Defects in coffee beans can significantly affect the quality of production so that defects cause a decreasing level production. The purpose this study is to implement GLCM (gray-level co-occurrence matrix) and K-NN (k-nearest neighbor) method on web-based program provided website detect bean defects. This uses algorithm extract features images classify defect beans. system development was built...

The traditional gray level co-occurrence matrix (GLCM) is in two-dimensional form. Because hyperspectral imagery in the feature space has the characteristic of volumetric data, it has a great potential for three-dimensional texture analysis. Previous studies have successfully extended traditional 2D GLCM to a 3D form (Gray Level Co-occurrence Matrix for Volumetric Data, GLCMVD) for extracting f...

Introduction: Advanced quantitative information such as radiomics features derived from magnetic resonance (MR) image may be useful for outcome prediction, prognostic models or response biomarkers in Glioblastoma (GBM). The main aim of this study was to evaluate MRI radiomics features for recurrence prediction in glioblastoma multiform.   Materials and Methods:</str...

conclusions test-retest and correlation analyses have identified non-redundant radiomics features and this feature are prone to errors if they employed as quantitative biomarker for gbm image analysis. however when we use robust and redundant feature, quantitative image radiomics features are informative and prognostic biomarkers for gbm magnetic resonance imaging. results results shows that th...

My aim is to extend the notion of gray zone, presented by Claire Bishop in her 2018 essay, Black Box, White Cube, Gray Zone, through that skilled intentionality, proposed Erik Rietveld and colleagues (Rietveld, Denys, Van Westen, 2018) field embodied cognitive science. Starting, thus, from an ecological-enactive approach cognition, I will present co-presence which try shed light on how we are a...

image denoising, co-occurrence matrix, salt and pepper noise, image statistics, universal denoiser This invention is concerned with a method for removing noise (denoising) from gray level images using statistics that model the image textures. The denoising method presented here uses the framework of the general denoising algorithm published in [1]. The algorithm in [1], known as the Discrete Un...

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. Various texture feature extraction methods include those based on gray-level values, transforms, auto correlation etc. We have chosen the Gray Level Co occurr...

Introduction Texture analysis using 2D-image-based gray level co-occurrence matrix method [1] has been demonstrated to be useful in distinguishing between malignant and benign breast lesions in contrast-enhanced MR images [2]. 2D texture analysis does not take advantage of the 3D data in breast MR images and requires high signal-to-noise ratio, which may not be available in dynamic studies. We ...