Object detection, a critical task in computer vision, has been revolutionized by Deep Learning technologies, especially convolutional neural networks (CNN). These techniques are increasingly deployed in infrared imaging systems for long-range target detection, localization, and identification. Its performance is highly dependent on the training procedure, network architecture and computing resources. In contrast, human-in-the-loop task performance can be reliably predicted using well-established models. Here we model the performance of a CNN developed for MWIR and LWIR sensors and compare against human perception models. We focus on tower detection relevant to vision-based geolocation tasks which present novel high-aspect ratio, unresolved and low-clutter scenarios.
KEYWORDS: Sensors, Cameras, Global Positioning System, Long wavelength infrared, Panoramic photography, Short wave infrared radiation, Mid-IR, Near infrared, MATLAB, Algorithm development
We built a multispectral data collection system and vehicle testbed for experimentation on vision-based geolocation. The data collection system includes a gimballed mount with VIS, NIR, SWIR, MWIR and LWIR sensors allowing us to compare simultaneous imagery of features or targets across all the atmospheric bands. For geolocation experiments, the testbed is equipped with a dual-GPS inertial measurement unit for true location and orientation, a CAN bus interface to pull vehicle speedometer and odometer data. It is also outfitted with a dual-GPU, rugged, edge computer used to control the system and collect data; the computer is pre-loaded with geospatial data (maps, tower positions, elevation data, etc.) necessary for tracking targets of interest or performing real-time geolocation estimates. 60% of the rear seat was replaced with an electronics rack which also houses a 3kW inverter providing power to all of the equipment. A weather-proof cable pass through was installed in the roof of the truck while a weather-proof enclosure provides wind and rain protection to the roof-mounted equipment. We present multispectral panoramic imagery of flat environments where cellular towers provide ideal references for geolocation and mountainous environments where the landscape and horizon topography provide viable geo-references. We will present an overview of the data collection modes, calibration procedures, and the driving data sets collected to date.
The ability to reliably and accurately ascertain a vehicle’s position is imperative for military operations as well as civilian and commercial navigation systems. Due to the susceptibility of GPS signals to RF spoofing and jamming, alternative means of vehicle self-localization are garnering substantial interest. Vision-based methods are among the most promising in environments with sufficiently distinguishable features such as towers, high-rise structures, and prominent identifiable topographical features. Here, we present a localization approach exploiting multiple spectral bands to identify key prominent scene features and determine vehicle position relative to those features to calculate a global vehicle position and heading. We employ geometric dead-reckoning using visible and LWIR imagery to quantify positional accuracy that is achievable with these bands. We utilize image recognition algorithms to identify features and map these into useful parameters for position extraction, leveraging geospatial data when possible.
Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.
We have developed the second generation of our time-domain near-infrared spectroscopy (TD-NIRS) system for baseline and functional brain imaging. The instrument uses a pulsed broadband supercontinuum laser emitting a large spectrum between 650 and 1700 nm, and a gated detection based on an intensified CCD camera. The source laser beam is split into two arms, below and above 776 nm. In each arm, a fast motorized filter wheel enables selection of a bandpass filter at the required wavelength. Each filtered laser beam is then launched into one array of source fibers. The multiplexing through the array of fibers is implemented through a very compact home-made design consisting of two galvanometer mirrors followed by an achromatic doublet. Source fibers are then recombined one-by-one from both arms into the source optodes to be positioned on the head. The detection fibers are all imaged in parallel through a relay lens on an intensified CCD camera. By using detection fibers of different lengths, we introduce optical delays that enable simultaneous recording in different delay windows of the temporal point spread functions. We present the instrumentation and show its preliminary functional imaging capabilities. We also introduce a new probe where we use different fiber lengths on the source and the detector sides in order to record simultaneously both wavelengths from one location through different sets of fibers.
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