This paper introduces a momentum-like regularisation term for the region-based Mixture of Gaussians framework. Momentum term has long been used in machine learning, especially in backpropagation algorithms to improve the speed of convergence and subsequently their performance. Here, we prove the convergence of the online gradient method with a momentum term and apply it to background modelling by using it in the update equations of the region-based Mixture of Gaussians algorithm. It is then shown with the help of experimental evaluation on both simulated data and well known video sequences that these regularised updates help improve the performance of the algorithm.

The Richardson-Lucy algorithm is one of the most important in image deconvolution. However, a drawback is its slow convergence. A significant acceleration was obtained using the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the image processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the heavy-ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has a proof of the convergence rate of O(K(-2)), where k is the number of iterations. We demonstrate the superior convergence performance, by a speedup factor of five, of the scaled H-B method on both synthetic and real 3D images.

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1norm, we derived a new set of updating equations for l1regularized online MoG. It is shown empirically that l1regularized online MoG converge faster than the original online MoG.

This paper describes the anatomy of a pilot surveillance system with a speech-based interface for content-based retrieval of video data. The proposed system relies on an ontology-based information sharing architecture and lets components of the system communicate among each other through TCP/IP communication channels. The aim of developing the pilot system was to explore dependencies between image analysis, event detection, video annotation, and speech- based retrieval of the video content in the context of a broader spoken dialogue system.

An automated unsupervised technique, based upon a Bayesian framework, for the segmentation of low light level imagery is proposed. Primarily, Mixture Modelling is used to provide a baseline estimate. This estimate is then refined to consider spatial correlations using Markov Random Field (MRF) Modelling. The technique has been implemented assuming low-light level Poisson statistics, and the results compared to the more widely used assumption of Gaussian statistics. Investigations revealed the Poisson technique quantitatively outperforms the Gaussian technique for synthetic low light imagery, both before and after avalanche multiplication, via the multiplication register of a Low-light Level Charge Coupled Device (L3CCD). The technique was then applied to the task of segmenting a biomedical dataset obtained from a L3CCD. Qualitative results were promising, again showing improvement over the Gaussian technique. 1

An independent moving objection detection system is described which uses a colour background model for a nonstationary sensor. Over time the RGB distribution of a static point in a scene, observed from a static or moving camera, is cylindrical rather than spherical and is modelled as such. The background model is recursively updated over time and is robustly aligned to the most recent frame. Discrepancies between the current observed frame and the background model are considered foreground events and are determined using a classification framework which combines per-pixel colour and brightness differences. In principle the colour (RGB) images provide greater discriminative power than greyscale (brightness). However for several sequences tested, which are of road scenes from an airborne sensor, it was found that the colour information did not improve the classification of foreground pixels significantly.

We present a novel visual representation, called local co-occurring patterns (LCPs), which consists of characteristic local features and the statistical co-occurance relations between them. The LCPs can be discovered using an associate rule mining algorithm. Experiments show that LCPs widely exist in a large image corpus, and are more discriminant than individual local features in visual categorization tasks such as subcategory and face recognition. Furthermore, state-of-the-art categorization performance was achieved on two test data-sets.