Download - AIBO VISION
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AIBO VISION
by
Utku TatlıdedeKemal Kaplan
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AIBO ROBOT
• Specifications:– ERS-210– CPU clock speed of 192MHZ – 20 degrees of freedom– Temperature,Infrared Distance,
Acceleration, Pressure, Vibration Sensors
– CMOS Image Sensor– Miniature Microphones,
Miniature Speaker, LCD Display
• Dimensions and Weight:
– Size (WxHxL) 6.06" (W) x 10.47" (H) x 10.79" (L)
– Weight 3.3 lbs. (1.5kg)
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• CCD camera • 16.8 million colors
(24 bit)• 176x144 pixel image • Field of view 57.6°
wide and 47.8° tall• Up to 25 fps• Stores information in
the YUV color space
AIBO VISION
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• Color Segmentation-Representations-Algorithms
• Region Building and Merging- Region Growing - Edge detection
• Object Recognition- Classification - Template matching - Sanity check
• Bounding Box Creation
PROCESSING OUTLINE
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• CIE-XYZ• RGB• CMY, CMYK• HSV, HSI,
HLS • YIQ• YUV,
YCbCr• LUT
COLOR REPRESENTATION
Color can be physically described as a spectrum which is the intensity of light at each wavelength.
• Radiance: Energy (W) from light source
• Luminance: Energy perceived by observer
• Brightness: Subjective descriptor
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• Additive color space
• Three primaries: red, green, and blue
• Cannot always produce a color equivalent to any wavelength
RGB (Red, Green, Blue)
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• Similar to HSV (Hue, Saturation, Value)
• Represents colors similarly how the human eye senses colors.
HSI (Hue, Saturation, Intensity)
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• Similar to YIQ and YCbCr
• Used for the PAL and SECAM broadcast television system
• Amount of information needed to define a color is greatly reduced
YUV (Luminance, Chrominance)
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CONVERSIONS
Y = .299R + .587G + .114B U = -.147R - .289G + .437B V = .615R - .515G - .100B H = cos-1([(R-B)+(R-G)]/2*[(R-G)2+(R-B)(G-B)]1/2)
S = 1 – 3[min(R,G,B)]/(R+G+B)
V = (R+G+B)/3
RGB TO YUV
RGB TO HSV
• Can we reduce the color space by using unsupervised dimension reduction techniques (like PCA)?
• Can we use different domains?
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• For each object, find the most accurate subspaces of the color space to represent the object.
• YUV seems the most promising color representation for our real time applicaiton.
AIM OF COLOR SEGMENTATION
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First label images, then use supervised pattern recognition techniques. Most common ones:
• C4.5• MLP• KNN
FINDING THE SUBSPACES
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• Forms a decision tree for classification.• Uses the concept of information gain
(effective decrease in entropy).
C4.5
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• The MLP network is suited to a wide range of applications such as pattern classification and recognition, interpolation, prediction and forecasting.
MLP (Multi-Layer Perceptron)
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• KNN is a simple algorithm that stores all available examples and classifies new instances of the example language based on a similarity measure.
KNN (K-Nearest Neighbor)
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• Condensed KNN: We can reduce the training set by removing the samples that introduce no extra information to the system.
CONDENSED KNN
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• PCA is a mathematical procedure that converts a number of possibly correlated variables into a hopefully smaller number of uncorrelated variables called principal components.
PCA (Principal Component Analysis)
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REGION BUILDING AND MERGING
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• RLE encodes multiple appearances of the same value.
RLE (Run Length Encoding)
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REGION GROWING
• This method depends on the satisfactory selection of a number of seed pixels.
• This method may be performed before color segmentation.
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• Merging algorithms: in which neighboring regions are compared and merged if they are similar enough in some features.
• Splitting Algorithms: in which large non-uniform regions are broken up into smaller regions which is hopefully uniform.
REGION MERGING AND SPLITTING
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OBJECT RECOGNITION
BALLBEACON
OPPONENT GOAL
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• Already done by color segmentation.– Ball: The biggest blob with “ball color”,– Beacons: Two adjecentblobs with
beacon colors, etc.
• Unclassified blobs are discarded.• Each object is classified with a
certainty.
CLASSIFICATION
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• Accomplished by using convolution or correlation.
• Only works for translation of the template.
• In case of rotation or size changes, it is ineffective.
• Also fails for partial views of objects.
TEMPLATE MATCHING
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• A series of sanity check inspections are performed by the AIBO vision module to ensure the object classification is logically correct. – Ball cannot be above the goal,– Goals cannot be below the field,– There cannot be two balls, etc.
SANITY CHECK
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• Requires affine transformations (translation, rotation, scaling)
• Required for calculating distance and position information
• The final products of the vision module are the bounding boxes of each visible object.
BOUNDING BOX CREATION
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• Cerberus RoboCup 2002 Team Report• rUNSWift RoboCup 2002 Team Report• NUBots RoboCup 2002 Team Report• CMPack RoboCup 2002 Team Report• MACHINE LEARNING, Mitchell• MACHINE VISION, Jain, Kasturi, Schunk
REFERENCES
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Any Question?
REFERENCES