Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded to extract texture, acceleration and other higher order visual features. Theorized and experimentally confirmed cortical information linking schemes, such as state dependent modulation and temporal synchronization are used to develop a PCNN-based visual information fusion network. The network is used to fuse the results of several object detection systems for the purpose of enhanced object detection accuracy. On actual mammograms and FLIR images, the network achieves an accuracy superior to any of the individual object detection systems it fused. Last, this research develops the first fully adaptive PCNN. Given only an input and a desired output, the adaptive PCNN will find all parameter values necessary to approximate that desired output

Accelerating Image Based Scientific Applications using Commodity Video Graphics Adapters

The processing power available in current video graphics cards is approaching super computer levels. State-of-the-art graphical processing units (GPU) boast of computational performance in the range of 1.0-1.1 trillion floating point operations per second (1.0-1.1 Teraflops). Making this processing power accessible to the scientific community would benefit many fields of research. This research takes a relatively computationally expensive image-based iris segmentation algorithm and hosts it on a GPU using the High Level Shader Language which is part of DirectX 9.0. The selected segmentation algorithm uses basic image processing techniques such as image inversion, value squaring, thresholding, dilation, erosion and a computationally intensive local kurtosis (fourth central moment) calculation. Strengths and limitations of the DirectX rendering pipeline are discussed. The primary source of the graphical processing power, the pixel or fragment shader, is discussed in detail. Impressive acceleration results were obtained. The iris segmentation algorithm was accelerated by a factor of 40 over the highly optimized C++ version hosted on the computer's central processing unit. Some parts of the algorithm ran at speeds that were over 100 times faster than their C++ counterpart. GPU programming details and HLSL code samples are presented as part of the acceleration discussion