Artificial Intelligence
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Dynamic fixation and active perception
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
Fixation simplifies 3D motion estimation
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Beta-measure for probabilistic segmentation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Extracting detected salient object by active segmentation
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Incrementally biasing visual search using natural language input
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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The semantic robots of the immediate future are robots that will be able to find and recognize objects in any environment. They need the capability of segmenting objects in their visual field. In this paper, we propose a novel approach to segmentation based on the operation of fixation by an active observer. Our approach is different from current approaches: while existing works attempt to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Furthermore, our solution integrates monocular cues (color, texture) with binocular cues (stereo disparities and optical flow). Experiments with real imagery collected by our active robot and from the known databases [1] demonstrate the promise of the approach.