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
Object-based visual attention for computer vision
Artificial Intelligence
A probabilistic approach for the adaptive integration of multiple visual cues using an agent framework
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Indoor Mobile Robotics at Grima, PUC
Journal of Intelligent and Robotic Systems
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Detection of visual landmarks is an important problem in the development of automated, vision-based agents working on unstructured environments. In this paper, we present an unsupervised approach to select and to detect landmarks in images coming from a video stream. Our approach integrates three main visual mechanisms: attention, area segmentation, and landmark characterization. In particular, we demonstrate that an incorrect segmentation of a landmark produces severe problems in the next steps of the analysis, and that by using multiple segmentation algorithms we can greatly increase the robustness of the system. We test our approach with encouraging results in two image sets taken in real world scenarios. We obtained a significant 52% increase in recognition when using the multiple segmentation approach with respect to using single segmentation algorithms.