1. Street Smarts: Visual Attention on the Go Alexander Patrikalakis May 13, 2009 6.XXX

2. Vision of Attention For machines to recreate human visual attention, we must accept that humans: – Maintain multi-scale orientation, intensity, and color feature neuronal maps in parallel – Combine multi-scale features into a central conspicuity (saliency) map – Maintain a Winner-Take-All neural network that saccades to and subsequently inhibits decreasingly salient points

3. Example Object recognition at all points of an image is infeasible time-wise Visual attention allows us to find the interesting points quickly Ullman agrees: “Recognition over the whole scene leads to a combinatorial explosion.”

4. Implementation Steps Analyzed previous work done by Ullman, Itti, and Koch on visual attention Implemented visual saliency model in C++ using Intel OpenCV, IPP, and TBB Implemented FOA shifting by saccading to points with decreasing saliency map values; same effect as a 2D neuronal matrix

5. Results Tested algorithm on 13 geometric scenes, and obtained plausible salient winners in each Tested algorithm on 40 natural scenes (roads and highways) and found that signs and signals are very salient (usually saccaded to first) Algorithm resilient to noise and takes advantage of multi-scale analysis

6. Itti: Normalization Promote maps with small numbers of strong maxima Suppress maps with large numbers of equally strong maxima Method: scales maps by the difference between global maximum and mean of remaining maxima

7. Ullman, Itti, Koch: Multi-scale features Multi-scale ArchitectureThree Feature Maps

8. Ullman: The Winner-Takes-All (WTA)

9. Simple Example

10. Noise Resilience

11. Multi-scale Advantage 1

12. Multi-scale Advantage 2

13. Problematic distractions

14. Contributions Reviewed past work done on biologically inspired visual attention models Identified Itti’s algorithm as a candidate for saliency detection in natural scenes involving road signs Demonstrated algorithm’s effectiveness on many natural scenes involving road signs Created a prototype saliency heuristic for evaluating sign effectiveness