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Prepared by:

Prepared by:. 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.

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  1. Prepared by: 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

  2. Key subjects of this framework include: Motivation Visual tracking applications Block diagram of object tracking system Image deformation types Object extraction Morphological operations Geometrical Modeling and pose estimation Conclusion and Future Work Outlines

  3. Motivation The main objectives of this research work are to: 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.

  4. Visual Tracking Applications • The precise object tracking is an essential issue in several applications such as: • Robot vision • Automated surveillance (civil and military) • Medical applications • Satellite and space systems • Traffic systems • Security etc.

  5. Block Diagram of Object Tracking System Video Camera Frame grabber PC Image Acquisition USB Camera USB Bus Image Processing Output Target

  6. Image Deformation Types Noise. Scaling &Rotation. Blurring Change of illumination.

  7. Image Deformation: Noise Definition: is considered to be any measurement that is not part of the phenomena of interest. Images are affected by different types of noise: • Gaussian noise • Salt and Pepper noise • Poisson Noise • Speckle Noise

  8. Image De-noising Techniques The following digital filters have been employed for denoising • Linear filter (Average filter, Gaussian filter and unsharp filter) • Non linear filter (Median filter and Adaptive filter) • Coiflet Wavelets • Proposed filter

  9. 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

  10. 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,-1) w(-1,0) w(-1,0) w(-1,1) w(-1,1) w(0,-1) w(0,-1) w(0,0) w(0,0) w(0,1) w(0,1) w(1,-1) w(1,-1) w(1,0) w(1,0) w(1,1) w(1,1) Pixels of image The result is the sum of products of the mask coefficients with the corresponding pixels directly under the mask Mask coefficients

  11. Nonlinear spatial filters also operate on neighborhoods, and the mechanics of sliding a mask past an image are the same as was just outlined. The filtering operation is based conditionally on the values of the pixels in the neighborhood under consideration. Order-statistics filters are nonlinear spatial filters whose response is based on ordering (ranking) Nonlinear Spatial Filters

  12. 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

  13. Wavelet Transform Figure 1 The two-dimensional FWT - the analysis filter Figure 2 Two-scale of two-dimensional decomposition

  14. 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

  15. Image Similarity Measure To validate the efficiency of the previous digital filters the following similarity measures have been applied • 2D Cross Correlation • Peak Signal-to-Noise Ratio (PSNR)dB

  16. 2D Cross Correlation Table 1. 2D cross correlation similarity measure

  17. Peak Signal-to-Noise Ratio (PSNR)dB Table 2. PSNR similarity measure

  18. 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.

  19. Scaling & Rotation Original image Scaled image Scaled &rotated image Figure 4 Rotated and scaled image

  20. Linear Transformation Figure 5 Control point selection

  21. Linear Transformation Original image recovered image Scaled & rotated image Figure 6 Recovered by using linear transformation

  22. Radon Transformation 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: x' is the perpendicular distance of the beam from the origin and θ is the angle of incidence of the beams.

  23. Radon Transformation Original image Edge detection Edge linking Figure7 Canny edge detection and edge linking

  24. Radon Transformation Figure 8Radon transform projections along 180 degrees, from -90 to +89

  25. Radon Transformation Original image Rotated image recovered image Figure 9 Recovered by using radon transform

  26. Blurring Blurring:degradation of an image can be caused by motion • There are two types of blurring • Known blurring: the length and the angle of blurring are known • Unknown blurring:the length and the angle of blurring are unknown

  27. Deblurring Techniques Deblurring using Wiener filter Deblurring using a regularized filter Deblurring using Lucy-Richardson algorithm Deblurring using blind deconvolution algorithm A blurred or degraded image can be approximately described by this equation

  28. Deblurring using the Blind Deconvolution Algorithm Figure 10Deblurring using the blind deconvolution algorithm

  29. Deblurring Techniques (b) Person detection under motion deformation (a) Blurred image (c)Deblurred image (d) Person detection in deblurred image Figure 11, Capability of object tracking under blurring (a, b) with known blur function and after deblurring (c, d 29

  30. Deblurring Techniques Blurred imagecorrelation with original one Deblurred image using correct parameterscorrelation

  31. Deblurring Techniques Deblurred image using longer PSFcorrelation Deblurred image using different anglecorrelation Figure 12, 2D cross correlation with the deblurring form

  32. Deblurring Techniques Table 3, 2D cross correlation with the deblurring form

  33. 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 ycbcr) to overcome the deformation problem

  34. RGBRepresentation The RGB color model mapped to a cube A Representation of additive color mixing • Weak points of the RGB color model • RGB color model is affected bythe change of illumination • RGB isnon uniform color model

  35. HSV Representations conical representation of the HSV The cylindrical representation of the HSV HSV color wheel • Hue, saturation and intensity are often plotted in cylindrical coordinates with hue the angle, saturation the radius, and intensity the axis.

  36. Chrominance is defined as the difference between a color and a reference white at the same luminance. YCbCr Color Model The conversion from RGB to YCbCr The conversion from YCbCr to RGB

  37. Advantage of YCbCr The main advantages of this model are: The luminance component (Y) of YCbCr is independent of the color The skin color cluster is more compact in YCbCr than in other color space YCbCr has the smallest overlap between skin and non-skin data in under various illumination conditions. YCbCr is broadly utilized in video compression standards YCbCr is a family of color spaces used in video systems. YCbCr is one of two primary color spaces used to represent digital component video (the other is RGB).

  38. Object Extraction • To track a visual target we have to relay on a segmentation technique such as: • Thresholding • Clustering • Region growing • Edge-based • Physical model-based • Frame Subtraction • Fast block matching • Throughout this framework a color table thresholding segmentation technique has been applied to extract the visual target

  39. Homogeneous Object Extraction Original image Tracked object sample

  40. Homogeneous Object Extraction Original image sample RGB HSV YCbCr Figure 13, Comparison of homogeneousobjectextraction

  41. Inhomogeneous Object Extraction Original image Tracked object sample

  42. Inhomogeneous Object Extraction Original image sample RGB HSV YCbCr Figure 14, Comparison of inhomogeneousobjectextraction 42

  43. Morphological operations The most basic morphological operations are dilation and erosion • Dilationadds pixels to the boundaries of objects in an image. • Expand/enlarge objects in the image • Fill gaps or bays of insufficient width • Fill small holes of sufficiently small size • Connects objects separated by a distance less than the size of the window • Erosion removes pixels on object boundaries. • to erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically). • Thus areas of foreground pixels shrink in size, and holes within those areas become larger

  44. Morphological operations • 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.

  45. Morphological operations Binary after removing extra pixel Binary object Binary object after dilation holes Binary object after closing Figure 15, The effect of the morphological operation

  46. Morphological operations Figure 16, Center of gravity, ellipse fitting and bound box of an image

  47. Geometrical Modeling Figure 17 object tracking at different distance

  48. Geometrical Modeling • The relation between distance (D) and no of pixel (N) Figure 18. The relation between range (D) and projection size (N) Where, a = 30606.621 b=-0.03410108

  49. Geometrical Modeling • The relation between the range and location of the object in 3D domain Figure 19. The relation between the range and location of the object in 3D domain

  50. Motion Estimation and Prediction based on FIR Figure 19, FIR model structures

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