Sharif Digital Repository

Sparse representation is a new approach that has received significant attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse representation and classification of universal facial expressions. In our method, expressive facials images of each subject are subtracted from a neutral facial image of the same subject. Then the PCA is applied to these difference images to model the variations within each class of facial expressions. The learned principal components are used as the atoms of the dictionary. In the classification step, a given test image is sparsely represented as a linear combination of the principal components of six basic... 

Aims. We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We used our data to search for variable stars in this cluster and refine the periods of known variables; we then used our variable star light curves to derive values for the cluster's parameters. Methods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster parameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate was possible. We also derived an estimate for the age of the cluster by fitting theoretical... 

Two previously unknown variable stars in the crowded central region of the globular cluster NGC 6981 are presented. The observations were made using the electron multiplying CCD (EMCCD) camera at the Danish 1.54 m Telescope at La Silla, Chile. The two variableswere not previously detected by conventional CCD imaging because of their proximity to a bright star. This discovery demonstrates that EMCCDs are a powerful tool for performing high-precision time-series photometry in crowded fields and near bright stars, especially when combined with difference image analysis  

Aims: We present VRi photometric observations of the quadruply imaged quasarHE0435-1223, carried out with the Danish 1.54 m telescope at the La Silla Observatory. Our aim was to monitor and study the magnitudes and colors of each lensed component as a function of time. Methods. We monitored the object during two seasons (2008 and 2009) in the VRi spectral bands, and reduced the data with two independent techniques: difference imaging and point spread function (PSF) fitting. Results. Between these two seasons, our results show an evident decrease in flux by ≈ 0.2-0.4 magnitudes of the four lensed components in the three filters. We also found a significant increase (≈ 0.05-0.015) in their V -... 

Aims. In this paper, we present the analysis of time-series observations from 2013 and 2014 of five metal-rich ([Fe/H] > -1) globular clusters: NGC 6388, NGC 6441, NGC 6528, NGC 6638, and NGC 6652. The data have been used to perform a census of the variable stars in the central parts of these clusters. Methods. The observations were made with the electron-multiplying charge-couple device (EMCCD) camera at the Danish 1.54m Telescope at La Silla, Chile, and they were analysed using difference image analysis to obtain high-precision light curves of the variable stars. Results. It was possible to identify and classify all of the previously known or suspected variable stars in the central regions... 

Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, including number of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some real videos from Tehran roads. The results show high accuracy performance of our method, even in changes of environmental conditions (e.g., lighting), by using efficient... 

Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based...