Artur Loza

Non-Gaussian model-based fusion of noisy images in the wavelet domain

This paper describes a new methodology for multimodal image fusion based on non-Gaussian statistical modelling of wavelet coefficients. Special emphasis is placed on the fusion of noisy images. The use of families of generalised Gaussian and alpha-stable distributions for modelling image wavelet coefficients is investigated and methods for estimating distribution parameters are proposed. Improved techniques for image fusion are developed, by incorporating these models into a weighted average image fusion algorithm. The proposed method has been shown to perform very well with both noisy and noise-free images from multimodal datasets, outperforming conventional methods in terms of fusion quality and noise reduction in the fused output.

Structural Similarity-Based Object Tracking in Video Sequences

This paper addresses the problem of object tracking in video sequences. The use of a structural similarity measure for tracking is proposed. The measure reflects the distance between two images by comparing their structural and spatial characteristics and has shown to be robust to illumination and contrast changes. As a result it guarantees robustness of the tracking process under changes in the environment. The previously used Bhattacharyya distance is not robust to such changes. Additionally, when a tracker is run with the Bhattacharyya distance, histograms should be calculated in order to find the likelihood function of the measurements. With the new function there is no need to calculate histograms. A particle filter (PF) is implemented where this measure is used for computing the distance between the reference and current frame. The algorithm performance has been tested and evaluated over real-world video sequences, and has been shown to outperform methods based on colour and edge histograms

The Effect of Pixel-Level Fusion on Object Tracking in Multi-Sensor Surveillance Video

This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been accomplished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR sequences, whereas the VIZ video showed the worst tracking performance due to higher levels of clutter. However, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to accurately assess how fusion affects the performance of the tracker.

PageRank Tracker: From Ranking to Tracking

Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability.

Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients

This paper describes a new method for contrast enhancement in images of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means of a shrinkage function, thus preventing noise amplification. The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy images, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output image.

Statistical Modelling for Wavelet Domain Image Fusion

This chapter describes a new methodology for multimodal image fusion based on non-Gaussian statistical modelling of wavelet coefficients of the input images. The use of families of generalised Gaussian and alpha-stable distributions for modelling image wavelet coefficients is investigated and methods for estimating distribution parameters are proposed. Improved techniques for image fusion are developed, by incorporating these models into the weighted average image fusion algorithm. The superior performance of the proposed methods is demonstrated using visual and infrared light image datasets.

Crowd modeling framework using fast head detection and shape-aware matching

Abstract. A framework for crowd modeling using a combination of multiple kernel learning (MKL)-based fast head detection and shape-aware matching is proposed. First, the MKL technique is used to train a classifier for head detection using a combination of the histogram of oriented gradient and local binary patterns feature sets. Further, the head detection process is accelerated by implementing the classification procedure only at those spatial locations in the image where the gradient points overlap with moving objects. Such moving objects are determined using an adaptive background subtraction technique. Finally, the crowd is modeled as a deformable shape through connected boundary points (head detection) and matched with the subsequent detection from the next frame in a shape-aware manner. Experimental results obtained from crowded videos show that the proposed framework, while being characterized by a low computation load, performs better than other state-of-art techniques and results in reliable crowd modeling.

Advanced algorithms for real-time video tracking with multiple targets

This paper investigates combinatorial and probabilistic approaches to real-time video target tracking. Of special interest are real-world scenarios, in which the presence of multiple targets and complex background pose a non-trivial challenge to automated trackers. Object tracking in an exemplary surveillance video sequence is accomplished by means of selected visual tracking techniques, based on two families of methods, combinatorial data association and Particle Filters. Based on the detailed analysis of the performance of the trackers tested, the advantages, complementary failure modes and computational requirements of each method have been identified. Taking into account the results obtained, the hybrid strategy for improved tracking performance is suggested, bringing together the best complementary features of the different tracking methods.

Video object tracking with differential Structural SIMilarity index

The Structural SIMilarity Measure (SSIM) combined with the sequential Monte Carlo approach has been shown [1] to achieve more reliable video object tracking performance, compared with similar methods based on colour and edge histograms and Bhattacharyya distance. However, the combined use of the structural similarity and a particle filter results in increased computational complexity of the algorithm. In this paper, a novel fast approach for video tracking based on the structural similarity measure is presented. The tracking algorithm proposed determines the state of the target (location, size) based on the gradient ascent procedure applied to the structural similarity surface of the video frame, thus avoiding computationally expensive sampling of the state space. The new method, while being computationally less expensive, has shown higher accuracy compared with the standard mean shift algorithm and the SSIM Particle Filter (SSIM-PF) [1] and its performance is illustrated over real video sequences.

Statistical Model-based fusion of noisy multi-band images in the wavelet domain

A new method for multimodal image fusion, based on statistical modelling of wavelet coefficients, is proposed in this paper. The algorithm draws from the weighted average scheme, but incorporates Laplacian bivariate parent-child statistical dependencies. The interscale dependency is brought in the form of shrinkage functions. The proposed method has been shown to perform very well with noisy datasets, outperforming other conventional methods in terms of fusion quality and noise reduction in the fused output.