Principal Component Analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating...

Images captured by a camera through glass often have reflection superimposed on the transmitted background. Among existing methods for reflection separation, multi-view methods are the most convenient to apply because they require the user to just take multiple images of a scene at varying viewing angles. Some of these methods are restricted to the simple case where the background scene and ref...

In the presence of outliers, the existing self-organizing rules for Principal Component Analysis (PCA) perform poorly. Using statistical physics techniques including the Gibbs distribution, binary decision fields and effective energies, we propose self-organizing PCA rules which are capable of resisting outliers while fulfilling various PCA-related tasks such as obtaining the first principal co...

Infrared target tracking plays an important role in both civil and military fields. The main challenges designing a robust high-precision tracker for infrared sequences include overlap, occlusion, appearance change. To this end, paper proposes based on the proximal principal component analysis method. Firstly, observation matrix is decomposed into sparse occlusion low-rank matrix, constraint op...

Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only sl...