Diffusion Tensors for Processing Sheared and Rotated Rectangles Gabriele Steidl and Tanja Teuber 指導教授 張元翔 指導教授 張元翔 學生 陳昱辰 學生 陳昱辰

I. INTRODUCTION Image restoration and simplification methods that respect important features such as edges play a fundamental role in digital image processing. Image restoration and simplification methods that respect important features such as edges play a fundamental role in digital image processing.

I. INTRODUCTION we adapt the diffusion tensor for anisotropic diffusion to avoid this effects in images containing rotated and sheared rectangles we adapt the diffusion tensor for anisotropic diffusion to avoid this effects in images containing rotated and sheared rectangles

I. INTRODUCTION Diffusion tensor Convolved Gaussion Flux Diffusion equation

ANISOTROPIC DIFFUSION MODEL Structure Tensor and Diffusion Tensor EED applies the structure tensor concept to define useful diffusion tensors. By definition,j is a rank 1 matrix with spectral decomposition

Fig.1

ANISOTROPIC DIFFUSION MODEL Adaptation to Rotated Rectangles Adaptation to Rotated Rectangles the new matrix can only distinguish between angles and its smoothing should nicely relate the vertices to the corresponding edges.

Fig.2 The local character of the angle smoothing via the structure tensor is obvious.

ANISOTROPIC DIFFUSION MODEL Remark: we can also consider rotated and sheared rectangles, where we know the shear matrices slightly modifying the diffusion tensor. We just define to be the angles of S

Fig.3 we can estimate the shear parameters by the gradients of the two nonhorizontal edges of each sheared rectangle.

ANISOTROPIC DIFFUSION MODEL Adaptation to Sheared Rectangles Adaptation to Sheared Rectangles To process such images while preserving sharp vertices, we want to incorporate an estimation of the shear parameters into the diffusion tensor

ANISOTROPIC REGULARIZATION MODEL we can use regularization methods to process images consisting of linearly transformed rectangles if we know the transformation matrix at each pixel Perona-Malik diffusivity leading to forward-backward diffusion. This PDE becomes well-posed by using the smoothed image in the diffusivity

Fig.6 Fig.7

Fig.5

DISCRETIZATION ISSUES 1) Anisotropic Diffusion To discretize diffusion model we apply an explicit time discretization and discretize partial spatial derivatives by central differences, where we additionally use a smoothing filter

DISCRETIZATION ISSUES 2) Anisotropic Regularization To minimize numerically,we compute the minimizer of its discrete counterpart. In this paper, we prefer, due to the observed fast convergence, to minimize the functional by SOCP.

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SUMMARY AND CONCLUSIONS Preserving vertices is still a problem in image processing. Preserving vertices is still a problem in image processing. Future work has to be invested for processing images containing Future work has to be invested for processing images containing both rotated and sheared rectangles as well as arbitrary multiple both rotated and sheared rectangles as well as arbitrary multiple orientations at corners and junctions. orientations at corners and junctions.