Robust Principal Component Analysis (RPCA) has generated a great amount of interest for background/foreground estimation in videos. The central hypothesis in this setting is that a video’s background can be well-represented by a low-rank model. However, in the presence of complex lighting conditions this model is only accurate in localised spatio-temporal regions. Following this observation, we...

Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on maximum likelihood procedure provides a manner to implement dimension reduction. Recently, bilinear PPCA (BPPCA) model, which assumes that noise terms follow matrix variate Gaussian distributions, has been introduced directly deal with two-dime...

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...

Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications data mining and machine learning, has shown improved background performance. Unfortunately, The traditional RPCA method considers the batch recovery all samples, leads higher storage cost. This paper pro...

The strong background radiation in high enthalpy hypersonic shock tunnels has posed severe challenges for measurement using luminescent coatings. We proposed a solution reducing from time-resolved temperature-sensitive paint (TSP) flow with Ma = 6.5 and T0 3525 K. TSP was applied on an inlet ramp model, the images were taken by high-speed camera at 2 kHz under modulated excitation. led to low s...

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 ...

Clouds, together with their shadows, usually occlude ground-cover features in optical remote sensing images. This hinders the utilization of these images for a range applications such as earth observation, land-cover classification and urban planning. In this work, we propose deep unfolded prior-aided robust principal component analysis (DUPA-RPCA) network removing clouds recovering information...

The robust principal component analysis (RPCA) decomposes a data matrix into low-rank part and sparse part. There are mainly two types of algorithms for RPCA. first type algorithm applies regularization terms on the singular values to obtain matrix. However, calculating can be very expensive large matrices. second replaces as multiplication small They faster than because no value decomposition ...