Background subtraction is one of the most commonly used components in machine vision systems. Despite the numerous algorithms proposed in the literature and used in practical applications, key challenges remain in designing a single system that can handle diverse environmental conditions. In this paper we present Multiple Background Model based Background Subtraction Algorithm as such a candida...

A PCA based gait segmentation method is proposed. Background images are used for PCA training. PCA reconstruction, recursive error compensation and single threshold based method is put forward for gait segmentation. Single threshold based method has the same gait segmentation ability as classical adaptive threshold based method, and the former is faster in implementation than the later. Propose...

Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a perpixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adj...

In a large social media corpus (2.9 million tokens), we analyze Flemish adolescents’ non-standard writing practices and look for correlations with the teenagers’ social class. Three different aspects of adolescents’ social background are included: educational track, parental profession, and home language. Since the data reveal that these parameters are highly correlated, we combine them into on...

The Cosmic Microwave Background (CMB) is an abundant source of cosmological information. However, this information is encoded in non-trivial ways in a signal that is difficult to observe. The resulting challenges in extracting this information from CMB data sets have created a new frontier. In this talk I will discuss the challenges of CMB data analysis. I review what cosmological information i...

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...