A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Curious George: An attentive semantic robot
Robotics and Autonomous Systems
Peripheral-foveal vision for real-time object recognition and tracking in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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One of the key competencies required in modern robots is finding objects in complex environments. For the last decade, significant progress in computer vision and machine learning literatures has increased the recognition performance of well localized objects. However, the performance of these techniques is still far from human performance, especially in cluttered environments. We believe that the performance gap between robots and humans is due in part to humans' use of an attention system. According to cognitive psychology, the human visual system uses two stages of visual processing to interpret visual input. The first stage is a pre-attentive process perceiving scenes fast and coarsely to select potentially interesting regions. The second stage is a more complex process analyzing the regions hypothesized in the previous stage. These two stages play an important role in enabling efficient use of the limited cognitive resources available. Inspired by this biological fact, we propose a visual attentional object categorization approach for robots that enables object recognition in real environments under a critical time limitation. We quantitatively evaluate the performance for recognition of objects in highly cluttered scenes without significant loss of detection rates across several experimental settings.