A Self-Organizing Multiple-View Representation of 3D Objects

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, we trained the network to recognise ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalisation capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

Multiple-User Quantum Optical Communication

A fundamental understanding of the information carrying capacity of optical channels requires the signal and physical channel to be modeled quantum mechanically. This thesis considers the problems of distributing multi-party quantum entanglement to distant users in a quantum communication system and determining the ability of quantum optical channels to reliably transmit information. A recent proposal for a quantum communication architecture that realizes long-distance, high-fidelity qubit teleportation is reviewed. Previous work on this communication architecture is extended in two primary ways. First, models are developed for assessing the effects of amplitude, phase, and frequency errors in the entanglement source of polarization-entangled photons, as well as fiber loss and imperfect polarization restoration, on the throughput and fidelity of the system. Second, an error model is derived for an extension of this communication architecture that allows for the production and storage of three-party entangled Greenberger-Horne-Zeilinger states. A performance analysis of the quantum communication architecture in qubit teleportation and quantum secret sharing communication protocols is presented. Recent work on determining the channel capacity of optical channels is extended in several ways. Classical capacity is derived for a class of Gaussian Bosonic channels representing the quantum version of classical colored Gaussian-noise channels. The proof is strongly mo- tivated by the standard technique of whitening Gaussian noise used in classical information theory. Minimum output entropy problems related to these channel capacity derivations are also studied. These single-user Bosonic capacity results are extended to a multi-user scenario by deriving capacity regions for single-mode and wideband coherent-state multiple access channels. An even larger capacity region is obtained when the transmitters use non- classical Gaussian states, and an outer bound on the ultimate capacity region is presented

Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame

Passive monitoring of large sites typically requires coordination between multiple cameras, which in turn requires methods for automatically relating events between distributed cameras. This paper tackles the problem of self-calibration of multiple cameras which are very far apart, using feature correspondences to determine the camera geometry. The key problem is finding such correspondences. Since the camera geometry and photometric characteristics vary considerably between images, one cannot use brightness and/or proximity constraints. Instead we apply planar geometric constraints to moving objects in the scene in order to align the scene"s ground plane across multiple views. We do not assume synchronized cameras, and we show that enforcing geometric constraints enables us to align the tracking data in time. Once we have recovered the homography which aligns the planar structure in the scene, we can compute from the homography matrix the 3D position of the plane and the relative camera positions. This in turn enables us to recover a homography matrix which maps the images to an overhead view. We demonstrate this technique in two settings: a controlled lab setting where we test the effects of errors in internal camera calibration, and an uncontrolled, outdoor setting in which the full procedure is applied to external camera calibration and ground plane recovery. In spite of noise in the internal camera parameters and image data, the system successfully recovers both planar structure and relative camera positions in both settings.

Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.

Multiple Mode Vibration Suppression in Controlled Flexible Systems

Prior research has led to the development of input command shapers that can reduce residual vibration in single- or multiple-mode flexible systems. We present a method for the development of multiple-mode shapers which are simpler to implement and produce smaller response delays than previous designs. An MIT / NASA experimental flexible structure, MACE, is employed as a test article for the validation of the new shaping method. We examine the results of tests conducted on simulations of MACE. The new shapers are shown to be effective in suppressing multiple-mode vibration, even in the presence of mild kinematic and dynamic non-linearities.

Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.

A Multiple-Context Equality-Based Reasoning System

Expert systems are too slow. This work attacks that problem by speeding up a useful system component that remembers facts and tracks down simple consequences. The redesigned component can assimilate new facts more quickly because it uses a compact, grammar-based internal representation to deal with whole classes of equivalent expressions at once. It can support faster hypothetical reasoning because it remembers the consequences of several assumption sets at once. The new design is targeted for situations in which many of the stored facts are equalities. The deductive machinery considered here supplements stored premises with simple new conclusions. The stored premises include permanently asserted facts and temporarily adopted assumptions. The new conclusions are derived by substituting equals for equals and using the properties of the logical connectives AND, Or, and NOT. The deductive system provides supporting premises for its derived conclusions. Reasoning that involves quantifiers is beyond the scope of its limited and automatic operation. The expert system of which the reasoning system is a component is expected to be responsible for overall control of reasoning.