer Vision for InteractionUse a computer and camera to extract information about what people are doing and use that as an implicit or explicit form of interaction“Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory for building artificial systems that obtain information from images” – Computer vision. (2009, April 24). In Wikipedia, The Free Encyclopedia. Retrieved 11:53, May 9, 2009, from http://en.wikipedia.org/w/index.php?title=Computer_vision&oldid=285848177 28-05-2011Jorge Cardoso4
28-05-2011Jorge Cardoso5ImageComputer vision are processes applied to sequences of imagesAn image is just a sequence of number (usually arranged in a matrix form)Eachnumberrepresents a colour50 44 23 31 38 52 75 5229 09 15 08 38 98 53 5208 07 12 15 24 30 51 5210 31 14 38 32 36 53 6714 33 38 45 53 70 69 4036 44 58 63 47 53 35 2668 76 74 76 55 47 38 3569 68 63 74 50 42 35 32
ImageEach pixel (i.e., number in the matrix) isusuallystoredin RGB or ARGB formatsoIt can be decomposed:28-05-2011Jorge Cardoso6or
Frame DifferencingIf we subtract two consecutive video frames, threshold and binarize it, we get a rough view of the movement in the videoProcedure:Go through the current frame image, pixel by pixelCalculate the distance between the color of the current frame’s pixel and the previous frame’s pixelIf distance is smaller than a given threshold, assume no movement (black), otherwise assume movement (white)28-05-2011Jorge Cardoso7
Frame DifferencingColor differenceQ: How do wecalculatethe “distance”betweentwocolors?A: Simpleversion: euclideandistance.Take a color as a pointin 3D space (RGB -> XYZ)CalculatethedistancebetweenthetwopointsSubtracteachcomponent (dR = R1-R2, dG = G1-G2, dB = B1-B2)Dist = Sqrt(dR*dR + dG*dG + dB*dB)A1: Slightly (perceptively) betterversion: weighteachcomponentdifferently:Dist = Sqrt(2*dR*dR + 4*dG*dG + 3*dB*dB)28-05-2011Jorge Cardoso8
Frame DifferencingQ: What’sitgood for?A:KALOETHOGRAPHIA (2010) Pedro Cascalheira http://artes.ucp.pt/blogs/index.php/vai/2011/02/14/alunos-2009-2010aB (2011) Jorge Coutinhohttps://picasaweb.google.com/lh/photo/x9bGyxFsTLVFbkk9kD3xLA?feat=directlinkWebCamPiano MemoAkten[video]28-05-2011Jorge Cardoso9
Frame Differencing - CodeProcessingCodeFrameDifferencingFrameDifferencingWithMotionDetectionFrameDifferencingForAirPianoJitterCodeFrameDifferencing.maxpatFrameDifferencingWithMotionDetection.maxpat28-05-2011Jorge Cardoso10
Color detectionIsolate color regions in an imageProcedure:Go through the image, pixel by pixelCalculate the distance between the color of the current pixel and the reference colorIf distance is smaller than a given threshold, keep the pixel28-05-2011Jorge Cardoso11
Color DetectionColor DifferenceFor a betterapproachthan RGB distance,use HSBGive more weight to theHueDist = Sqrt(3*dH*dH + dS*dS + 2*dB*dB)28-05-2011Jorge Cardoso12
Color DetectionQ: What’sitgood for?A: Catchoftheday [video]PlayDohAsInterfaceKeyboard[video]  28-05-2011Jorge Cardoso13
Color Detection - CodeProcessingCodeColorDifferenceColorDifferenceSequencerJitterCodeColorDifferenceWithFindbounds.maxpatColorDifference.maxpat28-05-2011Jorge Cardoso14
Brightenessdetection15Slightvariationon color detectionwhenwe’reonlyinterestedintrackingbrightpixelsFor example, to track a lightProcedure:Same as color detectionbutextractthebrightnessofeach pixel andkeeponlythehighestJorge Cardoso28-05-2011
28-05-2011Jorge Cardoso16Brighteness detectionQ: How do wecalculatethe “brightness” of a color?
A:
In RGB:
(perceptive) Brightness = (0.2126*R) + (0.7152*G) + (0.0722*B)
(physical/energy) Brightness = (R+G+B)/3
…
In HSB:
Brightness = B  
Just use thebrightness() functioninwhateverlanguage…BrightnessdetectionQ: Whynotjust color detection?A: In some situationsyoudon’tcareabout color, justthebrightness (for example to detect a lantern)Brightnessdetectionis more robust (i.e., lessinfluencedbylightingconditions)28-05-2011Jorge Cardoso17
18BrightenessdetectionQ:  What’sitgood for?
A:
DrawingWith Light [video]
BurningtheSound [video]Jorge Cardoso28-05-2011
Brightenessdetection - CodeProcessingCodeBrightnessDetectionJitterCodeBrightnessDetection.maxpat28-05-2011Jorge Cardoso19
Background subtraction20Ifwehave a fixedcamera, and a background image for reference, we can subtractthe background fromeachframe to isolatenewobjects:          This                          -                     This                         =                 ThisJorge Cardoso28-05-2011
Background subtractionQ: How do we “subtract” two images?A: Pixel by pixelQ: How do we “subtract” two pixels?A: Channel by channel (color component by color component, usually R, G and B)28-05-2011Jorge Cardoso21
Background subtractionPixel subtraction alone doesn’t work very well due to noise or shadows in the imageThe resulting subtraction must be thresholded to eliminate noise28-05-2011Jorge Cardoso22
Background subtractionAfter subtracting and thresholding, we can binarize the image to get a mask28-05-2011Jorge Cardoso23
Background subtractionQ: What’sitgood for?A: Isolatingobjectsin a scenewhenyoudon’thavemuchcontroloverthe background. Background SubtractionProcessingDemo [video]28-05-2011Jorge Cardoso24
Background subtraction - CodeProcessingcode:BackgroundSubtractionJittercodeBackgroundSubraction.maxpat28-05-2011Jorge Cardoso25
Blob extractionAll the previous techniques result in possibly many white areas distributed in the image28-05-2011Jorge Cardoso26http://en.wikipedia.org/wiki/Connected_Component_Labeling
Blob extractionBlob extraction (blob detection, connected component labeling) identifies those regions and and allows the extractions of some features area, perimeter, orientation, bounding box, Silhouette28-05-2011Jorge Cardoso27
Blob extractionProcedure:See http://en.wikipedia.org/wiki/Connected_Component_Labeling28-05-2011Jorge Cardoso28
Blob extractionQ:  What’sitgood for?A:Loliness (2007) Telmo Rocha [video]Células (2009) Ana Lima [video]SilhouetteCollisions [video]28-05-2011Jorge Cardoso29
Blob extraction - Code28-05-2011Jorge Cardoso30Processing libraries

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