Artificial Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blackboard Architectures and Applications
Blackboard Architectures and Applications
An Extended Data-Flow Architecture for Data Analysis and Visualization
VIS '95 Proceedings of the 6th conference on Visualization '95
An XML Based Framework for Cognitive Vision Architectures
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Building Modular Vision Systems with a Graphical Plugin Environment
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Robotics and Autonomous Systems
A Novel Hierarchical Framework for Object-Based Visual Attention
Attention in Cognitive Systems
Enhancing robustness of a saliency-based attention system for driver assistance
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A language for formal design of embedded intelligence research systems
Robotics and Autonomous Systems
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A scene exploration which is quick and complete according to current task is the foundation for most higher scene processing. Many specialized approaches exist in the driver assistance domain (e.g. car recognition or lane marking detection), but we aim at an integrated system, combining several such techniques to achieve sufficient performance. In this work we present a novel approach to this integration problem. Algorithms are contained in hierarchically arranged layers with the main principle that the ordering is induced by the requirement that each layer depends only on the layers below. Thus, higher layers can be added to a running system (incremental composition) and shutdown or failure of higher layers leaves the system in an operational state, albeit with reduced functionality (graceful degradation). Assumptions, challenges and benefits when applying this approach to practical systems are discussed. We demonstrate our approach on an integrated system performing visual scene exploration on real-world data from a prototype vehicle. System performance is evaluated on two scene exploration completeness measures and shown to gracefully degrade as several layers are removed and to fully recover as these layers are restarted while the system is running.