Precise Object Tracking under Deformation Eng. Mohamed Hassan, EAEA Supervised by: Prof. Dr. Hussien Konber, Al Azhar University Prof. Dr. Mohamoud Ashour, EAEA Dr. Ashraf Aboshosha, EAEA Submitted to: Communication & Electronics Dept., Al Azhar University
Overcome the imprecision in object tracking caused by different deformation sources such as noise, change of illumination, blurring, scaling and rotation.
Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model
Presenting a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors.
Spatial filtering term is the filtering operations that are performed directly on the pixels of an image.
The process consists simply of moving the filter mask from point to point in an image.
At each point (x,y) the response of the filter at that point is calculated using a predefined relationship.
Spatial Filters
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The result is the sum of products of the mask coefficients with the corresponding pixels directly under the mask Pixels of image Mask coefficients Linear Spatial Filters f(x-1,y-1) f(x-1,y) f(x-1,y+1) f(x,y-1) f(x,y) f(x,y+1) f(x+1,y-1) f(x+1,y) f(x+1,y+1) w(-1,-1) w(-1,0) w(-1,1) w(0,-1) w(0,0) w(0,1) w(1,-1) w(1,0) w(1,1) w(-1,-1) w(-1,0) w(-1,1) w(0,-1) w(0,0) w(0,1) w(1,-1) w(1,0) w(1,1)
The Wavelet transform is a multiresolution analysis tool which decomposes a signal into different frequency sub bands.
Wavelet transform, due to its excellent localization, has rapidly become an indispensable signal and image processing tool for a variety of applications.
Wavelet denoising attempts to remove the noise present in the signal while preserving the signal characteristics, regardless of its frequency content.
Wavelet Transform
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Figure 1 The two-dimensional FWT - the analysis filter Wavelet Transform Figure 2 Two-scale of two-dimensional decomposition
The proposed filter is a cascaded spatial filter based on median fitter and Coiflet wavelets. Its edge-preserving nature makes it useful in cases where edge blurring is undesirable. It is very useful in real object tracking. This filter is the best one for removing all types of noise
Denoising Proposed Filter I/p image Median filter Coiflet Wavelets O/p image Figure 3 Cascaded spatial filter based on median fitter and Coiflet wavelets
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Image Similarity Measure To validate the efficiency of the previous digital filters the following similarity measures have been applied
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Peak Signal-to-Noise Ratio (PSNR) dB Table 2. PSNR similarity measure Unsharp filter Average filter Gaussian filter Median filter Adaptive filter Proposed filter Salt and paper noise 18.59 27.37 25.49 36.00 22.97 49.48 Gaussian noise 9.94 26.16 23.80 26.42 26.79 32.80 Poisson noise 14.74 28.71 30.21 31.92 32.80 43.16 Speckle noise 10.86 26.73 25.38 26.71 27.59 37.67
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Scaling & Rotation Definition: Scaling & rotation is affine Transformation where Straight lines remain straight, and parallel lines remain parallel. Scaling and Rotation: The linear transformation and radon transformation have been used to recover an image from a rotated and scaled origin.
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Scaled image Original image Scaled &rotated image Figure 4 Rotated and scaled image Scaling & Rotation
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Figure 5 Control point selection Linear Transformation
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Original image Scaled & rotated image recovered image Figure 6 Recovered by using linear transformation Linear Transformation
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Radon transform: This transform is able to transform two dimensional images with lines into a domain of possible line parameters, where each line in the image will give a peak positioned at the corresponding line parameters. Projections can be computed along any angle θ, by use general equation of the Radon transformation : Radon Transformation x' is the perpendicular distance of the beam from the origin and θ is the angle of incidence of the beams.
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Original image Figure7 Canny edge detection and edge linking Edge detection Edge linking Radon Transformation
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Figure 8 Radon transform projections along 180 degrees, from -90 to +89 Radon Transformation
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Original image Rotated image recovered image Figure 9 Recovered by using radon transform Radon Transformation
A blurred or degraded image can be approximately described by this equation
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Deblurring using the Blind Deconvolution Algorithm Figure 10 Deblurring using the blind deconvolution algorithm
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Figure 11, Capability of object tracking under blurring (a, b) with known blur function and after deblurring (c, d (a) Blurred image (b) Person detection under motion deformation (c)Deblurred image (d) Person detection in deblurred image Deblurring Techniques
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Blurred image correlation with original one Deblurred image using correct parameters correlation Deblurring Techniques
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Deblurred image using longer PSF correlation Deblurred image using different angle correlation Figure 12, 2D cross correlation with the deblurring form Deblurring Techniques
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Table 3, 2D cross correlation with the deblurring form Deblurring Techniques Correlation Condition blurred image with the original one 0.0614 deblurred image with the original one using correct parameters 0.3523 deblurred image with the original one using longer PSF 0.0558 deblurred image with the original one using different angle 0.1231
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Change of Illumination Change of illumination Color model deformation may happen due to the change in illumination Proposed solution Selecting an appropriate color model (RGB, HSV or yc b c r ) to overcome the deformation problem
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R G B Representation The RGB color model mapped to a cube A Representation of additive color mixing
Weak points of the RGB color model
R G B color model is affected by the change of illumination
Opening and Closing are morphological operations which are based on dilation and erosion.
Opening smoothes the contours of objects, breaks narrow isthmuses and eliminates thin protrusions.
Closing also produces the smoothing of sections of contours but fuses narrow breaks, fills gaps in the contour and eliminates small holes.
Opening is basically erosion followed by dilation while closing is dilation followed by erosion.
Morphological operations
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Binary object Binary after removing extra pixel Binary object after dilation holes Binary object after closing Morphological operations Figure 15, The effect of the morphological operation
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Morphological operations Figure 16, Center of gravity, ellipse fitting and bound box of an image
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Geometrical Modeling Figure 17 object tracking at different distance
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