Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. Statistical and structural approaches have extensively studied in the texture analysis and classif ication whereas little work has reported to integrate them. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence mat...

The long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, i...

This study proposed an approach to retrieve images based on texture features using GLCM and image subblocks. Each image is divided into three rows and three columns with equal sizes. Texture features are extracted based on GLCM (Gray Level Co-occurrence Matrix) using four statistical features that is contrast, homogeneity, energy and correlation. The features are calculated in four directions (...

Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different text...

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we p...

This paper presents work on the development of automatic feature extraction from multispectral aerial images and lidar data based on test data from two different study areas with different characteristics. First, we filtered the lidar point clouds to generate a Digital Terrain Model (DTM) using a novel filtering technique based on a linear first-order equation which describes a tilted plane sur...

This paper describes a novel system for real-time video texture analysis. The system utilizes hardware to extract 2-order statistical features from video frames. These features are based on the Gray Level Co-occurrence Matrix (GLCM) and describe the textural content of the video frames. They can be used in a variety of video analysis and pattern recognition applications, such as remote sensing,...

ii To my family and friends iii ABSTRACT Surface textures are the most salient characteristics of an object as it encode surface details. Texture classification is the process to classify the images into different classes of textures and has been widely used in various implementations based on the textural information of the subjects, such as face detection, defects detection and rock classific...

Abstract: Classification of the cervical cell is one of the most important and crucial tasks in the medical image analysis. Due to its importance, the aim of the paper is to investigate about the classification of Cervical Cell as Normal Cell or Abnormal Cell by using individual feature extraction method and combining individual feature extraction features method with the classification techniq...