Manifold-Based Analysis of Natural Stochastic Textures with Application in Texture Synthesis
- Resource Type
- Conference
- Authors
- Zachevsky, Ido; Zeevi, Yehoshua Y.
- Source
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on. :1298-1302 Apr, 2018
- Subject
- Signal Processing and Analysis
Manifolds
Geometry
Principal component analysis
White noise
Stochastic processes
Measurement
Dimensionality reduction
Natural stochastic textures
manifolds
texture synthesis
- Language
- ISSN
- 2379-190X
Embedding textured images in manifolds reveals latent information regarding texture structure and allows useful analysis of these high dimensional images in a low dimensional space. We present a framework for analysis and synthesis of natural stochastic textures (NST) which constitute an important subset of textures that are modelled as realizations of random processes. The randomness of NST differentiates them from other types of images and requires a dedicated method for analysis and synthesis. We demonstrate several applications of this framework. The first is synthesis of new types of NST. The second is NST analysis, reaffirming our previous findings regarding the fundamental properties of NST, and showing that they emerge naturally in the latent parameter space. Finally, we show the advantage of producing a manifold representation with intrinsic geometry.