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Camera Phone Color Appearance Utility Finding a Way to Identify Color

Camera Phone Color Appearance Utility Finding 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.

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Camera Phone Color Appearance Utility Finding a Way to Identify Color

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  1. Camera Phone Color Appearance Utility Finding a Way to Identify Color Phillip Lachman Robert Prakash Elston Tochip

  2. Outline • Motivation • Goal • Methodology • Image Scaling via Edge Detection • Color Identification • Color Selection & Differentiation • Results • Lessons Learned • Future Work

  3. 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/

  4. Goal • To develop a software application that will be able to accomplish the following: 1) Receives a camera quality image 2) Identifies the predominant color(s) regions within the image 3) Estimates the color name for the predominant region 4) Audibly transmits the predominant color to the user

  5. Locating the Target

  6. 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

  7. Card Target Hole 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! Background Target Color

  8. Finding the Target: Discrimination Cont • The White Card Problem • White backgrounds or light color backgrounds cause edge detection problems in Canny Algorithm After Edge Detection Original Image Where is the card??

  9. 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!!

  10. Camera White Card Hinge Assembly to allow for folding Mount Baseboard 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

  11. 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

  12. Finding the Target: Examples with and without device

  13. Finding the Target: Edge Detection Algorithm • 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

  14. Edge Detection: Blurring and Sharpening Original Image After Smoothing and Laplace Applying Laplace removes Noise and Smoothes lines

  15. 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

  16. Edge Detection: Applying Canny Edge Detection Algorithm Original Image After Canny and Threshold

  17. Target Hole Left Fourth Card 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

  18. 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 First bin From Center Exceeding 10% First bin From Center Exceeding 10% Left Side Right Side

  19. 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

  20. 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

  21. 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.

  22. 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.

  23. 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

  24. 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

  25. 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%

  26. Color Identification: RGB to HSV • Equations used for conversion

  27. 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.

  28. Results White Light Green White Indian Red

  29. 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

  30. 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.

  31. 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

  32. 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

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