An Historical and Theological Analysis of Campus Crusade for Christ's Evangelistic Practice in Two American University Contexts

This dissertation, an exercise in practical theology, consists of a critical conversation between the evangelistic practice of Campus Crusade for Christ in two American university contexts, Bryan Stone's ecclesiologically grounded theology of evangelism, and William Abraham's eschatologically grounded theology of evangelism. It seeks to provide these evangelizing communities several strategic proposals for a more ecclesiologically and eschatologically grounded practice of evangelism within a university context. The current literature on evangelism is long on evangelistic strategy and activity, but short on theological analysis and reflection. This study focuses on concrete practices, but is grounded in a thick description of two particular contexts (derived from qualitative research methods) and a theological analysis of the ecclesiological and eschatological beliefs embedded within their evangelistic activities. The dissertation provides an historical overview of important figures, ideas, and events that helped mold the practice of evangelism inherited by the two ministries of this study, beginning with the famous Haystack Revival on Williams College in 1806. Both ministries, Campus Crusade for Christ at Bowling Green State University (Ohio) and at Washington State University, inherited an evangelistic practice sorely infected with many of the classic distortions that both Abraham and Stone attempt to correct. Qualitative research methods detail the direction that Campus Crusade for Christ at Bowling Green State University (Ohio) and Washington State University have taken the practice of evangelism they inherited. Applying the analytical categories that emerge from a detailed summary of Stone and Abraham to qualitative data of these two ministries reveals several ways evangelism has morphed in a manner sympathetic to Stone's insistence that the central logic of evangelism is the embodied witness of the church. The results of this analysis reveal the subversive and pervasive influence of modernity on these evangelizing communities—an influence that warrants several corrective strategic proposals including: 1) re-situating evangelism within a reading of the biblical narrative that emphasizes the present, social, public, and realized nature of the gospel of the kingdom of God rather than simply its future, personal, private, and unrealized dimensions; 2) clarifying the nature of the evangelizing communities and their relationship to the church; and 3) emphasizing the virtues that characterize a new evangelistic exemplar who is incarnational, intentional, humble, and courageous.

Contour Generator Points for Threshold Selection and a Novel Photo-Consistency Measure for Space Carving

Space carving has emerged as a powerful method for multiview scene reconstruction. Although a wide variety of methods have been proposed, the quality of the reconstruction remains highly-dependent on the photometric consistency measure, and the threshold used to carve away voxels. In this paper, we present a novel photo-consistency measure that is motivated by a multiset variant of the chamfer distance. The new measure is robust to high amounts of within-view color variance and also takes into account the projection angles of back-projected pixels. Another critical issue in space carving is the selection of the photo-consistency threshold used to determine what surface voxels are kept or carved away. In this paper, a reliable threshold selection technique is proposed that examines the photo-consistency values at contour generator points. Contour generators are points that lie on both the surface of the object and the visual hull. To determine the threshold, a percentile ranking of the photo-consistency values of these generator points is used. This improved technique is applicable to a wide variety of photo-consistency measures, including the new measure presented in this paper. Also presented in this paper is a method to choose between photo-consistency measures, and voxel array resolutions prior to carving using receiver operating characteristic (ROC) curves.

Periodic Motion Detection and Estimation via Space-Time Sampling

A novel technique to detect and localize periodic movements in video is presented. The distinctive feature of the technique is that it requires neither feature tracking nor object segmentation. Intensity patterns along linear sample paths in space-time are used in estimation of period of object motion in a given sequence of frames. Sample paths are obtained by connecting (in space-time) sample points from regions of high motion magnitude in the first and last frames. Oscillations in intensity values are induced at time instants when an object intersects the sample path. The locations of peaks in intensity are determined by parameters of both cyclic object motion and orientation of the sample path with respect to object motion. The information about peaks is used in a least squares framework to obtain an initial estimate of these parameters. The estimate is further refined using the full intensity profile. The best estimate for the period of cyclic object motion is obtained by looking for consensus among estimates from many sample paths. The proposed technique is evaluated with synthetic videos where ground-truth is known, and with American Sign Language videos where the goal is to detect periodic hand motions.

Search Space Reduction in QoS Routing

To provide real-time service or engineer constrained-based paths, networks require the underlying routing algorithm to be able to find low-cost paths that satisfy given Quality-of-Service (QoS) constraints. However, the problem of constrained shortest (least-cost) path routing is known to be NP-hard, and some heuristics have been proposed to find a near-optimal solution. However, these heuristics either impose relationships among the link metrics to reduce the complexity of the problem which may limit the general applicability of the heuristic, or are too costly in terms of execution time to be applicable to large networks. In this paper, we focus on solving the delay-constrained minimum-cost path problem, and present a fast algorithm to find a near-optimal solution. This algorithm, called DCCR (for Delay-Cost-Constrained Routing), is a variant of the k-shortest path algorithm. DCCR uses a new adaptive path weight function together with an additional constraint imposed on the path cost, to restrict the search space. Thus, DCCR can return a near-optimal solution in a very short time. Furthermore, we use the method proposed by Blokh and Gutin to further reduce the search space by using a tighter bound on path cost. This makes our algorithm more accurate and even faster. We call this improved algorithm SSR+DCCR (for Search Space Reduction+DCCR). Through extensive simulations, we confirm that SSR+DCCR performs very well compared to the optimal but very expensive solution.

Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process. Hence multiple solutions exist, and it is desirable to restrict the solution space to a smaller subset of feasible solutions. For example, not all human body poses are feasible due to anthropometric constraints. Since the space of feasible solutions may not admit a closed form description, the proposed framework seeks to exploit machine learning techniques to learn an approximation that is smoothly parameterized over such a space. One such technique is Gaussian Process Latent Variable Modelling. Scaled conjugate gradient is then used find the best matching pose in the space of feasible solutions when given an input image. The formulation allows easy incorporation of various constraints, e.g. temporal consistency and anthropometric constraints. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The estimation accuracy of the Specialized Mapping Architecture is at least one standard deviation worse than the proposed approach in the experiments with synthetic data. In experiments with real video of humans performing gestures, the proposed approach produces qualitatively better estimation results.

Tracking Human Body Pose on a Learned Smooth Space

Particle filtering is a popular method used in systems for tracking human body pose in video. One key difficulty in using particle filtering is caused by the curse of dimensionality: generally a very large number of particles is required to adequately approximate the underlying pose distribution in a high-dimensional state space. Although the number of degrees of freedom in the human body is quite large, in reality, the subset of allowable configurations in state space is generally restricted by human biomechanics, and the trajectories in this allowable subspace tend to be smooth. Therefore, a framework is proposed to learn a low-dimensional representation of the high-dimensional human poses state space. This mapping can be learned using a Gaussian Process Latent Variable Model (GPLVM) framework. One important advantage of the GPLVM framework is that both the mapping to, and mapping from the embedded space are smooth; this facilitates sampling in the low-dimensional space, and samples generated in the low-dimensional embedded space are easily mapped back into the original highdimensional space. Moreover, human body poses that are similar in the original space tend to be mapped close to each other in the embedded space; this property can be exploited when sampling in the embedded space. The proposed framework is tested in tracking 2D human body pose using a Scaled Prismatic Model. Experiments on real life video sequences demonstrate the strength of the approach. In comparison with the Multiple Hypothesis Tracking and the standard Condensation algorithm, the proposed algorithm is able to maintain tracking reliably throughout the long test sequences. It also handles singularity and self occlusion robustly.

Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why So Many Functions?

Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-91-J-1309)

Space, Time and Learning in the Hippocampus: How Fine Spatial and Temporal Scales Are Expanded into Population Codes for Behavioral Control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing, and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus, and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory.