A Catechumenate for the Korean Methodist Church: Ressourcement, Formation and a Response to Secularism and Korean Traditional Religions

Research by Korean sociologists of religion indicates that Korean Protestantism has lost much of the spiritual vitality of preceding generations and that it increasingly shows the influences of Korean shamanism, Neo-Confucianism, and Western secularism and consumerism. Suggestions in the areas of homiletics and Christian social ethics have been offered to help steer the Korean Protestant churches away from these worldviews toward a more biblically-based course. Drawing upon and expanding these earlier studies and proposals, the current work recommends another method for developing a biblically-based, spiritually-revitalized, baptismally-shaped and ministry-committed Protestantism in Korea: a pre-baptismal adult catechumenate, in this case one designed for the context of the Korean Methodist Church. In order to produce a renewed catechumenal structure for Korean Methodism, adult catechumenal processes as well as baptismal theologies and rites are examined and analyzed from three principal sources: the first five centuries of the Christian church, and especially the mystagogical literature of the fourth century; the Roman Catholic Rite of Christian Initiation of Adults developed after the Second Vatican Council; and the United Methodist Church in the United States, both texts officially authorized by the denomination's General Conference and unofficial materials, among them resources for an adult catechumenate in the Come to the Waters series. In addition, previous and current practices of preparation for baptism in the Korean Methodist Church are identified and critiqued. From these findings a set of principles is put forward that guide the proposed catechumenal structure.

Spatiotemporal Gesture Segmentation

Spotting patterns of interest in an input signal is a very useful task in many different fields including medicine, bioinformatics, economics, speech recognition and computer vision. Example instances of this problem include spotting an object of interest in an image (e.g., a tumor), a pattern of interest in a time-varying signal (e.g., audio analysis), or an object of interest moving in a specific way (e.g., a human's body gesture). Traditional spotting methods, which are based on Dynamic Time Warping or hidden Markov models, use some variant of dynamic programming to register the pattern and the input while accounting for temporal variation between them. At the same time, those methods often suffer from several shortcomings: they may give meaningless solutions when input observations are unreliable or ambiguous, they require a high complexity search across the whole input signal, and they may give incorrect solutions if some patterns appear as smaller parts within other patterns. In this thesis, we develop a framework that addresses these three problems, and evaluate the framework's performance in spotting and recognizing hand gestures in video. The first contribution is a spatiotemporal matching algorithm that extends the dynamic programming formulation to accommodate multiple candidate hand detections in every video frame. The algorithm finds the best alignment between the gesture model and the input, and simultaneously locates the best candidate hand detection in every frame. This allows for a gesture to be recognized even when the hand location is highly ambiguous. The second contribution is a pruning method that uses model-specific classifiers to reject dynamic programming hypotheses with a poor match between the input and model. Pruning improves the efficiency of the spatiotemporal matching algorithm, and in some cases may improve the recognition accuracy. The pruning classifiers are learned from training data, and cross-validation is used to reduce the chance of overpruning. The third contribution is a subgesture reasoning process that models the fact that some gesture models can falsely match parts of other, longer gestures. By integrating subgesture reasoning the spotting algorithm can avoid the premature detection of a subgesture when the longer gesture is actually being performed. Subgesture relations between pairs of gestures are automatically learned from training data. The performance of the approach is evaluated on two challenging video datasets: hand-signed digits gestured by users wearing short sleeved shirts, in front of a cluttered background, and American Sign Language (ASL) utterances gestured by ASL native signers. The experiments demonstrate that the proposed method is more accurate and efficient than competing approaches. The proposed approach can be generally applied to alignment or search problems with multiple input observations, that use dynamic programming to find a solution.