Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffracti...

When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GL...

In this paper, the authors propose a novel method for texture analysis to discriminate malignant GGO from benign GGO in LIDC/IDRI dataset. The proposed method for texture analysis is based on the oriented gray level co-occurrence matrix (GLCM), which is also proposed by the authors. The oriented GLCM has the advantages that the noise reduction is unnecessary, and arbitrary direction and distanc...

The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all the images in the database and statistics such as contrast, correlation, homogeneity and energy are derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all the...

The mission of automatically recognizing different facial expressions in human-computer environment is significant and challenging. This paper presents a method to identify the facial expressions by processing images taken from Facial Expression Database. The approach for emotion recognition is based on the texture features extracted from the gray-level co-occurrence matrix(GLCM) . The results ...

Image processing technology has been successfully applied to fault detection of copper flotation processes, and the key to realize image processing based fault condition recognition is accurately extracting froth image features closely related to key production indices. To extract texture features of froth images in real-time, a multi-scale gray level co-occurrence matrix (M-GLCM) method is pro...

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis (PCA) is used to reduce the feature ...

Image classification is a process of classifying an image based upon the training given to a classifier. There are various purposes of classification but in this work a Brain MRI image is taken as input and is mainly classified into three class’s malignant tumor and benign tumor and non tumor by using Neural Network classifier. Here a Gaussian Mixture model is used for the purpose of segmentati...

Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2009-25 Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the image. Texture is an important feature of many image types, which is the pattern of information or arrangement of the structure found in a picture. Tex...