Research Overview

measurements from living systems. The brain subsystem towards which most of our efforts are focused is the visual system. Our modeling efforts range from fairly detailed models of small neuronal circuits, such as a single hypercolumn of orientation-selective neurons in primary visual cortex, to large-scale models embodying several million highly-simplified neurons to explore mechanisms of visual attention, gaze control, object recognition, and goal-oriented scene understanding. Further, we strive to employ modeling principles which are mathematically optimal in some task- and goal-dependent sense. Thus, we are interested in investigating the tasks and conditions for which the biological brain approaches the theoretical limits of information processing.

research, where we highly accurately monitor the gaze of human participants to provide an implicit behavioral response, in complement to possible explicit responses such as pressing a response button. A second experimental focus is to employ in vivo functional neuroimaging techniques to correlate brain activity to psychophysical performance, for example using functional magnetic resonance imaging (fMRI) to measure local changes in brain blood oxygenation correlated with mental activity. This neuroimaging focus is interested not only in the basic science of normal brain function, but also in the medical investigation of how such function may be altered in disease conditions. Finally, a third upcoming experimental focus is to employ electrophysiological recording to probe the activity of single neurons or small groups of neurons in the living monkey brain as well as in slice preparations from rodent brains.

image processing, robotics, and artificial intelligence. The underlying vision is our belief that, to be proven truly useful and insightful, computational neuroscience models should not only be tested against neural or behavioral data in the context of specialized laboratory experiments, but should also be exercised in the context of more general applications which confront the models to the real world. For example, we investigate whether our biologically-inspired visual models can be extended to solve problems such as automatic target detection in cluttered natural scenes, video compression, autonomous robotic nagivation on land or under water, or animation of virtual agents. We also investigate how learning and knowledge representation techniques derived from research in artificial intelligence could be used to make our models more performant at solving given machine vision tasks.

Our modeling efforts emphasize four aspects that are important in biological vision: determining task-relevance of an entity, biasing attention for the low-level visual features of desired targets, recognizing these targets using the same low-level features, and incrementally building a visual map of task-relevance at every scene location. Given a task definition in the form of keywords, our model first determines and stores the task-relevant entities in symbolic working memory, using prior knowledge stored in symbolic long-term memory. The model then biases its saliency-based visual attention system for the learned low-level visual features of the most relevant entity. Next, it attends to the most salient location in the scene, and attempts to recognize the attended object through hierarchical matching against stored object representations in a visual long-term memory. The task-relevance of the recognized entity is computed and used to update the symbolic working memory. In addition, a visual working memory in the form of a topographic task-relevance map is updated with the location and relevance of the recognized entity.

1. V. Navalpakkam & L. Itti, Lecture Notes in Computer Science, 2002 (http://ilab.usc.edu/publications/Navalpakkam_Itti02bmcv.html)
2. V. Navalpakkam & L. Itti, Proc. WAPCV, 2003 (http://ilab.usc.edu/publications/Navalpakkam_Itti03wapcv.html)
3. C. Siagian & L. Itti, Proc. IEEE International Workshop on Face Processing in Video, 2004 (http://ilab.usc.edu/publications/Siagian_Itti04fpiv.html)
4. V. Navalpakkam & L. Itti, Vision Research, 2005 (http://ilab.usc.edu/publications/Navalpakkam_Itti05vr.html)
5. C. Siagian & L. Itti, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006 (http://ilab.usc.edu/publications/Siagian_Itti06pami.html)

More on the iLab publication server (http://ilab.usc.edu/publications/) on Scene Understanding (http://ilab.usc.edu/publications/sc.html)