USA As Represented by Secretary of the Navy

Clics, LLC

Heidi L Buck

John Stastny

Bryan D Bagnall

Elan Sharghi

Daniel F Garcia

Stephen L Hobbs

Erin Joslin

Eric C Hallenborg

Gregory Knowles Fleizach

Barry R Hunt

Charles D Brown

Stuart D'Alessandro

Leilani M Macedo

Jeffrey S Ploetner

Matthew Joseph Nicholson

Matthew Fong

Alan Fronk

Brian T Williams

John Stephen deGrassie

Corey A Lane

Jason Cole

Peter C Grossnickle

Michael R Hughes

Keith Pifko

Matthew Waller

Jerry Fu

Chessa Guilas

Joshua S Li

Brian Schalcosky

Bo Fitzsimmons

Wildon Zissel

A system for scanning a client's hair color and dispensing hair coloring to change the clients hair color from an initial color to a target color is disclosed. The system comprises an input for reading the measured color from the scanning device, a processor configured to compare the measured color to a desired target hair color and develop a coloring protocol for changing the clients current hair color to the desired target color, and a dispenser for dispensing one or more formulations that follow the protocol.

Systems and methods for coloring hair

A method and system are provided for processing maritime video data to improve processing time and reduce the risk of false detection. Video data input may be received from satellites, aircrafts, UAVs and other aerial vehicles. The method and system create annotated video output files indicating the location of tracked anomalies. The method and system perform an anomaly detection function on each data frame within the video input, identify anomalous regions within each data frame, and identify clusters. The method and system then perform an algorithm to discard clusters which do not meet threshold criteria to reduce the risk of false detection, as well as employ a horizon detection algorithm to eliminate superfluous image data unrelated to tracking maritime targets.

System for tracking maritime domain targets from full motion video

Systems and methods for automatic detection of disaster relief assets from a satellite image can include a satellite and a processor, which can contain non-transitory written directions that perform a method, which includes the step of defining spectra of interest (SOI). A reference spectral profile that is based on the SOI is built. The reference spectral profile can extend into the near infrared range to eliminate false positive indications. A satellite image of the spatial region of interest (ROI) is taken. For each pixel in the ROI, test spectra of the spatial region can be extracted and compared to the reference spectral profile using a spectral angle mapper (SAM) tool. When the comparison meets predetermined parameters, that pixel of the ROI can be deemed to be indicative of a disaster relief asset, and the geo-reference data for that pixel can be transmitted to a remote user.

Automatic asset detection for disaster relief using satellite imagery

Systems and methods for determining the speed of a vessel from an overhead image of the vessel's wake can include the initial step of defining a cusp line for the wake. A representative line segment from the defined cusp line that is suitable for building an image intensity profile for the image can be selected. Once the intensity profile is built along the line segment, the wake wavelength &#x3bb;from the image intensity profile can be found by measuring the distance between successive points of intensity maxima (or successive intensity minima points) along the line segment. Once &#x3bb;is found, the vessel speed can be determined from the images according to the formula &#x3bd;=&#x221a;{square root over (1.352*&#x3bb;)}. The systems and methods can be practiced using any overhead imagery systems that display a vessel's wake, including synthetic aperture radar (SAR) and electro-optical (EO) overhead imagery systems.

System and method for inferring vessel speed from overhead images

A method involves adapting a vessel detection algorithm based upon received synthetic aperture radar (SAR) image data and associated metadata, detecting one or more vessels within an image tile of the SAR image data by iteratively applying the adapted vessel detection algorithm to successive portions of the image tile, discarding a detected vessel if a false alarm is determined, extracting one or more features from the detected vessels that are not discarded, and generating one or more output products based upon the extracted features.

Adaptive automated synthetic aperture radar vessel detection method with false alarm mitigation

Systems and methods for processing satellite imagery include a satellite, a processor, a database of vessel position data and a computer readable storage medium. The methods process satellite imagery by fusing the imagery with information from the database to automatically identify ships. The methods include the steps of defining an Area of Interest (AOI) and Time of Interest (TOI) for the image and enlarging the AOI according to a time window that brackets the TOI and an assumed vessel maximum speed. Vessel position data from the database for all vessels within the enlarged AOI and the time window is accessed, and fused to imagery position data using Chi-Squared probability analysis. If the analysis meets predetermined probability threshold criteria, the vessel position is assigned to the satellite image to identify the vessel. Otherwise, the operator is alerted that imaged vessels do not correlate to vessel reporting data, or vice versa.

Method for fusing overhead imagery with automatic vessel reporting systems

A method for automatically detecting and mapping fires based on information extracted from commercial overhead EO/IR imagery, creating geo-referenced files which can be opened in most common geographic information system (GIS) software packages. The method creates a shapefile (*.shp & *.shx) and a Google Earth file (*.kmz) which contain the outlines of the areas from the image being processed with active fire in them, which types of files are typically very small compared to the size of the image file being processed. The method utilizes algorithms designed to process information contained in multi-spectral electro-optical imagery to classify pixels as 'fire' or 'non-fire'. The method also has the ability to identify the approximate length, width, and area of the fires detected.

Method for detecting and mapping fires using features extracted from overhead imagery

Methods for processing overhead imagery of a vessel include the step of determining an initial classification and classification probability based on the vessel length and length-to-width ratio. Next, mutually exclusive deck features can be extracted from the image. For several embodiments, the extracted deck features that can be spherical tanks, hatches and containers that are stored on deck. The initial classification probability is then weighted using the results of the deck feature extraction step to yield a posterior classification probability for the ship image. If the posterior classification probability is above a predetermined value, the image is assigned a posterior classification. If the posterior probability is below the predetermined value, the vessel image is classified as unknown, and the gross tonnage of the vessel is calculated using the length and width of the vessel.

Method for classifying vessels using features extracted from overhead imagery

Methods for automatic detection of ships in overhead images of bodies of water are disclosed. The image is initially analyzed to determine if land is present, and the portions of the overhead image where land is present are masked and not processed further. The methods include the steps of chipping the unmasked portions of the overhead image into a series of tiles, discriminating and removing clouds from the tiles using two-dimensional Fourier transforms, and characterizing tile background noise from the water's surface. Different ship detection algorithms are used, according to the level of background noise detected. Detected ships are output into a format that is easily interpreted by the user. The formatted output can also include a confidence rating, or a calculation of the certainty that the detected object in an output file is actually a ship.

Ship detection system and method from overhead images

A system and process for the geo-location of an asset given no a priori knowledge regarding time and location of the asset with minimized power consumption is disclosed. In the preferred embodiment, the system is accurate to about 30 meters CEP (50% circular error probable) using just a short segment, e.g., 100-200 ms, of digitized GPS L1 signal data.

System and method for geo-locating a receiver with reduced power consumption

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San Diego, CA, United States of America

Heidi L Buck