Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence matrix (GLCM). The other statistical approach to texture analysis is the texture spectrum approach. The present paper combines the fuzzy texture unit and GLCM app...

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt find and discover the best types of texture features classifiers that are appropriate for coarse mesh finite differenc (CMFD). Segmentation-based fractal analysis (SFTA), local binary pattern (LBP), Haralick have been chosen. K-nearest neighbors (KNN), naïve Bayes...

This paper takes the advantage of the property that the shadow will not obviously change the surface texture of object and presents a method based on texture feature consistency of shadow for image splicing detection. First, texture features of shadows areas and its adjacent lit areas are extracted. Then, the Euclidean distance is used to measure the similarity of texture features. Last, a text...

Flower classification is a useful way for grouping a flower in certain class using specific features. This research propose a new method of flower classification system using combination of color and texture features. The first phase is getting the crown of the flower, which is localized from a flower image by using pillbox filtering and OTSU’s thresholding. In the next phase, color and texture...