Gray Level Co-occurrence Matrix Descriptor Research Articles

Gray-level co-occurrence matrix of Smooth Pseudo Wigner-Ville distribution for cognitive workload estimation

Automatic, cost-effective, and reliable cognitive workload estimation (CWE) is one of the important issues in diagnosis and treatment of neurocognitive diseases, cognitive performance improvement and error preventive strategies. To address this issue, this paper has proposed a novel and robust CWE method by detecting the time–frequency (TF) changes of electrodermal activities (EDA). Firstly, the local and global properties of the time-variant characteristics of EDA have been presented using Smooth Pseudo Wigner-Ville distribution with enhanced TF resolution. Then, the transient changes in TF images of EDA signals have been quantified using a set of textural features based on Gray Level Co-occurrence Matrix descriptor (GLCM). Several static and dynamic classifiers, such as support vector machine, K- k-nearest neighbor, cascade forward neural network, and recurrent neural network have been explored. A real EDA data experiment recorded during arithmetic task with different workload levels have been used to evaluate the performance of the proposed method. The obtained results have confirmed that it can achieve a high estimation performance of 97.71% using contrast feature for discrimination of three workload levels. Further analysis has also suggested that the model is robust to GLCM parameters and classifiers and can provide a better tradeoff between computational complexity and high performance using minimum number of textural features in comparison with previous studies.

Correlation Study of 3D Surface Roughness of Milled Surfaces with Laser Speckle Pattern.

Current studies are focused on the correlation between characteristic features extracted from the laser speckle pattern of machined surfaces and 2D surface roughness parameters. Since milled surfaces are 3D in nature, 3D surface roughness parameters will provide a more accurate representation of the surface. Novelties of this work are: (1) an inexpensive laser pointer, which was used for presentation and was used without any spatial filtering setup for producing the laser speckle pattern; (2) a correlation study, which was conducted between characteristic features extracted from the speckle pattern and 3D surface roughness; and (3) the influence of angle of illumination, lens aperture size (f-number) and shutter speed on the correlation. A highest coefficient of determination of 0.8955 was obtained for the correlation between the gray level co-occurrence matrix descriptor, namely energy, and 3D surface roughness parameter, namely ten-point height S10z, at an illumination angle of 45°, f-number of 16 and shutter speed of 1/100 s.

A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image

Several CBIR schemes have been devised by the formation of the low dimensional image feature vector from the primitive visual features such as color, texture and/or shape of the image to speed up the retrieval process. In this paper, the visual contents of the images have been extracted using block level Discrete Cosine Transformation (DCT) and Gray Level Co-occurrence Matrix (GLCM). Since the DC coefficients based feature vector has retained the most significant visual components of the image, so initially, we have computed DC coefficients based uniform quantized histogram and some statistical parameters are derived from that histogram for the formation of the DC feature vector. Subsequently, other significant visual features are computed from the residual image where the residual image is obtained by taking the difference between the original image plane and the DC coefficients based reconstructed image plane. Thereafter, some statistical parameters from GLCMs of the residual image are considered for the construction of the GLCM based feature vector. This feature vector is suitable to identify the texture features of the residual image in a more effective way. The single feature vector has been obtained by combining DC and GLCM feature vectors since the combined extracted features from the images increase the accuracy of any image retrieval system. We have tested the scheme either in intensity image or on three color planes of an RGB color image. The experimental results are evaluated on two standard image databases and demonstrate the effectiveness of the proposed scheme in terms of average retrieval accuracy. In addition, the overall speed of the proposed CBIR system is high due to the formation of the low dimensional significant feature vector. The comparative results also show that the proposed scheme provides effective accuracy than some other state-of-the-art CBIR schemes.

Supervised Classification of Very High Resolution Optical Images Using Wavelet-Based Textural Features

In this paper, we explore the potentialities of using wavelet-based multivariate models for the classification of very high resolution optical images. A strategy is proposed to apply these models in a supervised classification framework. This strategy includes a content-based image retrieval analysis applied on a texture database prior to the classification in order to identify which multivariate model performs the best in the context of application. Once identified, the best models are further applied in a supervised classification procedure by extracting texture features from a learning database and from regions obtained by a presegmentation of the image to classify. The classification is then operated according to the decision rules of the chosen classifier. The use of the proposed strategy is illustrated in two real case applications using Pleiades panchromatic images: the detection of vineyards and the detection of cultivated oyster fields. In both cases, at least one of the tested multivariate models displays higher classification accuracies than gray-level cooccurrence matrix descriptors. Its high adaptability and the low number of parameters to be set are other advantages of the proposed approach.