Knowledge-based segmentation of SAR data with learned priors

©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.821747An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data

Introduction to the Special Issue on Partial Differential Equations and Geometry-Driven Diffusion in Image Processing and Analysis

©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.1998.66117

Guiding of Rydberg atoms in a high-gradient magnetic guide

We study the guiding of $^{87}$Rb 59D$_{5/2}$ Rydberg atoms in a linear, high-gradient, two-wire magnetic guide. Time delayed microwave ionization and ion detection are used to probe the Rydberg atom motion. We observe guiding of Rydberg atoms over a period of 5 ms following excitation. The decay time of the guided atom signal is about five times that of the initial state. We attribute the lifetime increase to an initial phase of $l$-changing collisions and thermally induced Rydberg-Rydberg transitions. Detailed simulations of Rydberg atom guiding reproduce most experimental observations and offer insight into the internal-state evolution

Robust Large Margin Deep Neural Networks

The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization reparametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED, and ImageNet datasets

Lessons from the Rademacher complexity for deep learning

Understanding the generalization properties of deep learning models is critical for successful applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks via the empirical Rademacher complexity and show that it is easier to control the complexity of convolutional networks compared to general fully connected networks. In particular, we justify the usage of small convolutional kernels in deep networks as they lead to a better generalization error. Moreover, we propose a representation based regularization method that allows to decrease the generalization error by controlling the coherence of the representation. Experiments on the MNIST dataset support these foundations

Role of the Mean-field in Bloch Oscillations of a Bose-Einstein Condensate in an Optical Lattice and Harmonic Trap

Using the Crank-Nicholson method, we study the evolution of a Bose-Einstein condensate in an optical lattice and harmonic trap. The condensate is excited by displacing it from the center of the harmonic trap. The mean field plays an important role in the Bloch-like oscillations that occur after sufficiently large initial displacement. We find that a moderate mean field significantly suppresses the dispersion of the condensate in momentum space. When the mean field becomes large, soliton and vortex structures appear in the condensate wavefunction.Comment: BEC simulation, 7 figure

Development that works, March 31, 2011

This repository item contains a single issue of the Pardee Conference Series, On March 31, 2011, more than 100 people participated in a conference titled “Development That Works,” sponsored by Boston University’s Frederick S. Pardee Center for the Study of the Longer-Range Future in collaboration with the BU Global Development program. In the pages that follow, four essays written by Boston University graduate students capture the salient points and overarching themes from the four sessions, each of which featured presentations by outstanding scholars and practitioners working in the field of development. The conference agenda and speakers’ biographies are included following the essays.The theme and the title of the conference—”Development That Works”—stemmed from the conference organizers’ desire to explore, from a groundlevel perspective, what programs, policies, and practices have been shown—or appear to have the potential—to achieve sustained, long-term advances in development in various parts of the world. The intent was not to simply showcase “success stories,” but rather to explore the larger concepts and opportunities that have resulted in development that is meaningful and sustainable over time. The presentations and discussions focused on critical assessments of why and how some programs take hold, and what can be learned from them. From the influence of global economic structures to innovative private sector programs and the need to evaluate development programs at the “granular” level, the expert panelists provided well-informed and often provocative perspectives on what is and isn’t working in development programs today, and what could work better in the future

PDEs for tensor image processing

Methods based on partial differential equations (PDEs) belong to those image processing techniques that can be extended in a particularly elegant way to tensor fields. In this survey paper the most important PDEs for discontinuity-preserving denoising of tensor fields are reviewed such that the underlying design principles becomes evident. We consider isotropic and anisotropic diffusion filters and their corresponding variational methods, mean curvature motion, and selfsnakes. These filters preserve positive semidefiniteness of any positive semidefinite initial tensor field. Finally we discuss geodesic active contours for segmenting tensor fields. Experiments are presented that illustrate the behaviour of all these methods