Gray Level Co-occurrence Matrix Approach Research Articles

IDENTIFICATION OF POTATO LEAF DISEASES USING ARTIFICIAL NEURAL NETWORKS WITH EXTREME LEARNING MACHINE ALGORITHM

Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

Visual perception of surface roughness via maximum variance direction of image patches

Most existing visual roughness perception algorithms are pixel-wised operations, which lead to unstable roughness measurements. To address this problem, a novel visual roughness perception in the maximum variance direction of image patches is proposed in this article. Specifically, each patch is assumed to be a point in a multi-dimensional Euclidean space. To improve the signal-to-noise ratio of roughness, all points are projected onto its maximum variance direction based on principal component analysis. In this direction, the variance of these points is characterized as roughness. A new roughness metric dataset is constructed by scanned images of sandpapers with different mesh sizes to verify the effectiveness of the proposed algorithm. Experiments on the dataset show the proposed method outperforms many computer vision-based methods, such as the gray-level co-occurrence matrix approach and the approach.

Changes of gray level co-occurrence matrix (GLCM) texture and antioxidant level during freezing of pumpkin

GLCM (gray level co-occurrence matrix) textural features are rarely extracted from SEM (scanning electron microscopy) food images to study the effect of food processing on cellular microstructure and bioactive contents. Using the GLCM approach, the current study attempted to use textural information from pumpkin SEM images to obtain detailed cellular degradation caused by freezing treatment and to find their relationship with the antioxidant content. High dimensional textural features of the samples were obtained during image processing and subjected to multivariate analysis. The increase of antioxidant activity due to the freezing of pumpkin was used to create a vector factor to indicate the membership class of individual pumpkin samples. Following freezing, the flavonoid content of the pumpkins increased ranging from 15.27% to 70.39%. Freezing also caused the formation and increased of several volatile compounds detected by GCMS. The PCA (principal component analysis) results showed that two components were sufficient to explain the most variance of the dataset. Further supervised sPLS-DA (sparse partial least squares-discriminant analysis) indicated a clear separation of samples based on their antioxidant levels, suggesting an existing relationship between the texture and antioxidant. In conclusion, GLCM textural changes can be used accurately to examine the effect of freezing on the antioxidant of pumpkin.

Mean of Correlation Method for Optimization of Affective States Detection in Children

At the moment, most of the studies on classification of affective states for children focus on visual observations and physiological cues, where all data collection for measuring physiological signals are contact-based and invasive. With the requirement of having the measuring device attached to the body approach, distraction of the subject normally masks the true affective states of the subject due to discomfort. In this paper, a non-invasive, contactless, and less distraction method is proposed to measure the physiological cues of the subjects using their thermal imprints from frontal face imaging. A thermal image camera is used to identify basic affective states, where it is a contactless and seamless device with ability to read the radiated thermal imprint of the subjects’ facial skin temperature. This paper proposes an effective algorithm of texture analysis based on novel technique using Gray Level Co-occurrence Matrix approach to be applied so as to identify blood-flow region. The cues from the first order statistics are computed in the identified blood flow region and concatenated along with second order statistics cues, in order to construct feature vectors to administer the vital and distinguishable characteristic pattern between affective states in thermal images. Result from the fine k-NN classifier obtained promises the efficacy of the proposed approach to be applied in our future work in human–robot interaction for autistic children learning and training.

Assessment of Tear Film Using Videokeratoscopy Based on Fractal Dimension.

The proposed automated approach for estimating the quality of the tear film closes the gap between the manual and automated assessment, translating the high-speed videokeratoscopy technology from scientific laboratories to a clinical practice. To develop and test a new method for characterizing Tear Film Surface Quality with high-speed videokeratoscopy utilizing a fractal dimension approach. The regularity of the reflected pattern in high-speed videokeratoscopy (E300; Medmont) depends on tear film stability. Thus, determining tear film stability can be addressed by estimating the fractal dimension of the reflected pattern. The method is tested on 39 normal subjects. The results of the fractal dimension approach are compared with those obtained using previously proposed automated method, based on a gray-level co-occurrence matrix approach, and with subjective results obtained by two operators that were assessing the video recordings in ideal conditions. Fractal dimension method was less affected by eye movements and changes in the videokeratoscopic image background than gray-level co-occurrence matrix method. Median difference of the noninvasive break-up time between manual and automated methods was 0.03 s (IQR = 4.47 s) and 0.0 s (IQR = 2.22 s) for gray-level co-occurrence matrix and fractal dimension approaches, respectively. Correlation coefficient with manual noninvasive break-up time was r = 0.86 (P < 0.001) for gray-level co-occurrence matrix approach, and r = 0.82 (P < 0.001) for fractal dimension approach. Significant statistical difference was found between noninvasive break-up measurements of manual and gray-level co-occurrence matrix method (P = 0.008). The proposed method has the potential to characterize tear film dynamics in more detail compared to previous methods based on high-speed videokeratoscopy. It showed good correlation with manual assessment of tear film.

Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information

The work presented here showed a comprehensive evaluation of dual-polarimetric RISAT-1 data for land use/land cover (LULC) classification. The textural images were extracted with the help of gray-level co-occurrence matrix approach. Analysis of inter-class separability using transformed divergence method was performed to recognize the potential textural images. The best combination of textural images was also identified on the basis of standard deviation of preferred textural images and correlation coefficients. The maximum likelihood classifier-based classification results for different scenarios were compared. Furthermore, various classification algorithms, maximum likelihood classifier (MLC), artificial neural network (ANN), random forest (RF) and support vector machine (SVM), were performed on the best identified scenario in order to observe the most suitable algorithm for LULC classification. The combination of radiometric and their related textural images was found improving the overall classification accuracy than individual datasets. The highest overall classification accuracy was found using SVM (88.97%) followed by RF (88.45%), ANN (83.65%) and MLC (78.18%).

An efficient multi-RVM classification-based ultrasound lung image retrieval approach

This paper proposes an efficient Multi-Relevance Vector Machine (multi-RVM) classification-based ultrasound image retrieval approach for retrieval of lung images relevant to the query image. In our proposed work, a hybrid median filter is used for filtering the training and testing ultrasound lung image. Extraction of feature in the lung image is performed by using the Tamura features and convoluted grey-level co-occurrence matrix approach. The particle swarm optimisation combined differential evolution feature selection approach performs selection of minimum set of features relevant to the query image. Multi-RVM-based classification technique is used to identify the types of lung diseases. Finally, the Hamming-distance-based retrieval technique performs retrieval of similar and relevant lung images from the database. From the performance analysis result, it is clearly evident that the proposed approach achieves better performance in terms of accuracy, sensitivity and specificity, when compared to the existing classification techniques.

Noise robustness of texture features

This note examines the noise robustness of two sets of texture features, one set derived from the popular multichannel filtering approach, and the other from the benchmark grey level co-occurrence matrix approach. Comparative experimental results are presented. The results clearly demonstrate the superiority of the multichannel approach.

Cloud field classification based upon high spatial resolution textural features: 2. Simplified vector approaches

The huge volume of data being collected in global climate studies makes it necessary to develop efficient automatic data analysis methods. While most cloud classification algorithms are based upon multispectral signatures, there is growing use of textural features. The results given in Part 1 of this study demonstrate that textural features computed from the Gray Level Cooccurrence Matrix (GLCM) approach produce high cloud classification accuracies. The present study compares classification results derived from two vector approaches, Sum and Difference Histogram (SADH) and Gray Level Difference Vector (GLDV), with those from the GLCM approach. It is found that the SADH approach produces accuracies equivalent to those obtained using GLCM, but with greater ability to resolve error clusters; also, there is a 30% savings in run time and a 50% savings in storage requirements. The GLDV approach suffers a slight degradation in classification accuracy but has a 40% savings in run time and an 87% savings in storage requirements. Textural features are not highly sensitive to moderate variations in cloud threshold selection. However, the whole cloud, rather than only the brightest portions of the cloud, produce the highest classification accuracies. A very important result is that spatial information content and classification accuracy are preserved even at lower radiometric resolutions with effective gray levels of 16. means that significantly low resolution digitized versions of satellite imagery retain essentially the full spatial information content of the original digital data. Substitution of digitized imagery can significantly reduce the expense of many remote sensing studies.