Steganalysis is a technique to detect the hidden embedded information in the provided data. This study proposes a novel steganalytic algorithm which distinguishes between the normal and the stego image. III level contourlet is exploited in this study. Contourlet is known for its ability to capture the intrinsic geometrical structure of an image. Here, the lowest frequency component of each leve...

In this paper, scale invariant texture classification method based on Fuzzy logic is developed. It is applied for the classification of texture images. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Gray Level Co-occurrence matrix (GLCM). Two features are obtained from each sub-band of DWT coefficients up to fifth level of decomposition an...

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using Artificial Neural Network (ANN). No previous research combines GLCM method with artificial neural networks in classification. The number of images this is 360 three labels, including 120 A images, AA AAA images. epochs were 10, 20, 30, 40, 50, 60, 70, 80. test results at epoch 10 got 80....

Mechanical properties of internal curing concrete are greatly affected by its physical such as water content, cementing material porosity, and saturation. At the micro- level, impact is finally reflected in surface texture materials. In this study, image recognition technology was used to find that samples have significant micromorphology features. A parameter–strength model established based o...

Gray level co-occurrence matrix (GLCM) is an important method to extract the image texture features of synthetic aperture radar (SAR). However, GLCM can only extract the textures under single scale and single direction. A kind of texture feature extraction method combining nonsubsampled contour transformation (NSCT) and GLCM is proposed, so as to achieve the extraction of texture features under...

With advancement in technology, especially imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many detection techniques have been developed from time time. For rapid and accurate of forged image, novel hybrid technique is used research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoin...

This paper presents 15 texture features based on GLCM (Gray-Level Co-occurrence Matrix) and GLRLM (Gray-Level Run-Length Matrix) to be used in an automatic computer system for breast cancer diagnosis. The task of the system is to distinguish benign from malignant tumors based on analysis of fine needle biopsy microscopic images. The features were tested whether they provide important diagnostic...

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...

The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, decision support are combined to design a smart agriculture assistance system. proposed system has sensor pack that provides continuous data capture temperature records, air soil moisture camera obtaining near-infrared (NIR) images the plant leaves use with an AI We identify twelve types vegetat...

Image segmentation is a key step of oil spills detection in SAR images. For the problem that the traditional multi-spectral clustering algorithm with the features extraction by GLCM (Gray-Level Co-occurrence Matrix) has such limitations as direction sensitivities and difficulties in selecting the best feature combination etc., this paper proposes a multi-scale segmentation method of oil spills ...