This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks: one pixel as a node. Node degree and clustering coefficient, using with traditional and extended hierarchical measurements, are used to characterize "organisation" of textures.
Texture analysis represents one of the main areas in image processing and computer vision. The current article describes how complex networks have been used in order to represent and characterized textures. More specifically, networks are derived from the texture images by expressing pixels as network nodes and similarities between pixels as network edges. Then, measurements such as the node degree, strengths and clustering coefficient are used in order to quantify properties of the connectivity and topology of the analyzed networks. Because such properties are directly related to the structure of the respective texture images, they can be used as features for characterizing and classifying textures. The latter possibility is illustrated with respect to images of textures, DNA chaos game, and faces. The possibility of using the network representations as a subsidy for DNA characterization is also discussed in this work.
Texture characterization and classification remains an important issue in image processing and analysis. Much attention has been focused on methods involving spectral analysis and co-occurrence matrix, as well as more modern approaches such as those involving fractal dimension, entropy and criteria based in multiresolution. The present work addresses the problem of texture characterization in terms of complex networks: image pixels are represented as nodes and similarities between such pixels are mapped as links between the network nodes. It is verified that several types of textures present node degree distributions which are far distinct from those observed for random networks, suggesting complex organization of those textures. Traditional measurements of the network connectivity, including their respective hierarchical extensions, are then applied in order to obtain feature vectors from which the textures can be characterized and classified. The performance of such an approach is compared to co-occurrence methods, suggesting promising complementary perspectives.
The 3D reconstruction of a scene with 2D images requires several scene acquisitions or the use of specific lighting (structured light). The solution choice depends on the scene to be reconstructed. Unfortunately in the case of face reconstruction, health standards forbid the use of structured light; the elaboration of a multi-sensor system, such as stereovision is then required. In this paper, primary results about depth reconstruction from defocused images are presented. The used method relies on inverse ray tracing and provides results better than those obtained by using the conventional gradient based method.