Perspective Texture Synthesis Based on Improved Energy Optimization

<div><p>Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it’s time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.</p></div

Texture scale variation using viewer centered spherical coordinate system.

<p>Tilt components of surface orientation is illustrated using a set of round patches arranged on a sphere. Central line at each patch shows the direction of the surface normal.</p

Neighborhood number: An example of replacing pixel-based computation with 2*2 patch.

<p>Neighborhood number: An example of replacing pixel-based computation with 2*2 patch.</p

Perspective texture synthesis of few selected sample textures, for all the textures input is on the left and output is to the right.

<p>Second and third row’s last input textures are taken from [Kwatra et al. 2005].</p

Some examples of perspective texture, which apparently have visual properties.

<p>These three texture images will be texture samples of our approach, as input, to show the synthesis effect.</p

Energy optimization process on sparse.

<p>Energy optimization process on sparse.</p

Perspective texture synthesis of a flower image.

<p>(a) the input example; (b) scale map with σ = 60°, τ = 20°; (c) our result; (d) optimization [Kwatra et al. 2005] result.</p

Definition of slant and tilt angle from 3D space and image coordination.

<p>(a) XY is image plane; Z is normal of image plane; π is texture plane; Z’is normal of texture plane; Zp’ is the projection of Z’ on XY; σ is the slant angle (between Z and Z’); τ is the tilt angle (between X and Zp’). (b) Image coordination of a texture.</p

The flow-process diagram of our algorithm.

<p>The flow-process diagram of our algorithm.</p

We obtain scale maps for the given tilt and slant.

<p>From the left to the right: the input example, scale map with σ = 30° and τ = 18°, scale map with σ = 60° and τ = 60°.</p