Ford Global Technolgoies, LLC

Xianling Zhang

Nikita Jaipuria

Rohan Bhasin

Vidya Nariyambut Murali

Nathan Tseng

Jinesh J Jain

Shreyasha Paudel

Katherine Stevo

Meghana Laxmidhar Gaopande

Mayar Arafa

Artem Litvak

A computer includes a processor and a memory storing instructions executable by the processor to receive a dataset of images; extract feature data from the images; optimize a number of clusters into which the images are classified based on the feature data; for each cluster, optimize a number of subclusters into which the images in that cluster are classified; determine a metric indicating a bias of the dataset toward at least one of the clusters or subclusters based on the number of clusters, the numbers of subclusters, distances between the respective clusters, and distances between the respective subclusters; and after determining the metric, train a machine-learning program using a training set constructed from the clusters and the subclusters.

De-biasing datasets for machine learning

A computer includes a processor and a memory storing instructions executable by the processor to receive a plurality of first images of an environment in a first lighting condition, classify pixels of the first images into categories, mask the pixels belonging to at least one of the categories from the first images, generate a three-dimensional representation of the environment based on the masked first images, and generate a second image of the environment in a second lighting condition based on the three-dimensional representation and on a first one of the first images.

Image relighting

A computer includes a processor and a memory storing instructions executable by the processor to receive a first image of a scene in a first lighting condition, generate a three-dimensional representation of the scene based on the first image, and generate a second image of the scene in a second lighting condition based on the three-dimensional representation and on the first image. The first image is an only image of the scene used for generating the three-dimensional representation. The first image is an only image of the scene used for generating the second image.

Single-perspective image relighting

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset. The instructions can include further instructions to train a CNN to using the first and the second CNN training datasets.

Neural network image processing

A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

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San Jose, CA, United States of America

Xianling Zhang