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Published byLisa Tucker Modified over 9 years ago
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Student Name: Honghao Chen Supervisor: Dr Jimmy Li Co-Supervisor: Dr Sherry Randhawa
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Content: Background Introduction Procedures Methods Future Extension Conclusion
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Purpose Circling parking lots Wasting time and effort Providing information via digital displays Open car parks Detecting individual and predetermined parking slot Video and image processing Real-time and mobile apps
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Where to go How long to take Where to go if occupied
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Video Mosaicking Stitch video frames together A comprehensive view of the scene A compact representation of the video data
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Procedure: 1. Loading a video sequence 2. Matching points between successive frames by the Corner Matching subsystem 3. Estimating Geometric Transformation block 4. Computing an accurate estimate of the transformation matrix 5. Overlaying the current video frame onto the output image
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Perspective Transformation To change the ‘perspective’ of the active content from one state to another To find a full image
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Image Extraction Predetermined region of each parking space Reference setting
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Methods Color Histogram CPSNR – Color Peak Signal-to-Noise Ratio NCD – Normalized Color Difference
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Color Histogram Provide a global description of the appearance of an image Produce a level for every pixel value in the original image
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Empty parking slots
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Occupied by blue car
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Occupied by red car
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Procedure: Describe the levels of the original image Specify the desired density function Obtain the transformation function Apply the inverse transformation function
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CPSNR (Color Peak Signal-to-Noise Ratio) The ratio between the maximum possible value (power) Is expressed in terms of the logarithmic decibel scale
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I: The matrix data of the original image I: The matrix data of the degraded image M, N : The number of rows and columns of input image R: 255 for an 8-bit unsigned integer data type
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Images took from different locations
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Comparison
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As a quality measurement between the original and a compressed image The higher the CPSNR, the better the quality of the compressed
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Normalized Color Difference Depending on the illumination Depending on different lighting conditions or cameras Allowing for object recognition techniques based on color To compensate for variations
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Application: For object recognition on color images Detect all intensity values from the image while preserving color values NormalizedRed = r/sqrt(Red^2 + Green^2 + Blue^2); NormalizedGreen = g/sqrt(Red^2 + Green^2 + Blue^2); NormalizedBlue = b/sqrt(Red^2 + Green^2 + Blue^2);
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Choosing a suitable reference
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Comparison
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Under different lighting conditions
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Range of NCD Value Information to display > 17Occupied < 8Available
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NCD: 27.3598
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Future Extension Mobile apps for real-time update -GPS Extra information -The nearest slot if possible
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Conclusion: Principle Procedures Video Static image Slot reference Methods (NCD) Display Results Analysis Over 17 Occupied Below 8 Available
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