Dynamic Texture Stimulation Model: Motion Clouds We first provide an axiomatic derivation of the Motion Clouds model introduced in [1]. In particular, we show that the original definition as a spatiotemporal stationary Gaussian field could be approximated by the random aggregation of warped patterns providing a generative model. We define a dynamic texture as 1 I (x, t) = p X p✓p (x Xp Vpt)) (1...

Image inpainting or image completion consists in filling in the missing data of an image in a visually plausible way. Many works on this subject have been proposed these recent years. They can mainly be decomposed into two groups: geometric methods and texture synthesis methods. Texture synthesis methods work best with images containing only textures while geometric approaches are limited to sm...

We present a synthesis technique that can automatically generate stroke patterns based on a user-specified reference pattern. Our method is an extension of texture synthesis techniques to vector-based patterns. Such an extension requires (a) an analysis of the pattern properties to extract meaningful pattern elements (defined as clusters of strokes) and (b) a synthesis algorithm based on simila...

We present a synthesis technique that can automatically generate stroke patterns based on a user-specified reference pattern. Our method is an extension of texture synthesis techniques to vector-based patterns. Such an extension requires (a) an analysis of the pattern properties to extract meaningful pattern elements (defined as clusters of strokes) and (b) a synthesis algorithm based on simila...

We present an eficient multiresolution block samplingbased texture synthesis algorithm that works well for a wide vatiety of challenging textures. We consider the texture synthesis as a procedure that first estimates and then resumples a probability function. To achieve the eflect of good and fast estimation of the probubility function for a given texture us well us to retain the structural inf...

Features such as edges and corners play an important role in visual data processing. The proposed research employs features to facilitate image segmentation, texture synthesis and visual data approximation. Edges capture primary color/intensity changes in an image, which can be utilized to accelerate image segmentation. In example-based texture synthesis, edges and ridges/valleys can also help ...

Large terrain databases require a great number of polygons and textures. In consequence, transmission of terrain data over slow networks is still worrying. Multiresolution models allow progressive transmission, that is, the transmission of a simple model followed by successive refinements. In this work we describe a new multiresolution model called Geometric-Textured Bitree (GTB) that enables p...

In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary locations and scales to control the desired output texture. Our generative network learns to synthesize objects co...

1 Goal and Scope My project goal is to design a perspective texture synthesis algorithm. With the generated texture world, I hope to create an infinity roam effect similar to [HRRG08]. 2 Problems with traditional texture synthesis Traditional texture synthesis approaches create visually similar images by comparing local neighbors. They can preserve local statistical properties of the texels. Ho...

An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these functions separately with structure and texture filling-in algorithms. The first function used in the dec...