Ubuntu, Jesus, and Earth: Integrating African Religion and Christianity in Ecological Ethics

Africa faces problems of ecological devastation caused by economic exploitation, rapid population growth, and poverty. Capitalism, residual colonialism, and corruption undermine Africa's efforts to forge a better future. The dissertation describes how in Africa the mounting ecological crisis has religious, political, and economic roots that enable and promote social and environmental harm. It presents the thesis that religious traditions, including their ethical expressions, can effectively address the crisis, ameliorate its impacts, and advocate for social and environmental betterment, now and in the future. First, it examines African traditional religion and Christian teaching, which together provide the foundation for African Christianity. Critical examination of both religious worldviews uncovers their complementary emphases on human responsibility toward planet Earth and future generations. Second, an analysis of the Gwembe Tonga of Chief Simamba explores the interconnectedness of all elements of the universe in African cosmologies. In Africa, an interdependent, participatory relationship exists between the world of animals, the world of humans, and the Creator. In discussing the annual lwiindi (rain calling) ceremony of Simamba, the study explores ecological overtones of African religions. Such rituals illustrate the involvement of ancestors and high gods in maintaining ecological integrity. Third, the foundation of the African morality of abundant life is explored. Across Sub-Saharan Africa, ancestors' teachings are the foundation of morality; ancestors are guardians of the land. A complementary teaching that Christ is the ecological ancestor of all life can direct ethical responses to the ecological crisis. Fourth, the eco-social implications of ubuntu (what it means to be fully human) are examined. Some aspects of ubuntu are criticized in light of economic inequalities and corruption in Africa. However, ubuntu can be transformed to advocate for eco-social liberation. Fifth, the study recognizes that in some cases conflicts exist between ecological values and religious teachings. This conflict is examined in terms of the contrast between awareness of socioeconomic problems caused by population growth, on the one hand, and advocacy of a traditional African morality of abundant children, on the other hand. A change in the latter religious view is needed since overpopulation threatens sustainable living and the future of Earth. The dissertation concludes that the identification of Jesus with African ancestors and theological recognition of Jesus as the ecological ancestor, woven together with ubuntu, an ethic of interconnectedness, should characterize African consciousness and promote resolution of the socio-ecological crisis.

Towards Eco-Dharma: The Contribution of Gandhian Thought to Ecological Ethics in India

Ecological concern prompts poor and indigenous people of India to consider how a society can ensure both protection of nature and their rightful claim for a just and sustainable future. Previous discussions defended the environment while ignoring the struggles of the poor for sustenance and their religious traditions and ethical values. Mohandas Karamchand Gandhi addressed similar socio-ecological concerns by adopting and adapting traditional religious and ethical notions to develop strategies for constructive, engaged resistance. The dissertation research and analysis verifies the continued relevance of the Gandhian understanding of dharma (ethics) in contemporary India as a basis for developing eco-dharma (eco-ethics) to link closely development, ecology, and religious values. The method of this study is interpretive, analytical, and critical. Françoise Houtart’s social analytical method is used to make visible and to suggest how to overcome social tensions from the perspective of marginalized and exploited peoples in India. The Indian government's development initiatives create a nexus between the eco-crisis and economic injustice, and communities’ responses. The Chipko movement seeks to protect the Himalayan forests from commercial logging. The Narmada Bachao Andolan strives to preserve the Narmada River and its forests and communities, where dam construction causes displacement. The use of Gandhian approaches by these movements provides a framework for integrating ecological concerns with people's struggles for survival. For Gandhi, dharma is a harmony of satya (truth), ahimsa (nonviolence), and sarvodaya (welfare of all). Eco-dharma is an integral, communitarian, and ecologically sensitive ethical paradigm. The study demonstrates that the Gandhian notion of dharma, implemented through nonviolent satyagraha (firmness in promoting truth), can direct community action that promotes responsible economic structures and the well-being of the biotic community and the environment. Eco-dharma calls for solidarity, constructive resistance, and ecologically and economically viable communities. The dissertation recommends that for a sustainable future, India must combine indigenous, appropriate, and small- or medium-scale industries as an alternative model of development in order to help reduce systemic poverty while enhancing ecological well-being.

Recognition of Human Action Using Moment-Based Feature

The performance of different classification approaches is evaluated using a view-based approach for motion representation. The view-based approach uses computer vision and image processing techniques to register and process the video sequence. Two motion representations called Motion Energy Images and Motion History Image are then constructed. These representations collapse the temporal component in a way that no explicit temporal analysis or sequence matching is needed. Statistical descriptions are then computed using moment-based features and dimensionality reduction techniques. For these tests, we used 7 Hu moments, which are invariant to scale and translation. Principal Components Analysis is used to reduce the dimensionality of this representation. The system is trained using different subjects performing a set of examples of every action to be recognized. Given these samples, K-nearest neighbor, Gaussian, and Gaussian mixture classifiers are used to recognize new actions. Experiments are conducted using instances of eight human actions (i.e., eight classes) performed by seven different subjects. Comparisons in the performance among these classifiers under different conditions are analyzed and reported. Our main goals are to test this dimensionality-reduced representation of actions, and more importantly to use this representation to compare the advantages of different classification approaches in this recognition task.

Mining Anomalies Using Traffic Feature Distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.

Processing of Synthetic Aperture Radar Images by a Multiscale Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. The segmentation is performed by three "copies" of the BCS and FCS, of small, medium, and large scales, wherein the "short-range" and "long-range" interactions within each scale occur over smaller or larger distances, corresponding to the size of the early filters of each scale. A diffusive filling-in operation within the segmented regions at each scale produces coherent surface representations. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.

Synchronized Oscillations During Cooperative Feature Lining in a Cortical Model of Visual Perception

A neural network model of synchronized oscillations in visual cortex is presented to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these experiments, synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained for single bar stimuli and also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli using different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models.

Synchronized Oscillations During Cooperative Feature Linking in a Cortical Model of Visual Perception

A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.

Processing of Synthetic Aperture Radar Images by the Boundary Contour System and Feature Contour System

An improved Boundary Contour System (BCS) and Feature Contour System (FCS) neural network model of preattentive vision is applied to two large images containing range data gathered by a synthetic aperture radar (SAR) sensor. The goal of processing is to make structures such as motor vehicles, roads, or buildings more salient and more interpretable to human observers than they are in the original imagery. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. Finally, a diffusive filling-in operation within the segmented regions produces coherent visible structures. The combination of BCS and FCS helps to locate and enhance structure over regions of many pixels, without the resulting blur characteristic of approaches based on low spatial frequency filtering alone.

Cortical Dynamics of Feature Binding and Reset: Control of Visual Persistence

An analysis of the reset of visual cortical circuits responsible for the binding or segmentation of visual features into coherent visual forms yields a model that explains properties of visual persistence. The reset mechanisms prevent massive smearing or visual percepts in response to rapidly moving images. The model simulates relationships among psychophysical data showing inverse relations of persistence to flash luminance and duration, greaterr persistence of illusory contours than real contours, a U-shaped temporal function for persistence of illusory contours, a reduction of persistence: due to adaptation with a stimulus of like orientation, an increase or persistence due to adaptation with a stimulus of perpendicular orientation, and an increase of persistence with spatial separation of a masking stimulus. The model suggests that a combination of habituative, opponent, and endstopping mechanisms prevent smearing and limit persistence. Earlier work with the model has analyzed data about boundary formation, texture segregation, shape-from-shading, and figure-ground separation. Thus, several types of data support each model mechanism and new predictions are made.