classical lbp such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. in this paper, we introduce an improved lbp algorithm to solve these problems that utilizes fast pca algorithm for reduction of vector dimensions of extracted features. in other words, proffer method (fast pca+lbp) is an improved lbp algorithm that is extracted ...

the karnafully is one of the most important rivers due to its profound influence on water chemistry and sediment characteristics. the present study intended to assess the quality of water and sediment from intertidal zone of this river in respect to the pollution index. seasonal water and sediment samples were collected during four seasons (monsoon, post-monsoon, winter, and pre-monsoon) of 201...

In order to effectively improve fusion quality, a novel multi-focus image fusion approach with sparse decomposition is proposed. The source images are decomposed into principal and sparse components by robust principal component analysis (RPCA) decomposition. A sliding window technique is applied to inhibiting blocking artifacts. The focused pixels of the source images are detected by using the...

Background recovery is a very important theme in computer vision applications. Recent research shows that robust principal component analysis (RPCA) is a promising approach for solving problems such as noise removal, video background modeling, and removal of shadows and specularity. RPCA utilizes the fact that the background is common in multiple views of a scene, and attempts to decompose the ...

Detection of salient object regions is useful for many vision tasks. Recently, a variety of saliency detection models have been proposed. They often behave differently over an individual image, and these saliency detection results often complement each other. To make full use of the advantages of the existing saliency detection methods, in this paper, we propose a salience learning model which ...

Video background recovery is a very important task in computer vision applications. Recent research offers robust principal component analysis (RPCA) as a promising approach for solving video background recovery. RPCA works by decomposing a data matrix into a low-rank matrix and a sparse matrix. Our previous work shows that when the desired rank of the low-rank matrix is known, fixing the rank ...

Principal Component Analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. In recent research, several robust PCA algorithms were presented to enhance the robustness of PCA model. However, the existing robust PCA methods incorrectly center the data using the `2-norm distance to calculate the mean, which actually is not the optimal mean due to the `1-norm used in ...

where Ωt is a selection operator that specifies the observable subset of entries at time t, at ∈ R r are the coefficients specifying the linear combination of the columns of Ut, and st ∈ R n is a sparse outlier vector. When the subspace Ut is stationary, we drop the subscript t from Ut and the problem reduces to robust matrix completion or robust principal component analysis where the task is t...

Principal component analysis (PCA) is one of the most widely used multivariate techniques in statistics. It is commonly used to reduce the dimensionality of data in order to examine its underlying structure and the covariance/correlation structure of a set of variables. While singular value decomposition provides a simple means for identification of the principal components (PCs) for classical ...

X-ray coronary angiography can provide rich dynamic information of cardiac and vascular function. Extracting contrast-filled vessel from the complex dynamic background (caused by the movement of diaphragm, lung, bones, etc.) in X-ray coronary angiograms has great clinical significance in assisting myocardial perfusion evaluation, reconstructing vessel structures for diagnosis and treatment of h...