In this paper we propose a technique for classifying images by modeling features extracted at diierent scales. Speciically, we use texture measures derived from Pap Smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obt...

Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approa...

Several vibrational band-shape studies which have used either Raman or infra-red (IR) spectroscopy have been reported in the literature [1—3]. With very few exceptions [4,5], however, the studies have generally been performed on symmetrical molecules in non-viscous fluids. In such situations molecular motions may be characterized with little ambiguity [6]. Translational and rotational motions o...

The present work aims to bring out a new approach of features selection for the discrimination of textures in sequences of images containing moving objects. The moving textures are analysed using spatio-temporal co-occurrence matrices from which we extract features characterizing the textures themselves as well as their movements. The originality of this approach is to select features that allo...

An expanded model for the thermodynamics of co-clusters and their strengthening is presented, and the model is applied to predict co-cluster formation and strengthening in AlMg-Si alloys. The models are tested against data on a wide range of Al-Mg-Si alloys aged at room temperature. The strengthening due to co-clusters is predicted well. The formation of the co-clusters is studied in an Al-0.5a...

In this paper we propose a technique for classifying images by modeling features extracted at di erent scales. Speci cally, we use texture measures derived from Pap smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obt...

This paper put forward a new method of co-occurrence matrix to describe image features. In this paper putting a new implemented work which is comparison with texton co-occurrence matrix to describe image features. Maximum work done successfully using texton co-occurrence matrix. A new class of texture features based on the co-occurrence of gray levels at points. These features are compared with...

Statewide land cover change detection analysis provides a useful tool for conservation planning and environmental monitoring and addresses issues of habitat fragmentation and urban sprawl. Furthermore, using historical and recent land cover data offers two perspectives on landscape dynamics. To this end, the first alliance level land cover map of Kansas recently completed by the KARS Program wa...

Camera model identification is of interest for many applications. In-camera processes, specific of each model, leave traces that can be captured by features designed ad hoc, and used for reliable classification. In this work we investigate on the use of blind features based on the analysis of image residuals. In particular, features are extracted locally based on co-occurrence matrices of selec...

Background: Although phytoremediation is a promising method for pollution control, biomass produced by the remediation process must be managed; otherwise, it will eventually return to the environment and cause secondary pollution. Therefore, research and policy development for the post-remediation management of biomass are both required. Results: While there are many published studies of phytor...