A Deep-structured Conditional Random Field Model for Object Silhouette Tracking

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.Comment: 17 page

A context-based navigation paradigm for accessing web data.

This paper presents a context-based navigation paradigm, so as to overcome the phenomenon of user disorientation in a Web environment. Conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. Consequently, orientation is improved through reduced cognitive overhead and an increased sense of document coherence.Information; Requirements; Cognitive;

A general framework for positioning, evaluating and selecting the new generation of development tools.

This paper focuses on the evaluation and positioning of a new generation of development tools containing subtools (report generators, browsers, debuggers, GUI-builders, ...) and programming languages that are designed to work together and have a common graphical user interface and are therefore called environments. Several trends in IT have led to a pluriform range of developments tools that can be classified in numerous categories. Examples are: object-oriented tools, GUI-tools, upper- and lower CASE-tools, client/server tools and 4GL environments. This classification does not sufficiently cover the tools subject in this paper for the simple reason that only one criterion is used to distinguish them. Modern visual development environments often fit in several categories because to a certain extent, several criteria can be applied to evaluate them. In this study, we will offer a broad classification scheme with which tools can be positioned and which can be refined through further research.

Buzz monitoring in word space

This paper discusses the task of tracking mentions of some topically interesting textual entity from a continuously and dynamically changing flow of text, such as a news feed, the output from an Internet crawler or a similar text source - a task sometimes referred to as buzz monitoring. Standard approaches from the field of information access for identifying salient textual entities are reviewed, and it is argued that the dynamics of buzz monitoring calls for more accomplished analysis mechanisms than the typical text analysis tools provide today. The notion of word space is introduced, and it is argued that word spaces can be used to select the most salient markers for topicality, find associations those observations engender, and that they constitute an attractive foundation for building a representation well suited for the tracking and monitoring of mentions of the entity under consideration