Eye movements in reading: perceptual and language processes
Eye movements in reading: perceptual and language processes
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Vision, instruction, and action
Vision, instruction, and action
A multiscale model of adaptation and spatial vision for realistic image display
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended tasks elicit complex eye movement patterns
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combining attention and recognition for rapid scene analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
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
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Applying computational tools to predict gaze direction in interactive visual environments
ACM Transactions on Applied Perception (TAP)
Memory representations in natural tasks
Journal of Cognitive Neuroscience
Eye movement analysis for activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Multimodal recognition of reading activity in transit using body-worn sensors
ACM Transactions on Applied Perception (TAP)
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Visual perception is an inherently selective process. To understand when and why a particular region of a scene is selected, it is imperative to observe and describe the eye movements of individuals as they go about performing specific tasks. In this sense, vision is an active process that integrates scene properties with specific, goal-oriented oculomotor behavior. This study is an investigation of how task influences the visual selection of stimuli from a scene. Four eye tracking experiments were designed and conducted to determine how everyday tasks affect oculomotor behavior. A portable eyetracker was created for the specific purpose of bringing the experiments out of the laboratory and into the real world, where natural behavior is most likely to occur. The experiments provide evidence that the human visual system is not a passive collector of salient environemental stimuli, nor is vision general-purpose. Rather, vision is active and specific, tightly coupled to the requirements of a task and a plan of action. The experiments support the hypothesis that the purpose of selective attention is to maximize task efficiency by fixating relevant objects in the scene. A computational model of visual attention is presented that imposes a high-level constraint on the bottom-up salient properties of a scene for the purpose of locating regions that are likely to correspond to foreground objects rather than background or other salient nonobject stimuli. In addition to improving the correlation to human subject fixation densities over a strictly bottom-up model [Itti et al. 1998; Parkhurst et al. 2002], this model predicts a central fixation tendency when that tendency is warranted, and not as an artificially primed location bias.