Hardware acceleration of the trace transform for vision applications

Computer Vision is a rapidly developing field in which machines process visual data to extract meaningful information. Digitised images in their pixels and bits serve no purpose of their own. It is only by interpreting the data, and extracting higher level information that a scene can be understood. The algorithms that enable this process are often complex, and data-intensive, limiting the processing rate when implemented in software. Hardware-accelerated implementations provide a significant performance boost that can enable real- time processing. The Trace Transform is a newly proposed algorithm that has been proven effective in image categorisation and recognition tasks. It is flexibly defined allowing the mathematical details to be tailored to the target application. However, it is highly computationally intensive, which limits its applications. Modern heterogeneous FPGAs provide an ideal platform for accelerating the Trace transform for real-time performance, while also allowing an element of flexibility, which highly suits the generality of the Trace transform. This thesis details the implementation of an extensible Trace transform architecture for vision applications, before extending this architecture to a full flexible platform suited to the exploration of Trace transform applications. As part of the work presented, a general set of architectures for large-windowed median and weighted median filters are presented as required for a number of Trace transform implementations. Finally an acceleration of Pseudo 2-Dimensional Hidden Markov Model decoding, usable in a person detection system, is presented. Such a system can be used to extract frames of interest from a video sequence, to be subsequently processed by the Trace transform. All these architectures emphasise the need for considered, platform-driven design in achieving maximum performance through hardware acceleration

3D Cylindrical Trace Transform based feature extraction for effective human action classification

Human action recognition is currently one of the hottest areas in pattern recognition and machine intelligence. Its applications vary from console and exertion gaming and human computer interaction to automated surveillance and assistive environments. In this paper, we present a novel feature extraction method for action recognition, extending the capabilities of the Trace transform to the 3D domain. We define the notion of a 3D form of the Trace transform on discrete volumes extracted from spatio-temporal image sequences. On a second level, we propose the combination of the novel transform, named 3D Cylindrical Trace Transform, with Selective Spatio-Temporal Interest Points, in a feature extraction scheme called Volumetric Triple Features, which manages to capture the valuable geometrical distribution of interest points in spatio-temporal sequences and to give prominence to their action-discriminant geometrical correlations. The technique provides noise robust, distortion invariant and temporally sensitive features for the classification of human actions. Experiments on different challenging action recognition datasets provided impressive results indicating the efficiency of the proposed transform and of the overall proposed scheme for the specific task

Unfolding the Hierarchy of Voids

We present a framework for the hierarchical identification and characterization of voids based on the Watershed Void Finder. The Hierarchical Void Finder is based on a generalization of the scale space of a density field invoked in order to trace the hierarchical nature and structure of cosmological voids. At each level of the hierarchy, the watershed transform is used to identify the voids at that particular scale. By identifying the overlapping regions between watershed basins in adjacent levels, the hierarchical void tree is constructed. Applications on a hierarchical Voronoi model and on a set of cosmological simulations illustrate its potential.Comment: 5 pages, 2 figure

Markovian dynamics of concurrent systems

Monoid actions of trace monoids over finite sets are powerful models of concurrent systems---for instance they encompass the class of 1-safe Petri nets. We characterise Markov measures attached to concurrent systems by finitely many parameters with suitable normalisation conditions. These conditions involve polynomials related to the combinatorics of the monoid and of the monoid action. These parameters generalise to concurrent systems the coefficients of the transition matrix of a Markov chain. A natural problem is the existence of the uniform measure for every concurrent system. We prove this existence under an irreducibility condition. The uniform measure of a concurrent system is characterised by a real number, the characteristic root of the action, and a function of pairs of states, the Parry cocyle. A new combinatorial inversion formula allows to identify a polynomial of which the characteristic root is the smallest positive root. Examples based on simple combinatorial tilings are studied.Comment: 35 pages, 6 figures, 33 reference