Both mid-wave and long-wave IR cameras are used to measure various temperature profiles in thermoplastic parts as
they are printed. Two significantly different 3D-printers are used in this study. The first is a small scale commercially
available Solidoodle 3 printer, which prints parts with layer thicknesses on the order of 125μm. The second printer used
is a “Big Area Additive Manufacturing” (BAAM) 3D-printer developed at Oak Ridge National Laboratory. The BAAM
prints parts with a layer thicknesses of 4.06 mm. Of particular interest is the temperature of the previously deposited
layer as the new hot layer is about to be extruded onto it. The two layers are expected have a stronger bond if the
temperature of the substrate layer is above the glass transition temperature. This paper describes the measurement
technique and results for a study of temperature decay and substrate layer temperature for ABS thermoplastic with and
without the addition of chopped carbon fibers.
This paper reports on a novel approach to atmospheric cloud segmentation from a space based multi-spectral pushbroom satellite system. The satellite collects 15 spectral bands ranging from visible, 0.45 um, to long wave infa-red (IR), 10.7um. The images are radiometrically calibrated and have ground sample distances (GSD) of 5 meters for visible to very near IR bands and a GSD of 20 meters for near IR to long wave IR. The algorithm consists of a hybrid-classification system in the sense that supervised and unsupervised networks are used in conjunction. For performance evaluation, a series of numerical comparisons to human derived cloud borders were performed. A set of 33 scenes were selected to represent various climate zones with different land cover from around the world. The algorithm consisted of the following. Band separation was performed to find the band combinations which form significant separation between cloud and background classes. The potential bands are fed into a K-Means clustering algorithm in order to identify areas in the image which have similar centroids. Each cluster is then compared to the cloud and background prototypes using the Jeffries-Matusita distance. A minimum distance is found and each unknown cluster is assigned to their appropriate prototype. A classification rate of 88% was found when using one short wave IR band and one mid-wave IR band. Past investigators have reported segmentation accuracies ranging from 67% to 80%, many of which require human intervention. A sensitivity of 75% and specificity of 90% were reported as well.
The Multispectral Thermal Imager Satellite (MTI), launched on March 12, 2000, is a multispectral pushbroom system that acquires 15 unique spectral bands of data from 0.45-10.7 microns, with resolutions of 5 m for the visible bands and 20 m for the infrared. Scene data are collected on three separate sensor chip assemblies (SCAs) mounted on the focal plane. The process of image registration for MTI satellite imagery therefore requires two separate steps: (1) the multispectral data collected by each SCA must be coregistered and (2) the SCAs must be registered with respect to each other. An automated algorithm was developed to register the MTI imagery. This algorithm performs a phase correlation on edge-maps generated from paired bands of data and then spatial-filters the result to calculate the relative shifts between bands. The process is repeated on every combination of band pairs to generate a vector of coregistration results for each SCA. The three SCAs are then registered to each other using a similar process operating on just one spectral band. The resulting registration values are used to produce a linearly shifted un-resampled coregistered image cube. This study shows the results of 791 image registration attempts using the EdgeReg registration code and compares them to a perfect reference data set of the same images registered manually.
The timely analysis and exploitation of data from multispectral/hyperspectral sensors from remote sensing platforms can be a daunting task. One such sensor platform is the Multispectral Thermal Imager (MTI), which provides a highly informative source of remote sensing data. In a typical exploitation scenario, an image analyst may need to consistently locate regions/objects of interest from a stream of imagery in a timely manner. Many available image analysis/segmentation techniques are often either slow, not robust to spectral variabilities from view to view or within a spectrally similar region, or may require a significant amount of user intervention including a priori knowledge to achieve a segmentation corresponding to self-similar regions within the data. This paper discusses an unsupervised segmentation approach that exploits the gross spectral shape of MTI data. We describe a nonparametric unsupervised approach based on a graph theoretic representation of the data. The goal of this approach is to perform coarse level segmentation that can stand alone or as a potential precursor to other image analysis tools. In comparison to previous techniques, the key characteristics of this approach are in its simplicity, speed, and consistency. Most importantly it requires few user inputs and determines the number of spectral clusters, their overall size, and subsequent pixel assignment directly from the data.