11,607 research outputs found
Overcoming the Challenges Associated with Image-based Mapping of Small Bodies in Preparation for the OSIRIS-REx Mission to (101955) Bennu
The OSIRIS-REx Asteroid Sample Return Mission is the third mission in NASA's
New Frontiers Program and is the first U.S. mission to return samples from an
asteroid to Earth. The most important decision ahead of the OSIRIS-REx team is
the selection of a prime sample-site on the surface of asteroid (101955) Bennu.
Mission success hinges on identifying a site that is safe and has regolith that
can readily be ingested by the spacecraft's sampling mechanism. To inform this
mission-critical decision, the surface of Bennu is mapped using the OSIRIS-REx
Camera Suite and the images are used to develop several foundational data
products. Acquiring the necessary inputs to these data products requires
observational strategies that are defined specifically to overcome the
challenges associated with mapping a small irregular body. We present these
strategies in the context of assessing candidate sample-sites at Bennu
according to a framework of decisions regarding the relative safety,
sampleability, and scientific value across the asteroid's surface. To create
data products that aid these assessments, we describe the best practices
developed by the OSIRIS-REx team for image-based mapping of irregular small
bodies. We emphasize the importance of using 3D shape models and the ability to
work in body-fixed rectangular coordinates when dealing with planetary surfaces
that cannot be uniquely addressed by body-fixed latitude and longitude.Comment: 31 pages, 10 figures, 2 table
Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21
Published in final edited form as:
Brain Struct Funct. 2019 January ; 224(1): 351–362. doi:10.1007/s00429-018-1777-z.Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.Accepted manuscrip
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution.Comment: Final publication available at ACS Nano Letter
Shift Estimation Algorithm for Dynamic Sensors With Frame-to-Frame Variation in Their Spectral Response
This study is motivated by the emergence of a new class of tunable infrared spectral-imaging sensors that offer the ability to dynamically vary the sensor\u27s intrinsic spectral response from frame to frame in an electronically controlled fashion. A manifestation of this is when a sequence of dissimilar spectral responses is periodically realized, whereby in every period of acquired imagery, each frame is associated with a distinct spectral band. Traditional scene-based global shift estimation algorithms are not applicable to such spectrally heterogeneous video sequences, as a pixel value may change from frame to frame as a result of both global motion and varying spectral response. In this paper, a novel algorithm is proposed and examined to fuse a series of coarse global shift estimates between periodically sampled pairs of nonadjacent frames to estimate motion between consecutive frames; each pair corresponds to two nonadjacent frames of the same spectral band. The proposed algorithm outperforms three alternative methods, with the average error being one half of that obtained by using an equal weights version of the proposed algorithm, one-fourth of that obtained by using a simple linear interpolation method, and one-twentieth of that obtained by using a naiÂżve correlation-based direct method
Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series
Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
A Multiple-Expert Binarization Framework for Multispectral Images
In this work, a multiple-expert binarization framework for multispectral
images is proposed. The framework is based on a constrained subspace selection
limited to the spectral bands combined with state-of-the-art gray-level
binarization methods. The framework uses a binarization wrapper to enhance the
performance of the gray-level binarization. Nonlinear preprocessing of the
individual spectral bands is used to enhance the textual information. An
evolutionary optimizer is considered to obtain the optimal and some suboptimal
3-band subspaces from which an ensemble of experts is then formed. The
framework is applied to a ground truth multispectral dataset with promising
results. In addition, a generalization to the cross-validation approach is
developed that not only evaluates generalizability of the framework, it also
provides a practical instance of the selected experts that could be then
applied to unseen inputs despite the small size of the given ground truth
dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1
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