nowadays, sar imaging systems have become more interesting for scientists. high spatial resolution of recent sar images has become a key factor for their accurate applications such as interferometry, radargrammetry and change detection. in these applications, sar image matching procedure is a critical step for conjugate point extraction which is much more difficult than the optical images. the ...

The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (...

The estimation of metallurgical parameters of flotation process from froth visual features is the ultimate goal of a machine vision based control system. In this study, a batch flotation system was operated under different process conditions and metallurgical parameters and froth image data were determined simultaneously. Algorithms have been developed for measuring textural and physical froth ...

This paper presents a way to classify between the nest image and the non-nest image. The input image is processed by Canny Edge Detection, and using Local Binary Pattern to extract the local textural features. K-means was applied to clustering LBP feature. And, dividing images into several blocks and finding out the dominant local textural features for describing the corresponding block. The pe...

Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural fe...

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks ...

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector ma...

The selection of suitable features is the most critical part of any classification process. In the area of textural classification, one way to determine if the features are suitable is to synthesize an image which has the given textural features. An algorithm for synthesizing textures that have a set of given cooccurrence matrices is presented, and it is shown that these synthetic images do ind...

This paper presents a method for classification of Mammographic Microcalcifications using Quadmture Mirror Filter (QMF) bank subband decomposition as a tool for extracting textuml features. The QMF bank splits the input image into four subbands: low-low band, low-high band, high-low band and high-high band, and textural features baaed on eo-occurrence matrices are computed from the subsampled s...

Analysis of medical images plays a very important role in clinical decisionmaking. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagno...