Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition: fundamentals and case studies
Object recognition: fundamentals and case studies
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional Strategies for Object Recognition
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
An attentive, multi-modal laser "eye"
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
ACM SIGGRAPH 2005 Papers
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In this paper, we present a new recognition system for the fast detection and classification of objects in spatial 3D data. The system consists of two main components: A biologically motivated attention system and a fast classifier. Input is provided by a 3D laser scanner, mounted on an autonomous mobile robot, that acquires illumination independent range and reflectance data. These are rendered into images and fed into the attention system that detects regions of potential interest. The classifier is applied only to a region of interest, yielding a significantly faster classification that requires only 30% of the time of an exhaustive search. Furthermore, both the attention and the classification system benefit from the fusion of the bi-modal data, considering more object properties for the detection of regions of interest and a lower false detection rate in classification.