Image Slicer Performances from a Demonstrator for the SNAP/JDEM Mission - Part I: Wavelength Accuracy

A well-adapted visible and infrared spectrograph has been developed for the SNAP (SuperNova/Acceleration Probe) experiment proposed for JDEM. The instrument should have a high sensitivity to see faint supernovae but also a good redshift determination better than 0.003(1+z) and a precise spectrophotometry (2%). An instrument based on an integral field method with the powerful concept of imager slicing has been designed. A large prototyping effort has been performed in France which validates the concept. In particular a demonstrator reproducing the full optical configuration has been built and tested to prove the optical performances both in the visible and in the near infrared range. This paper is the first of two papers. The present paper focus on the wavelength measurement while the second one will present the spectrophotometric performances. We adress here the spectral accuracy expected both in the visible and in the near infrared range in such configuration and we demonstrate, in particular, that the image slicer enhances the instrumental performances in the spectral measurement precision by removing the slit effect. This work is supported in France by CNRS/INSU/IN2P3 and by the French spatial agency (CNES) and in US by the University of California.Comment: Submitted to PAS

Energy-resolved neutron imaging for reconstruction of strain introduced by cold working

Energy-resolved neutron transmission imaging is used to reconstruct maps of residual strains in drilled and cold-expanded holes in 5-mm and 6.4-mm-thick aluminum plates. The possibility of measuring the positions of Bragg edges in the transmission spectrum in each 55 × 55 µm2 pixel is utilized in the reconstruction of the strain distribution within the entire imaged area of the sample, all from a single measurement. Although the reconstructed strain is averaged through the sample thickness, this technique reveals strain asymmetries within the sample and thus provides information complementary to other well-established non-destructive testing methods

Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling

We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image. This strategy partitions training into two distinct stages. First, in an unsupervised manner, we learn a set of generic dictionaries optimized for sparse coding of image patches. We train a multilayer representation via recursive sparse dictionary learning on pooled codes output by earlier layers. Second, we encode all training images with the generic dictionaries and learn a transfer function that optimizes reconstruction of patches extracted from annotated ground-truth given the sparse codes of their corresponding image patches. At test time, we encode a novel image using the generic dictionaries and then reconstruct using the transfer function. The output reconstruction is a semantic labeling of the test image. Applying this strategy to the task of contour detection, we demonstrate performance competitive with state-of-the-art systems. Unlike almost all prior work, our approach obviates the need for any form of hand-designed features or filters. To illustrate general applicability, we also show initial results on semantic part labeling of human faces. The effectiveness of our approach opens new avenues for research on deep sparse representations. Our classifiers utilize this representation in a novel manner. Rather than acting on nodes in the deepest layer, they attach to nodes along a slice through multiple layers of the network in order to make predictions about local patches. Our flexible combination of a generatively learned sparse representation with discriminatively trained transfer classifiers extends the notion of sparse reconstruction to encompass arbitrary semantic labeling tasks.Comment: to appear in Asian Conference on Computer Vision (ACCV), 201

Terahertz dynamic aperture imaging at stand-off distances using a Compressed Sensing protocol

In this text, results of a 0.35 terahertz (THz) dynamic aperture imaging approach are presented. The experiments use an optical modulation approach and a single pixel detector at a stand-off imaging distance of approx 1 meter. The optical modulation creates dynamic apertures of 5cm diameter with approx 2000 individually controllable elements. An optical modulation approach is used here for the first time at a large far-field distance, for the investigation of various test targets in a field-of-view of 8 x 8 cm. The results highlight the versatility of this modulation technique and show that this imaging paradigm is applicable even at large far-field distances. It proves the feasibility of this imaging approach for potential applications like stand-off security imaging or far field THz microscopy.Comment: 9 pages, 13 figure

Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection

This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented

Software Defined DCF77 Receiver

This paper shows the solution of time stamp software defined receiver integration into low cost com-mercial devices. The receiver is based on a general pur-pose processor and its analog to digital converter. The amplified signal from a narrow-band antenna is connected to the converter and no complicated filtration has to be used. All signal processing is digitally provided by the processor. During signal reception, the processor stays available for its main tasks and signal processing con-sumes only a small part of its computational power

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure