Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

HG 37.15-37.25

Brown sediment with clasts ranging from small to large in size. Clast shape ranges from sub-angular to rounded. Rotation structures were abundant throughout the sample, along with necking structures. Lineations and edge-to-edge grain crushing were also present in minor amounts.

Pay roll voucher #37 from the Engineer Department of the Welland Railway for the Southern Division for the month of October

Pay roll voucher #37 from the Engineer Department of the Welland Railway for the Southern Division for the month of October, Nov. 6, 1857.