: Dynamic clustering for tracking evolving environments

Evolving Triggers for Dynamic Environments

In this work we address the problem of managing the reactive behavior in distributed environments in which data continuously changes over time, where the users may need to explicitly express how the triggers should be (self) modified. To enable this we propose the (ECA) – Evolving and Context-Aware Event-Condition-Action paradigm for specifying triggers that capture the desired reactive behavio...

Motion-based Tracking for Dynamic, Unstructured Environments

The use of robots in dynamic and unstructured environments poses diicult challenges for machine vision. The vision systems for such robots must report changes in the world quickly and must deal with a vast range of objects. In this paper, we describe a vision system that makes use of motion to pick out and track moving objects without requiring information about their shape. We have implemented...

Clustering-Based Hierarchical Radiosity for Dynamic Environments

This paper extends hierarchical radiosity and clustering techniques to dynamic environments, in which dynamic manipulations, such as repositioning an object and changing surface attributes, are repeatedly applied. The cluster techniques can be effective in reducing the computational complexity of both initial linking creation and link updating of dynamic manipulations. When the object is reposi...

Robust Clustering for Tracking Noisy Evolving Data Streams

We present a new approach for tracking evolving and noisy data streams by estimating clusters based on density, while taking into account the possibility of the presence of an unknown amount of outliers, the emergence of new patterns, and the forgetting of old patterns. keywords: evolving data streams, robust clustering, dynamic clustering, stream clustering, scalable clustering

Tracking Optima in Dynamic Environments