Block-level discrete cosine transform coefficients for autonomic face recognition

This dissertation presents a novel method of autonomic face recognition based on the recently proposed biologically plausible network of networks (NoN) model of information processing. The NoN model is based on locally parallel and globally coordinated transformations. In the NoN architecture, the neurons or computational units form distributed networks, which themselves link to form larger networks. In the general case, an n-level hierarchy of nested distributed networks is constructed. This models the structures in the cerebral cortex described by Mountcastle and the architecture based on that proposed for information processing by Sutton. In the implementation proposed in the dissertation, the image is processed by a nested family of locally operating networks along with a hierarchically superior network that classifies the information from each of the local networks. The implementation of this approach helps obtain sensitivity to the contrast sensitivity function (CSF) in the middle of the spectrum, as is true for the human vision system. The input images are divided into blocks to define the local regions of processing. The two-dimensional Discrete Cosine Transform (DCT), a spatial frequency transform, is used to transform the data into the frequency domain. Thereafter, statistical operators that calculate various functions of spatial frequency in the block are used to produce a block-level DCT coefficient. The image is now transformed into a variable length vector that is trained with respect to the data set. The classification was done by the use of a backpropagation neural network. The proposed method yields excellent results on a benchmark database. The results of the experiments yielded a maximum of 98.5% recognition accuracy and an average of 97.4% recognition accuracy. An advanced version of the method where the local processing is done on offset blocks has also been developed. This has validated the NoN approach and further research using local processing as well as more advanced global operators is likely to yield even better results

Psychological and Physiological Processes Underlying Perception and Attention: A Study of Binocular Rivalry.

This thesis is concerned with an investigation of certain psychological and physiological processes underlying perception and attention. In this context binocular rivalry is selected for close investigation since it has at different times been related to both perception and attention. This relationship is demonstrated by a series of investigations which show that the stimulus that is currently non-dominant in rivalry is nevertheless fully analysed. The nature of rivalry indicates that two complementary visual systems contribute to perception and attention. Whilst one system (superior colliculus - posterior association cortex) is responsible for monitoring unperceived/unattended information and initiating a shift in attention, the other system (geniculo-striate cortex) is concerned with currently perceived/attended information. In the terminology of control theory, these two visual systems contribute to feedforward and feedback control respectively. The interaction between the two is considered to be the correlate of conscious perception and attention, reflecting the sampling of sensory information by a process that matches this information against the expectations based on a model of the world. Confirmation of a number of predictions refines and further anchors the theory to physiological mechanisms

Digital reconstruction, quantitative morphometric analysis, and membrane properties of bipolar cells in the rat retina.

A basic principle of neuroscience is that structure reflects function. This has led to numerous attempts to characterize the complete morphology of types of neurons throughout the central nervous system. The ability to acquire and analyze complete neuronal morphologies has advanced with continuous technological developments for over 150 years, with progressive refinements and increased understanding of the precise anatomical details of different types of neurons. Bipolar cells of the mammalian retina are short-range projections neurons that link the outer and inner retina. Their dendrites contact and receive input from the terminals of the light-sensing photoreceptors in the outer plexiform layer and their axons descend through the inner nuclear and inner plexiform layers to stratify at different levels of the inner plexiform layer. The stratification level of the axon terminals of different types of bipolar cells in the inner plexiform layer determines their synaptic connectivity and is an important basis for the morphological classification of these cells. Between 10 and 15 different types of cone bipolar cells have been identified in different species and they can be divided into ON-cone bipolar cells (that depolarize to the onset of light) and OFF-cone bipolar cells (that depolarize to the offset of light). Different types of cone bipolar cells are thought to be responsible for coding and transmitting different features of our visual environment and generating parallel channels that uniquely filter and transform the inputs from the photoreceptors. There is a lack of detailed morphological data for bipolar cells, especially for the rat, where biophysical mechanisms have been most extensively studied. The work presented in this thesis provides the groundwork for the future goal of developing morphologically realistic compartmental models for cone and rod bipolar cells. First, the contribution of gap junctions to the membrane properties, specifically input resistance, of bipolar cells was investigated. Gap junctions are ubiquitous within the retina, but it remains to be determined whether the strength of coupling between specific cell types is sufficiently strong for the cells to be functionally coupled via electrical synapses. There are gap junctions between the same class of bipolar cells, and this appears to be a common circuit motif in the vertebrate retina. Surprisingly, our results suggested that the gap junctions between OFF-cone bipolar cells do not support consequential electrical coupling. This provides an important first step both to elucidate the potential roles for these gap junctions, and also for the development of compartmental models for cone bipolar cells. Second, from image stacks acquired from multiphoton excitation microscopy, quantitative morphological reconstructions and detailed morphological analysis were performed with fluorescent dye-filled cone and rod bipolar cells. Compared to previous descriptions, the extent and complexity of branching of the axon terminals was surprisingly high. By precisely quantifying the level of stratification of the axon terminals in the inner plexiform layer, we have generated a reference system for the reliable classification of individual cells in future studies that are focused on correlating physiological and morphological properties. The workflow that we have implemented can be readily extended to the development of morphologically realistic compartmental models for these neurons.Doktorgradsavhandlin

NASA Tech Briefs, June 1991

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Fifth International Microgravity Combustion Workshop

This conference proceedings document is a compilation of 120 papers presented orally or as poster displays to the Fifth International Microgravity Combustion Workshop held in Cleveland, Ohio on May 18-20, 1999. The purpose of the workshop is to present and exchange research results from theoretical and experimental work in combustion science using the reduced-gravity environment as a research tool. The results are contributed by researchers funded by NASA throughout the United States at universities, industry and government research agencies, and by researchers from at least eight international partner countries that are also participating in the microgravity combustion science research discipline. These research results are intended for use by public and private sector organizations for academic purposes, for the development of technologies needed for the Human Exploration and Development of Space, and to improve Earth-bound combustion and fire-safety related technologies

Evaluation of remote sensing methods for continuous cover forestry

The overall aim of the project was to investigate the potential and challenges in the application of high spatial and spectral resolution remote sensing to forest stands in the UK for Continuous Cover Forestry (CCF) purposes. Within the context of CCF, a relatively new forest management strategy that has been implemented in several European countries, the usefulness of digital remote sensing techniques lie in their potential ability to retrieve parameters at sub-stand level and, in particular, in the assessment of natural regeneration and light regimes. The idea behind CCF is the support of a sustainable forest management system reducing disturbance of the forest ecosystem and encouraging the use of more natural methods, e.g. natural regeneration, for which the light environment beneath the forest canopy plays a fundamental role.The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Park (lat. 56°10' N, long. 4° 23' W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterized by their different light regimes, were established within the area for the measurement of forest variables using a forest inventory approach and hemispherical photography. The remote sensing data available for the study consisted of Landsat ETM+ imagery, a small footprint multi-return lidar dataset over the study area, Airborne Thematic Mapper (ATM) data, and aerial photography with same acquisition date as the lidar data.Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phenological change as a factor to consider for future acquisitions of remotely sensed imagery. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). It can be concluded that no single date is ideal for discriminating the species studied (early summer was best) and that a combination of two or three datasets covering their phenological cycles is optimal for the differentiation. Although the approaches used helped to characterize the forest species, especially to the discrimination between spruces, larch and the deciduous oak species, further work is needed in order to define an optimum approach to discriminate between spruce species (e.g. Sitka spruce and Norway spruce) for which spectral responses are very similar. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended.Lidar, ATM, and aerial photographic datasets were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and tree heights from multiple return lidar data, the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, followed by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree heights were estimated for all sample plots. The results showed that lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38 m.This study assessed the utility of an object-oriented approach for deciduous and coniferous crown delineation, based on small-footprint, multiple return lidar data, high resolution ATM imagery, and aerial photography. Special emphasis in the analysis was made in the fusion of aerial photography and lidar data for tree crown detection and classification, as it was expected that the high vertical accuracy of lidar, combined with the high spatial resolution aerial photography would render the best results and would provide the forestry sector with an affordable and accurate means for forest management and planning. Most of the field surveyed trees could be automatically and correctly detected, especially for the spruce and larch plots, but the complexity of the deciduous plots hindered the tree recognition approach, leading to poor crown extent and gap estimations. Indicators of light availability were calculated from the lidar data by calculation of laser hit penetration rates and percentage canopy cover. These results were compared to estimates of canopy openness obtained from hemispherical pictures for the same locations.Finally, the synergistic benefits of all datasets were evaluated and the forest structural variables determined from remote sensing and hemispherical photography were examined as indicators of light availability for regenerating seedlings