Honeywell International Inc.

Abhishek Alladi

Debabrata Pal

Parag Ravindra Rao

Luke M Zeleznak

Mohammed Ibrahim Mohideen

Shouvik Das

Aralakuppe Ramegowda Yogesha

Perumal Kumar

Rafeek Sainudeen

Anvita Singh

Systems and methods are provided for identifying light sources at an airport. Light sources are detected in a vehicle camera image. A region of interest (ROI) is determined for each light source based on the location of the light source in the image. A distance and relative angle are determined between each light source and a vehicle camera location. A gray scale version of each ROI is generated based on pre-defined relationships between intensities of red, green, and blue colors in the image and gray color intensities. The gray scale version is compared with pre-defined color histograms to determine a color associated with each light source. Each histogram corresponds to a gray-scale equivalent of an associated color. Context data associated with the image is determined. A light source type is assigned to each of the light sources based on the color of the light source and the context data.

Systems and methods for identifying lights sources at an airport

Methods and systems are provided for automated validation of images generated by uncertified software. One method involves analyzing an unverified image and a reference image in parallel to identify sets of kernels associated with a layer of a convolutional neural network, creating a shared set of kernels associated with that layer in accordance with one or more kernel shortlisting criteria, obtaining a first feature map for the unverified image using that layer of the convolutional neural network and the shared set of kernels, obtaining a second feature map for the reference image using that layer of the convolutional neural network and the shared set of kernels, calculating a similarity score for the unverified image based at least in part on differences between the feature maps and a weighting factor associated with the first layer, and automatically validating the unverified image based at least in part on the similarity score.

Methods and systems for automated display verification

Disclosed are methods, systems, and non-transitory computer-readable medium for managing energy use in a vehicle. For instance, the method may include receiving forecasted data from a first external source, receiving real-time data corresponding to at least one weather parameter at a first location at a first time, and continuously determining whether to perform an adjustment to a control parameter of the vehicle by using a machine learning model that is based on the forecasted data for the at least one weather parameter, the real-time data for the at least one weather parameter, a battery condition of the vehicle, and/or an estimated amount of energy consumed by traveling along a first navigation path.

Systems and methods for managing energy use in automated vehicles

A system for fluid flow verification comprises a video recording device that captures frames of actual fluid or fluid display, and a processor coupled to the recording device. The processor hosts fluid verification modules, including a fluid flow rate verification module, and a fluid color verification module. The fluid flow rate verification module is operative to receive the frames; binarize region of interest, and compute histogram of white/black pixels; count white pixels to detect actual fluid quantity; determine whether actual fluid quantity matches expected fluid quantity; if there is a match, measure fluid change rate; determine whether there is steady fluid flow; if not, report fluid flow rate anomaly. The fluid color verification module is operative to: receive binarized region of interest and histogram; detect fluid color by extracting sample color mask of pixels; determine whether detected fluid color matches expected fluid color; if not, report fluid color anomaly.

System and method for vision based graphical fluid flow anomaly detection for display verification

A method of verifying graphical objects using color invariant feature matching is provided. The method comprises determining, for each of a plurality of primary color channels, a color value for each inlier pixel in an actual object for that primary color channel; determining, for each color channel, a color difference factor (CDF) for an expected object by comparing, for each of the plurality of primary color channels, the color value for each inlier pixel in the expected object to the color value for each corresponding inlier pixel in the actual object; determining a false point value for the expected object from the CDF; and determining that the expected object matches the actual object when the false point value for the expected object is lower than the false point value for other expected objects.

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Karnataka, India

Abhishek Alladi