Image Processing, Pattern Recognition and Attentional Algorithms
in a Space-Variant Active Vision System
10:30 a.m., Wednesday, June 24, 1992
room 1302, Warren Weaver Hall
A space-variant sensor motivated by human vision system has highest resolution at the center with rapidly decreasing resolution toward the peripheral area. It has the advantages of a wide visual field and, at the same time, high central resolution. The dramatic reduction of pixel number in this kind of sensor makes it possible to build a real-time vision system using only moderate computational resources. On the other hand, the space-variant image has different layout compared to a raster image. The neighbor relationships change from pixel to pixel. We need to device special method to solve this neighborhood problem.
We use a connectivity graph to represent neighbor relations between pixels in a space-variant image. We can use it to define operators for edge detection, smoothing, etc. We use a two-level pyramid based on the connectivity graph to perform local thresholding for segmentation. The translation, rotation and scaling graph are three extensions of the connectivity graph which are used to translate, rotate and scale space-variant images. We can use these graphs to perform scale and rotation independent template matching.
We successfully apply several feature designs for OCR in the space-variant domain. They include: Characteristic-Loci, Partition, Heat-Signature, and Projection. All of them are translation and scale invariant. We also have two rotation invariant methods based on Partion and Projection methods.
Since space-variant sensor has higher resolution at the center, the recognition result is more reliable if we point the sensor close to the candidate object. Therefore, if we want to recognize any single character, the center of this character is the best place for pointing the sensor. But for recognizing adjacent characters, except for well separated ones, we need to point the sensor to the place where we can separate these characters.
Based on this reliability analysis, we devised four attentional rules and an algorithm for moving sensors to recognize character strings in static natural scenes.
Finally, we describe the algorithms for reading characters from the license plate on a moving vehicle. It includes stages for traffic zone finding, moving car finding, license plate finding, license plate tracking, and character reading.