High-resolution Fovea Research Articles

Centering peripheral features in an indoor environment using a binocular log-polar 4 DOF camera head

Abstract This paper describes a hardware/software system that successfully detects certain stationary objects in an indoor scene. The system uses a four degree of freedom (4 DOF) pan-tilt-verge stereo camera head (built in-house) coupled with an inexpensive hardware image processor. A log-polar (LP) image mapping facilitates the detection of floor-wall-ceiling boundaries. The mapping also substantially reduces the data bandwidth of the input imagery by transforming a video resolution image into 64 × 64 pixel multiresolution image. A model matching algorithm detects the wanted object in the LP domain. If the object is in the low-resolution periphery of the LP image, the camera must be moved to center the high-resolution fovea on the object where stereo-from-vergeance can be used to calculate the distance to the object.

Variable-resolution character thinning

Most current thinning algorithms use only small windows (templates) due to the heavy computation load associated with large template size. However, the lack of global information leads to severe shape distortion and misclassification. We develop a new variable resolution method to solve this problem. The proposed algorithm, which is parallel in nature, uses a 9 × 9 window with a high resolution 3 × 3 center and low resolution periphery. The resolution is reduced by a ratio of 9:2 in the periphery. Thus the complexity of our method is less than twice that of a method using 4 × 4 windows. Our method resembles the human visual system which has a high resolution fovea and low resolution periphery. The advantage of the proposed algorithm over two well-known algorithms is demonstrated using several input characters.