as widespread deserts is located in west and southwest of iran plateau, dust storms form due to west andsouthwest systems over syria or iraq as well as arabian peninsula. these systems severely affect west and southwestregions. sometimes the fine dusts transmit to central, north east, and east regions. in this study for investigating dustysynoptical patterns, meteorological data at 5 synoptic s...

background: in order to check the practicality of classification of universities of medical sciences (umss) based on their infrastructures, and scientific contributions, this study explored the most appropriate indicators to measure the size and productivity of umss. methods: in the first phase, we approached a group of experts who had a deep experience in the management of umss and in the mini...

In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock sa...

The paper contains description of hybrid methods of face recognition which are based on independent component analysis, principal component analysis and rough set theory. The feature extraction and pattern forming from face images have been provided using Independent Component Analysis and Principal Component Analysis. The feature selection/reduction has been realized using the rough set techni...

Facial symmetry is obviously a useful natural characteristic of facial images, which will help develop face-oriented recognition technology and algorithms. This paper will apply it to face recognition after introducing mirror images. By combining PCA with the even-odd decomposition principle, a new algorithm called Symmetrical Principal Component Analysis is proposed, in which different energy ...

A class of image-matrix-based feature extraction algorithms has been discussed earlier. The correspondence argues that 2-D principal component analysis and Fisher linear discriminant (FLD) are equivalent to block-based PCA and FLD. In this correspondence, we point out that this statement is not rigorous.

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

An object-oriented mapping approach based on subspace analysis of airborne hyperspectral images was investigated in this paper. Hyperspectral features were extracted based on subspace learning approaches, in order to reduce the redundancy of spectral space and extract the characteristic images for the further object-oriented classification. In this paper, three kinds of spectral feature extract...

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experimen...