Abstract: Systems and methods to automatically generate training data including airborne object trajectory data may receive real trajectory data from within a first environment, receive imaging data associated with a second environment, and transform and/or modify the real trajectory data to synthetic trajectory data for the second environment. Then, the synthetic trajectory data may be superimposed within the imaging data of the second environment. In addition, images of an airborne object may be rendered along the synthetic trajectory data to generate training data that may be used to train machine learning models or algorithms for various computer vision applications.

Abstract: Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Abstract: A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Abstract: Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Abstract: A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Abstract: A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Abstract: Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for dynamic read table generation. One apparatus includes a set of non-volatile storage cells. A controller for a set of non-volatile storage cells is configured to, in response to unsuccessfully reading a storage cell of the set of non-volatile storage cells using a parameter, read the storage cell using one or more shifted values. A controller for a set of non-volatile storage cells is configured to, in response to successfully reading a storage cell using one or more shifted values, add the one or more shifted values to a storage device.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for dynamic read table generation. One apparatus includes a set of non-volatile storage cells. A controller for a set of non-volatile storage cells is configured to, in response to unsuccessfully reading a storage cell of the set of non-volatile storage cells using a parameter, read the storage cell using one or more shifted values. A controller for a set of non-volatile storage cells is configured to, in response to successfully reading a storage cell using one or more shifted values, add the one or more shifted values to a storage device.