The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different...

This paper aims at introducing a textural pattern analysis approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of four stages – artefacts reduction, fuzzy divergence-based segmentation of P. vivax infected region(s) and normal ery...

Object-based image analysis (OBIA) is employed to classify forest types, including deciduous, evergreen and mixed forests, in a U.S. National Park unit using very high spatial resolution (VHR) IKONOS satellite imagery. This research investigates the effect of scale on segmentation quality and object-based forest type classification. Average local variance and spatial autocorrelation analyses ar...

The shape of an object is one of the most important features in content based image retrieval. However, the statistical feature of edge is rarely used as a feature that codes local spatial information. This paper presents an approach to represent spatial edge distributions using principal component analysis (PCA) on the edge co-occurrence matrix (ECM). The ECM is based on the statistical featur...

The present paper put forward efficient content-based image retrieval (CBIR) system by extracting structural, texture and local features from images. The local features are extracted from local directional pattern (LDP). The LDP produces a steady local edge response in the presence of noise, illumination changes. The LDP coded image is converted in to a ternary pattern image based on a threshol...

Unsupervised segmentation, based on texture analysis, classifies each image region into three groups: live cells, necrotic cells and background. The segmentation is based on three discriminant functions, built using a total of 12 parameters derived from the histogram and the co-occurrence matrix. These parameters were selected performing a discriminant analysis on a training set that included i...

Many passive image tamper detection techniques have been presented in the expanding field of image forensics. Some of these techniques use a classifier for a final decision based on whole image statistics, resulting in a lack of forgery localization. The aim of this paper is to add localization to a previously published algorithm that uses grey-level co-occurrence matrix (GLCM) for extracting t...

This paper presents a novel steganalysis scheme with highdimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental resu...

Description: The process is a method of classifying prostate tumors as cancerous or benign. It classifies the tumors according to the Gleason grading scale to determine the cancerous nature of the tumor. The process utilizes a shearlet transform, as well as three other features, and combines them via multiple kernel learning. The shearlet transform is used to represent the local structure of im...

In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to human’s fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on ...