Narratives of Holiness Identity: The "Sanctified Person" in the Church of the Nazarene

This dissertation is an exercise in practical theology, which investigates and responds to the problem of changing holiness identity in the Church of the Nazarene. The first part of the study is an empirical investigation into the social context of contemporary Nazarene holiness identity and practices among Nazarenes in three congregations located in the Northeast United States. Previous research relied too heavily on secularization and sect-church theory to understand the dynamics of religious identity change among Nazarenes. The theological result was a pessimistic appraisal of the future possibilities of holiness identity and practice in the Church of the Nazarene. This study employs an alternative theory—Nancy T. Ammerman's theory of narrative religious identity—to understand the dynamics of lived religious life within these congregations and to identify the various holiness narratives at play. Ammerman's theory facilitates an empirical description of the multiple holiness identities emerging out of the social contexts of these Nazarene congregations and offers a way to account for identity change. At the heart of this research is the theoretical notion that a particular religious identity, in the case of the Church of the Nazarene, the "sanctified person," emerges out of a particular ecclesial context characterized by religious narratives and practices that shape this identity. Chapter one reviews the problem of holiness identity in the Church of the Nazarene and offers an analysis of recent sociological attempts to understand the changing identity among Nazarenes. Chapter two draws on sociological research to describe and depict the range of views of holiness held by some contemporary Nazarenes. Chapter three identifies the varieties of holiness identity within the three Nazarene congregations that are part of the study. Chapter four investigates the social sources that shape the various holiness identities discovered in these congregations. Chapter five is a description of the many ways religious narratives are enacted and engaged within these congregations. The second part of the study is a theological critique of contemporary Nazarene holiness identity. Chapter six draws on the theory of narrative identity proposed by Nancy Ammerman and outlines a theoretical model which describes the social conditions necessary to shape holiness identity, "the sanctified person," within the context of the local congregation. Finally, chapter seven draws on the theological resources of Mennonite scholar and historian John Howard Yoder to propose a way of construing and facilitating holiness identity formation that takes the ecclesiality of hoilness more seriously, emphasizes a clearer relationship between Jesus and the "Christlikeness" that is central to holiness, and highlights the importance of religious practices in the formation of a holiness identity.

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.

Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views

The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-D view categories whose outputs arc combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes as multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may also be used for scene understanding by using a preprocessor and classifier that can determine both What objects are in a scene and Where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaussian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the classifier, a supervised learning system based on the fuzzy ARTMAP algorithm. Fuzzy ARTMAP learns 2-D view categories that are invariant under 2-D image translation, rotation, and dilation as well as 3-D image transformations that do not cause a predictive error. Evidence from sequence of 2-D view categories converges at 3-D object nodes that generate a response invariant under changes of 2-D view. These 3-D object nodes input to a working memory that accumulates evidence over time to improve object recognition. ln the simplest working memory, each occurrence (nonoccurrence) of a 2-D view category increases (decreases) the corresponding node's activity in working memory. The maximally active node is used to predict the 3-D object. Recognition is studied with noisy and clean image using slow and fast learning. Slow learning at the fuzzy ARTMAP map field is adapted to learn the conditional probability of the 3-D object given the selected 2-D view category. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of l28x128 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex.