Dynamic Color Recognition for the Aibo

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Dynamic Color Recognition for the Aibo

• Nick Lahens
• David Puehn

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What we did

• Our goal was to develop the AIBOs ability to
  learn and recognize color
• We investigated Tekkotsus built-in vision
  algorithms and classes
• Using these vision classes we added functionality
  to allow the AIBO to learn colors dynamically

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AIBO Vision

• AIBO sees color in YUV
• YUV defines the luminance and chrominance of a
  color
• Tekkotsu allows us to interface with the AIBOs
  vision by sending us events from the raw camera
• These events provide us with image data such as
  pixel values, image resolution and layers

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Why?

• Robot vision is extremely sensitive to changes in
  the environment
• Color values in the real world are different from
  those in ideal lab conditions
• For example, a robot might see the color blue
  differently depending upon the time of day. This
  doesnt even include problems arising from the
  attributes of the colors physical surface, such
  as reflectivity.
• To counter this problem, we sought to develop
  code capable of re-teaching AIBO colors in new
  lighting conditions

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The ColorRecog Architecture

• Consists of two main components
• Color-Learning
• Color-Recognition
• These two components communicate through Color
  structs containing
• The colors YUV values
• A unique ID
• A threshold

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Perceiving Colors

• We begin by capturing an image from the AIBOs
  camera
• Next, we calculate the average Y, U and V values
  by iterating through each pixel in the image
• Finally, we return the average color of the image

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PerceiveColor Algorithm

• foreach pixel in image.Y_channel
• sumY pixel.value
• avgY sumY / numPixels
• foreach pixel in image.U_channel
• sumU pixel.value
• avgU sumU / numPixels
• foreach pixel in image.V_channel
• sumV pixel.value
• avgV sumV / numPixels
• color.Y avgY
• color.U avgU
• color.V avgV
• return color

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Learning Colors

• First, we perceive the current color viewed by
  the AIBO
• Requires the Aibos view be filled completely
  with the desired color for the best results.
• Next, we assign the color a unique ID
• Used to differentiate between different color
  structs
• Finally, we add the color to a collection of
  learned colors
• If the color already exists, update its values

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Recognition of Colors

• We begin again by perceiving the current color
  viewed by the AIBO
• Then we compare this color to each color we have
  learned thus far
• The threshold property provides a level of
  tolerance for slight environmental variations
• If we have a match, we return the colors ID.
• Else, we return the No-Color-Found ID

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Demo
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Learning and Recognition Stimulus
Recognize color
Learn Yellow
Learn Blue
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Conclusion

• Using ColorRecog allows the AIBO to effectively
  adapt to the ever changing visual environment
• Dynamic color recognition allows someone to teach
  a robot colors without having to modify any code

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A Better Approach

• Calculate the average value from the largest
  color segment
• Allow Aibo to perceive and learn colors in
  situations where view is not completely saturated
  with a single color
• Only compare U and V values since they are the
  real determiners of color while luminance is
  brightness

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Learning Colors - Usage

• Activate the ColorBehavior
• Place an object such that it takes up the Aibos
  entire field of vision
• Give the Aibo a stimulus mapped to the desired
  color
• Behavior adds colors name to struct and stores
  it in color array

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Perceiving Colors

• PerceiveColor retrieves representation of Aibos
  vision as three, 2-dimm arrays
• Each array represents a different vision channel
  (Y, U or V)
• Each element is a pixels value for the given
  vision channel
• Mean values for all pixels in each channel are
  calculated
• Yields average YUV values for entire image
• Returns Color struct containing the YUV values