In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for cla...

An optimal thenar palmprint classification model is proposed in this paper. Firstly, the thenar palmprint image is enhanced using a high-frequency emphasis filter and histogram equalization. Then, from the enhanced image thirteen textural features of gray level co-occurrence matrix (GLCM) are extracted as classification feature vectors. Finally, the SVM classifier is used for classification and...

Hyperspectral imaging (HSI) has been used to perform objects identification and change detection in natural environment. Indeed, HSI provide more detailed information due to the high spectral, spatial and temporal resolution. However, the high spatial and spectral resolutions of HSI enable to precisely characterize the information pixel content. In this work, we are interested to improve the cl...

The application of He-Ne laser technologies for description of articular cartilage degeneration, one of the most common diseases worldwide, is an innovative usage of these technologies used primarily in material engineering. Plain radiography and magnetic resonance imaging are insufficient to allow the early assessment of the disease. As surface roughness of articular cartilage is an important ...

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We prop...

Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed b...

The purpose of this work was to apply and test Haralick’s gray level co-occurrence matrix (GLCM) technique for automatic calculation and segmentation of the ischemic stroke volume from CT images. For this task, the 3nearest neighbors classifier was trained to perform stroke and non-stroke area classification. The segmentation and classification results were compared versus a manual segmentation...

This paper describes that actomyosin complex particles are automatically selected. We propose a new approach, which combines both gray level co-occurrence matrix to extract texture features and SVM classifier to detect actomyosin complex particles automatically. Experimental results show that detection rate achieves 93.58%, the false positive rate is 3.66%, and the area under the ROC curve (AUC...

The paper describes a new method to segment ischemic stroke region on computed tomography (CT) images by utilizing joint features from mean, standard deviation, histogram, and gray level co-occurrence matrix methods. Presented unsupervised segmentation technique shows ability to segment ischemic stroke region.

The concept of an algorithm developed for the segmentation of a road border from the content of an image produced by TV camera mounted on a moving vehicle is presented. The extraction of a road boundary is an important step in the context of autonomous vehicle guidance. The segmentation algorithm combines statistical texture descriptors and the ones based on gray level co-occurrence matrix.