Abstract: A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Abstract: A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Abstract: A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Abstract: A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: According to an embodiment, a method for generating a 3-D stereo structure comprises registering and rectifying a first image frame and a second image frame by local correction matching, extracting a first scan line from the first image frame, extracting a second scan line from the second image frame corresponding to the first scan line, calculating a pixel distance between the first scan line and the second scan line for each pixel for a plurality of pixel shifts, calculating a smoothed pixel distance for each pixel for the pixel shifts by filtering the pixel distance for each pixel over the pixel shifts, and determining a scaled height for each pixel of the first scan line, the scaled height comprising a pixel shift from among the pixel shifts corresponding to a minimal distance of the smoothed pixel distance for the pixel.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: A method and apparatus for identifying a target object. A group of objects is identified in an image. The group of objects provides a context for identifying the target object in the image. The target object is searched for in the image using the context provided by the group of objects.

Abstract: According to an embodiment, a method for generating a 3-D stereo structure comprises registering and rectifying a first image frame and a second image frame by local correction matching, extracting a first scan line from the first image frame, extracting a second scan line from the second image frame corresponding to the first scan line, calculating a pixel distance between the first scan line and the second scan line for each pixel for a plurality of pixel shifts, calculating a smoothed pixel distance for each pixel for the pixel shifts by filtering the pixel distance for each pixel over the pixel shifts, and determining a scaled height for each pixel of the first scan line, the scaled height comprising a pixel shift from among the pixel shifts corresponding to a minimal distance of the smoothed pixel distance for the pixel.

Abstract: Described is a system and method for detecting elevated structures, such as bridges and overpasses, in point cloud data. A set of data from a three-dimensional point cloud of a landscape is received by the system. The set of data points comprises inlier data points and outlier data points. The inlier data points in the three-dimensional point cloud data are identified and combined into at least one segment. The segment is converted into an image comprising at least one image level. Each image level is processed with an edge detection algorithm to detect elevated edges. The elevated edges are vectorized to identify an elevated structure of interest in the landscape. The present invention is useful in applications that require three-dimensional sensing systems, such as autonomous navigation and surveillance applications.

Abstract: A method and apparatus for processing images. Clusters of first features identified in a first image are identified. Each cluster in the clusters comprises a first group of features from the first features. A transformation for registering each cluster in the clusters with a corresponding cluster comprising a second group of features from second features identified in a second image is identified using an initial correspondence between the first features in the first image and the second features in the second image. A set of clusters from the clusters of the first features is identified using the transformation identified for each cluster. A final transformation for registering the first image with the second image is identified using the set of clusters.

Abstract: A method and apparatus for identifying a target object. A group of objects is identified in an image. The group of objects provides a context for identifying the target object in the image. The target object is searched for in the image using the context provided by the group of objects.

Abstract: A method and apparatus for processing images. Clusters of first features identified in a first image are identified. Each cluster in the clusters comprises a first group of features from the first features. A transformation for registering each cluster in the clusters with a corresponding cluster comprising a second group of features from second features identified in a second image is identified using an initial correspondence between the first features in the first image and the second features in the second image. A set of clusters from the clusters of the first features is identified using the transformation identified for each cluster. A final transformation for registering the first image with the second image is identified using the set of clusters.