Tracking in a space variant active vision system

Without the ability to foveate on and maintain foveation, active vision for applications such as surveillance, object recognition and object tracking are difficult to build. Although foveation in cartesian coordinates is being actively pursued by many, multi-resolution high accuracy foveation in log polar space has not been given much attention. This paper addresses the use of foveation to track a single object as well as multiple objects for a simulated space variant active vision system. Complex logarithmic mapping is chosen firstly because it provides high resolution and wide angle viewing. Secondly, the spatially variant structure of log polar space leads to an object increasing in size as it moves towards the fovea. This is important as we know which object is closer to the fovea at any instant in time.<br /

MULTILEVEL GOVERNANCE IN EUROPEAN RIVER BASINS: CHALLENGES IN THE INTEGRATION OF ADAPTATION, DISASTER RESPONSE, AND RESILIENCE

This dissertation examines some of the strengths and weaknesses in basin level governance particularly as it relates to three current policy priorities: adaptive governance, international frameworks for response to natural and man-made disasters, and resilience in integrated water resources management. While these priorities are well-established in the academic and policy literature, in practice the ability to implement them at multiple levels has proven challenging. Though my dissertation highlights these challenges using case studies of European river basins, the observations and lessons for improving integrated management at multiple levels of governance, in multiple sectors, and among various actors are more broadly relevant to other natural resource governance settings. The first paper of this dissertation explores adaptive governance in the Tisza sub-basin, considering both constraints and policy options for strengthening adaptive governance at the sub-basin level. The Tisza is the largest sub-basin to the Danube River basin, and faces increasing pressures exacerbated by climate change. The Tisza countries have experienced challenges with managing climate change adaptation in a nested, consistent, and effective manner pursuant to the European Union Water Framework Directive. This is due, in part, to inefficiencies in climate change adaptation, such as weakened vertical coordination. This paper examines the conceptual domains relating to adaptation in international governance, and adaptation in transboundary water management in particular, with a focus on multilevel governance. International laws and policies governing transboundary waters in the Danube basin and Tisza sub-basin are reviewed. Using interviews and document analysis, the paper highlights challenges to adaptation in the Tisza sub-basin, including policy, fiscal, institutional, and capacity. The paper concludes with an exploration of possible policy options for sub-basin management, such as the development of a sub-basin commission, the establishment of a permanent Tisza expert group to be housed at and coordinated by the ICPDR, the use of new or existing bilateral treaties, and designing a framework for managing the Tisza. The second paper analyzes the transition in international frameworks of response to natural and man-made disasters as incorporated and integrated at multiple levels of governance. It begins with a discussion of the distinctions between so-called “natural” disasters and “man-made” accidents, how and why they are treated differently, and how recent developments in international law and practice are raising questions about the merits of these historic distinctions. Anthropogenic climate change drives more extreme and sometimes cascading disasters that require complex and overlapping types of response; it is argued that the distinctions in response to natural and man-made disasters are counterproductive, outdated, and ultimately flawed. The paper examines the policy and institutional frameworks governing response to natural disasters and man-made accidents in the Danube River basin and Tisza River sub-basin. Using expert interviews and legal and policy analysis, it then explores the differences in how natural disasters and man-made accidents are monitored and how they are responded to. The paper concludes with an analysis of the implications of transitioning policies toward a more holistic framework for response, regardless of whether the cause is natural, man-made, or (as is increasingly the case) some combination. The third paper advances the concept of a new approach – resilient IWRM – and how this approach can be applied to the management practices of the Danube and Rhine River basins and other river basins around the world. Using the Sendai Framework for Disaster Risk Reduction, the leading framework for resilience, and supported by expert interviews, the paper analyzes what resilience measures have been addressed, and what gaps remain in the basin management frameworks of the Danube and Rhine River basins. The paper concludes with a discussion of the current constraints in the resilient IWRM framework of the Danube and Rhine River basins, in addition to options for overcoming these challenges. This dissertation concludes with a discussion of crosscutting dimensions of analysis, specifically the challenges faced in integrating climate change adaptation, response to natural and man-made disasters, and resilience into multiple levels of water governance. While these conceptual elements are well-established, the ability to operationalize these elements has proven difficult from multiple perspectives highlighted in this dissertation. The difficulties suggest a more nuanced and pragmatic approach to both their framing and their operationalization

Estimation of instrinsic dimension via clustering

The problem of estimating the intrinsic dimension of a set of points in high dimensional space is a critical issue for a wide range of disciplines, including genomics, finance, and networking. Current estimation techniques are dependent on either the ambient or intrinsic dimension in terms of computational complexity, which may cause these methods to become intractable for large data sets. In this paper, we present a clustering-based methodology that exploits the inherent self-similarity of data to efficiently estimate the intrinsic dimension of a set of points. When the data satisfies a specified general clustering condition, we prove that the estimated dimension approaches the true Hausdorff dimension. Experiments show that the clustering-based approach allows for more efficient and accurate intrinsic dimension estimation compared with all prior techniques, even when the data does not conform to obvious self-similarity structure. Finally, we present empirical results which show the clustering-based estimation allows for a natural partitioning of the data points that lie on separate manifolds of varying intrinsic dimension

Interest points harvesting in video sequences for efficient person identification

International audienceWe propose and evaluate a new approach for identification of persons, based on harvesting of interest point descriptors in video sequences. By accumulating interest points on several sufficiently time-spaced images during person silhouette or face tracking within each camera, the collected interest points capture appearance variability. Our method can in particular be applied to global person re-identification in a network of cameras. We present a first experimental evaluation conducted on a publicly available set of videos in a commercial mall, with very promising inter-camera pedestrian reidentification performances (a precision of 82% for a recall of 78%). Our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making re-identification among hundreds of persons computationally feasible in less than ~ 1/5s second. Finally, we also present a first feasibility test for on-the-fly face recognition, with encouraging results

Real-time motion data annotation via action string

Even though there is an explosive growth of motion capture data, there is still a lack of efficient and reliable methods to automatically annotate all the motions in a database. Moreover, because of the popularity of mocap devices in home entertainment systems, real-time human motion annotation or recognition becomes more and more imperative. This paper presents a new motion annotation method that achieves both the aforementioned two targets at the same time. It uses a probabilistic pose feature based on the Gaussian Mixture Model to represent each pose. After training a clustered pose feature model, a motion clip could be represented as an action string. Then, a dynamic programming-based string matching method is introduced to compare the differences between action strings. Finally, in order to achieve the real-time target, we construct a hierarchical action string structure to quickly label each given action string. The experimental results demonstrate the efficacy and efficiency of our method