Research Projects:

Modeling the role of salience in the allocation of overt visual attention
Developing a model of visual selective attention for dynamic natural scenes
Testing real-time models of overt shifts of visual attention
Developing attention-based video compression
Conducting psychophysical experiments on the Internet



Modeling the role of salience in the allocation of overt visual attention


A biologically motivated computational model of bottom-up visual selective attention was used to examine the degree to which stimulus salience guides the allocation of attention. Human eye movements were recorded while participants viewed a series of digitized images of complex natural and artificial scenes. Stimulus dependence of attention, as measured by the correlation between computed stimulus salience and fixation locations, was found to be significantly greater than that expected by chance alone and furthermore was greatest for eye movements that immediately follow stimulus onset. The ability to guide attention of three modeled stimulus features (color, intensity and orientation) was examined and found to vary with image type. Additionally, the effect of the drop in visual sensitivity as a function of eccentricity on stimulus salience was examined, modeled, and shown to be an important determiner of attentional allocation. Overall, the results indicate that stimulus-driven, bottom-up mechanisms contribute significantly to attentional guidance under natural viewing conditions.

The following paper describes this research:

Parkhurst, Law, and Niebur (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107-123.





Developing a model of visual selective attention for dynamic natural scenes


We are developing a computational model of stimulus-driven visual selective attention based on what is known about visual information processing in the primate visual system. The model has the ability to make predictions for attentional allocation in static and dynamic natural scenes. The model calculates stimulus salience based on color, intensity, orientation and motion. A schematic diagram of the model architecture is shown to the right.

Click on the images below to display an MPEG video sequence of a swinging pendulum and the resulting dynamic salience map generated by the model.


A description of our progress and results can be found in the following dissertation:

Parkhurst, D. (2002). Selective attention in natural vision: Using computational models to quantify stimulus-driven attentional allocation. Ph.D. thesis, The Johns Hopkins University, Baltimore, MD. [PDF] [PS]

People involved:
Derrick Parkhurst
Ernst Niebur





Testing real-time models of overt shifts of visual attention


We are currently investigating the mechanisms responsible for generating overt attentional shifts (i.e. eye movements) and covert attentional shifts within a salience map representation. To address this question we have implemented a real-time model of visual selective attention that takes input from a webcam (Logitech Quickcam), runs in real time (15-30 fps), and generates eye movements. These eye movements are used to pan and tilt the camera (the camera is mounted on a TrackerPod device) so that the camera actively tracks visually salient stimuli. Using simplified, real-time models are especially useful in testing different attentional mechanisms with real world stimuli.


People involved:
Derrick Parkhurst
Ernst Niebur




Developing attention-based video compression


We are currently investigating a number of variable-resolution display techniques in an MPEG-4 video compression application that take advantage of the basic fact that normal viewers only attend to relatively small portions of natural images at any one time. We use a computational model of visual selective attention to predict where in each video frame that viewers are likely to fixate and we maintain high resolution at those locations and reduce the resolution at all other locations. We are currently examining the degree of compression that is obtainable using this technique by comparing visual quality estimates and eye movement measures obtained when human participants view compressed and non-compressed video sequences.

People involved:
Derrick Parkhurst
Joey Baick
Ernst Niebur




Conducting psychophysical experiments on the Internet


We are interested in how people allocate attention when viewing natural scenes. To examine this question, we have designed a number of on-line psychophysical experiments where people can participate over the Internet. These experiments utilize the participant's own browser to display visual stimuli and record their responses. You can participate in one of our experiments by going to
this page.

People involved:
Derrick Parkhurst
Ernst Niebur
Chinwendu Okoronkwo
Christian Sutton
Jennifer Cox




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