Texture analysis plays an essential and a major rule in image classification and segmentation in a wide range of applications such as medical imaging, remote sensing and industrial inspection. In this paper, we review the well known approaches of texture feature extraction and perform a comparative study between them. These approaches are namely gray level histogram, edge detection, and co-occu...

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...

Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3x3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture descri...

remote sensing can be used as a powerful tool by using data from different sources and combine them for vegetation and land cover classification. pasture type classification provides key information for analysis of agricultural productivity, carbon accounting and biodiversity. the first data set that used in this study landsat tm (thematic mapper) optical image and the second envisat asar radar...

Random and natural textures classification is still one of the biggest challenges in the field of image processing and pattern recognition. In this paper, texture feature extraction using Slant Hadamard Transform was studied and compared to other signal processing-based texture classification schemes. A parametric SHT was also introduced and employed for natural textures feature extraction. We ...

Texture analysis is a highly significant area in the arena of computer vision and connected pitches. Not the least, classification is also equally important and laudable zone in the area of understanding the texture pattern and is gaining a lot of interest among the researchers in the field of computer vision. It finds a widespread application in area of pattern classification, robotic applicat...

Conventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a g...

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of...

Textures have an intrinsic multiresolution property due to their varying texel size. This suggests using multiresolution techniques in texture analysis. Recently linear scale space techniques along with multiple classifier systems have been proposed as an effective approach in texture classification especially at small sample sizes. However, linear scale space blurs and dislocates conceptually ...