camera phone color appearance utility finding a way to identify color phillip lachman robert prakash...
TRANSCRIPT
Camera Phone Color Appearance UtilityFinding a Way to Identify Color
Phillip Lachman
Robert Prakash
Elston Tochip
Outline
Motivation Goal Methodology
Image Scaling via Edge Detection Color Identification Color Selection & Differentiation
Results Lessons Learned Future Work
Motivation
Phones becoming the portal able digital platform for variety of imaging applications i.e. pictures, video, organizers etc.
Approximately 10 million blind people within the U.S. 55,200 legally blind children 5.5 million elderly individuals
http://www.afb.org/Section.asp?sectionid=15#num
Color blind people within the U.S. ~ 8% of males ~ 0.4% - 2% females
http://www.otal.umd.edu/UUPractice/color/
Goal
To develop a software application that will be able to accomplish the following:1) Receives a camera quality image2) Identifies the predominant color(s)
regions within the image3) Estimates the color name for the
predominant region4) Audibly transmits the predominant color
to the user
Locating the Target
General Guidelines and Suggestions Use a White Card
Provides a white color to baseline lighting conditions Required for computing color of target Suggested by fellow classmates and Bob Dougherty
Detecting the White Card Use an Edge Detection Algorithm
Many Image Processing Edge detection methods available Identify edges by computing changes in gradient around pixels.
Chose Canny Edge Detection algorithm Fundamentally easy to understand and implement
Edge Detection
Finding the Target: Discrimination
How does the algorithm discriminate the target being photographed?
Background clutter and scenery complicate the image Discrimination Solution
The White Card “Scope” Use the rectangular white card with a square target hole to allow
object color through Use Edge Detection image processing algorithms to find the white
card Find the white card, find the target!
Target Hole
Card
Background
Target Color
Finding the Target: Discrimination Cont
The White Card Problem White backgrounds or light color backgrounds cause
edge detection problems in Canny Algorithm
Original Image After Edge Detection
Where is the card??
Finding the Target: Discrimination Cont
The White Card Problem Cont. Adding a Black Outline to the card edges and target
hole greatly improve detection!
Original Image After Edge Detection
There’s the card!!
Finding the Target: Aiming the Camera How does a blind person AIM the camera to take a
picture of the target? Photographs may NOT include target Photographing target too close may not allow enough lighting to
determine color Aiming Solution- White Card Holder
Use a phone attachment which holds the white card AND attaches to phone camera
Guarantees white card and target in the camera Field of View Guarantees camera is not directly on top of the object, providing
ample lighting for color detection
Camera White Card
Mount
Hinge Assembly to allow for folding
Baseboard
Finding the Target: Aiming the Camera
Additional Benefits of Card Holding Device Fixes Orientation of the card
Chose to have card positioned vertically with edges parallel to photo edges
Simplifies algorithm detection, increasing speed
Removes excess background scenery Device maintains a fixed 6-8 inches between camera
and white card Scene is dominated by white card and maximizes number of
pixels covering the target
Finding the Target: Examples with and without device
Phase 1: Blurring and Sharpening Edges Preprocess Images to blur and eliminate noisy pixels Apply a 3x3 Laplacian Matrix Kernel to resulting image
Kernel is an approximation of the second derivative, highlighting changes in intensity
Matrix =
Adding results of Gaussian Blurring and Laplace yields image with cleaner and more distinct lines
Finding the Target: Edge Detection Algorithm
-1 -1 -1
-1 8 -1
-1 -1 -1
Edge Detection: Blurring and Sharpening
Applying Laplace removesNoise and Smoothes lines
Original Image
After Smoothing and Laplace
Finding the Target: Edge Detection Algorithm
Phase 2: Apply Canny Edge Detection Algorithm
Step 1: Applies Gaussian smoothing in 2 dimensions to the image via convolution Size of the mask = 20x20 with a sigma = 5
Step 2: Compute the resulting gradient of the intensities in the image
Step 3: Threshold the norm of the gradient image to isolate edge pixels
Edge Detection: Applying Canny Edge Detection Algorithm
Original Image
After Canny and Threshold
Finding the Target: Edge Detection Algorithm
Phase 3: Finding the edges in the photo Step 1: Recursively search, row by row, from the
outer left/right edges of the image towards the center. Search for 1 quarter length from right/left side
Target Hole
Card
Left Fourth
Finding the Target: Edge Detection Algorithm
Phase 3 Cont: Step 2: Bin and compute outer edges based on
which values are closest to the center Find left/right edges based on bin having AT LEAST 10% of
the total pixels available on the each side
Step 3: Compute Top and Bottom Edge Compute the average row at which both the left and right
edges begin/end Value gives rough estimate of top/bottom of white card
Right Side Left Side
First bin From CenterExceeding 10%
First bin From CenterExceeding 10%
Finding the Target: Target Hole Detection Algorithm
Outer Target White Card edges Located Proceed to Locate INNER Target Hole edges
Phase 4: Identifying Inner Target Hole Edges Step 1: Crop Canny Thresholded image to dimensions
obtained for outer edge Step 2: Perform recursive row by row outside to inside
search until a high threshold is found on both sides. Step 3: Bin and compute left/right edges as before Step 4: Compute Top and Bottom edges as before
Finding the Target: Output to Color Detection
Phase 5: Compute overall Target Hole Position in ORIGINAL image
Sum up inner and outer edge values computed previously Crop the original image to these dimensions and output to
Color Detection model
Color Detection: Original Idea
Keep it simple:
Use brightest point on white card as white point.
Normalize R,G and B separately. Good results, slight reddish tinge.
20 40 60 80 100 120 140 160 180 200 220
10
20
30
40
50
60
70
80
90
100
20 40 60 80 100 120 140 160 180 200 220
10
20
30
40
50
60
70
80
90
100
Color Detection: “Gray World”
Use “Gray world” theory:
Normalize means of RGB to 128
Results slightly better in low lighting conditions, but less effective under good lighting.
20 40 60 80 100 120 140 160 180 200 220
10
20
30
40
50
60
70
80
90
100
20 40 60 80 100 120 140 160 180 200 220
10
20
30
40
50
60
70
80
90
100
Color Identification: Original Idea
Keep it simple: Bin using RGB Problem:
No clear grouping Small changes in one value, changes color
dramatically Solution:
Attempt to identify groups by using max of R, G and B values.
Still contained overlaps
Color Identification: CIE- L*a*b
Convert RGB to CIELab Benefits:
Device independent.
Problems: Conversion formulas complicated and
processor intensive. Light source information is required.
Solution: use HSV
Color Identification: Use HSV Convert RGB to HSV
The HSV (Hue, Saturation, Value) model is a simple transformation from RGB.
Hue, the color type (like red, blue, etc) ranges from 0-360
Saturation, the "vibrancy" of the color: Ranges from 0-100%
Value, the brightness of the color: Ranges from 0-100%
Color Identification: RGB to HSV Equations used for conversion
Color Selection & Differentiation
Currently code identifies 24 colors based on HSV color system.
Color identification is acceptable, but starts to fail in low lighting conditions.
Results
White
Light Green
White
Indian Red
Lessons Learned Edge Detection: Think about the Big
Picture User Feasibility is Critical
If a soldier cannot aim a gun, how accurate is his shot?
Simplicity is Essential Presetting card orientation led to efficiency and
shortcuts for edge detection Slanted Orientation requires much more
processing time and development Original code variants tried to and failed to account
for all orientations
Lessons Learned
Color Detection: HSV is a compromise between simply
binning on RGB values and conversion to L*a*b.
Normalization using the white point more effective than “gray world”.
Minimum level of lighting in required, since camera is low quality.
References http://robotics.eecs.berkeley.edu/~mayi/imgproc/cacode.html
http://homepages.inf.ed.ac.uk/rbf/HIPR2/canny.htm
http://www.aquaphoenix.com/lecture/matlab10/page3.html
http://en.wikipedia.org/wiki/Canny
http://www.afb.org
http://www.otal.umd.edu/UUPractice/color/
Class notes on Color and jpeg tutorials
Future Work Implementing the processing onto an
actual camera phone Decreasing the processing time to audibly
deliver the color to the user Increasing color library Refining overall algorithm to distinguish
more detailed backgrounds. Patches Patterns Color Designs