Texture is ubiquitous in natural images and constitutes a powerful cue for a variety of image analysis and computer vision applications, like segmentation, shape from texture, and image retrieval. We pursue the construction of a concise texture analysis and segmentation system for generic natural images. Our research directions are in the areas of feature extraction, feature interpretation and texture segmentation.

1. Feature extraction: Building on the generic multicomponent AM-FM image modeling framework we research into more efficient algorithms for component demodulation and compact, descriptive representations of texture. We introduce regularized algorithms for demodulation based on energy separation using combinations of Gabor filtering and the nonlinear energy operators. Moreover, dominant modulation components are seeked through alternative channel selection energy measures.

2. Probabilistic analysis: The dominant AM-FM component channel selection is formulated through a probabilistic procedure by modeling observations in terms of sinusoids and introducing locality in likelihood expressions. This facilitates the interpretation of Gabor filtering in terms of model fitting, which is a formulation we also use to phrase edge detection in common terms with texture analysis. This lays the ground for a probabilistic discrimination between edges, textured and smooth areas, which is a practically important problem for image segmentation.

3. Image segmentation: We developed an unsupervised, variational segmentation scheme based on dominant component features that uses curve evolution implemented with level set methods. The method is an enhancement of the original Region Competition - Geodesic Active \Regions that combines heterogenous cues based on probabilistic feature interpretetion. This Weighted Curve Evolution method incorporates the posterior probabilities of the texture and edge classes in the evolution law.

The focus here is on the development and efficient use of descriptive models for a broad class of texture images. Current research includes the topics of:

• expansion and application of the multicomponent AM-FM model for images
• multiband Gabor filtering and filterbank design
• non-linear energy operators (EO) and energy tracking
• demodulation of narrowband components via the Energy Separation Algorithm (ESA)
• advanced and enhanced schemes (Complex, Regularized, GaborESA)
• Dominant Components Analysis (DCA) with alternative selection criteria (Teager Energy)
• low-dimensional feature vectors: dominant amplitude, frequencies and modulation energy

Figure 2. Filtering and modulation components (amplitude and cosine of phase signals) example

Combining the above two aspects of the problem, the texture and non-texture cues are fused in a curve evolution scheme for natural image segmentation.

• DCA features are used as cues for variational unsupervised texture segmentation
• formulation of a Weighted Curve Evolution as a method that allows to combine texture, edge and intensity cues in a locally adaptive manner

Figure 5. Segmentation with normal (left) and weighted (right) curve evolution

Based on these ideas, visually appealing results were obtained on natural images containing both textured and non-textured areas. Additionally, the method resulted in systematically better segmentation results on the Berkeley Segmentation Benchmark, consisting of 100 natural images.

• Giorgos Evangelopoulos
• Iasonas Kokkinos
• Petros Maragos

• I. Kokkinos, G. Evangelopoulos and P. Maragos,
  Texture Analysis and Segmentation using Modulation Features, Generative Models and Weighted Curve Evolution,
  IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. 1, pp. 142-157, Jan. 2009.
  [pdf] [slides] [bib]
  
• G. Evangelopoulos, I. Kokkinos and P. Maragos,
  Advances in Variational Image Segmentation using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration,
  Proc. Int' l Workshop on Variational and Level Set Methods (VLSM-05), Beijing, China, Oct. 2005, Springer LNCS, vol. 3275, pp. 121-136.
  [pdf] [bib]
  
• I. Kokkinos and P. Maragos,
  A Detection-Theoretic Approach to Texture and Edge Discrimination,
  Proc. 4th Int' l Workshop on Texture Analysis and Synthesis, in conjunction with ICCV-05, Beijing, China.
  [pdf]
  
• I. Kokkinos, G. Evangelopoulos and P. Maragos,
  Advances in Texture Analysis: Energy Dominant Component and Multiple Hypothesis Testing,
  Proc. IEEE Int' l Conf. on Image Processing (ICIP-04), Singapore, Oct. 2004, vol. 3, pp. 1509-1512.
  [pdf] [bib]
  
• I. Kokkinos, G. Evangelopoulos and P. Maragos,
  Modulation-Feature Based Textured Image Segmentation Using Curve Evolution,
  Proc. IEEE Int' l Conf. on Image Processing (ICIP-04), Singapore, Oct. 2004, vol. 2, pp. 1204-1207.
  [pdf] [bib]

A toolbox for image texture analysis with modulation features and generative models, implements the ideas of probabilistic feature interpretation and can be found here.