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.

General Orders No. 61, 24 September 1918

General Orders, dated 24 September 1918, includes telegram messages received by the Corps Commander. The telegrams quoted include one from Woodrow Wilson and Major General Summerall. Both are messages of congratulations and praise.

TvD PB #61

Brown sediment with well dispersed clasts ranging from small to large. Clast shape ranges from angular to sub-rounded. Lineations can be seen throughout the sample. There is one small patch of another domain present. This domain is light brown, and contains clasts that are clustered together. Grain crushing is abundant in this domain, as well as in other areas of the sample.