Abstract: A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

Abstract: An image of a region captured by an unmanned aerial vehicle flying at an altitude may be received. A computer vision algorithm may be executed with the image as an input to compute an overall confidence score associated with detecting one or more candidate objects in the image. Responsive to determining that the confidence score is below a predefined minimum threshold or above a predefined maximum threshold, the unmanned aerial vehicle may be controlled to change its altitude and recapture the image of the region at a new position. Responsive to determining that the overall confidence score is not below the predefined minimum threshold, information associated with the image may be stored on a storage device.

Abstract: A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

Abstract: A system for automatically generating an athletic training schedule includes a wearable device having one or more sensors for determining a quantity of athletic training performed by a user wearing the wearable device and an athletic performance of the user. A processor is configured to receive the quantity of athletic training performed and the athletic performance data from the wearable device, and to estimate a relationship between the quantity of athletic training performed and the athletic performance. A selection of a competitive target is received. A performance level of the selected competitive target is estimated. A minimum level of training needed to meet or exceed the estimated performance level is determined based on the estimated relationship between the quantity of athletic training performed and the athletic performance. The athletic training schedule is automatically generated based on the determined minimum level of training.

Abstract: A system, method and program product for implementing a sketch-based retrieval system. A system is disclosed that includes: an interface having a canvas for entering inputs including user generated strokes, and extracting features associated with the inputs; a hierarchy system for assigning an importance to the inputs; a matching system for retrieving matches from a database based on extracted features and importance associated with the extracted features; a feedback system that provides real-time feedback on the canvas to suggest improved inputs for the user; and a ranking system that ranks retrieved matches for output in the interface by incorporating user interaction history.

Abstract: An image of a region captured by an unmanned aerial vehicle flying at an altitude may be received. A computer vision algorithm may be executed with the image as an input to compute an overall confidence score associated with detecting one or more candidate objects in the image. Responsive to determining that the confidence score is below a predefined minimum threshold or above a predefined maximum threshold, the unmanned aerial vehicle may be controlled to change its altitude and recapture the image of the region at a new position. Responsive to determining that the overall confidence score is not below the predefined minimum threshold, information associated with the image may be stored on a storage device.

Abstract: A system, method and program product for implementing a sketch-based retrieval system. A system is disclosed that includes: an interface having a canvas for entering inputs including user generated strokes, and extracting features associated with the inputs; a hierarchy system for assigning an importance to the inputs; a matching system for retrieving matches from a database based on extracted features and importance associated with the extracted features; a feedback system that provides real-time feedback on the canvas to suggest improved inputs for the user; and a ranking system that ranks retrieved matches for output in the interface by incorporating user interaction history.

Abstract: A system, method and program product for implementing a sketch-based retrieval system. A system is disclosed that includes: an interface having a canvas for entering inputs including user generated strokes, and extracting features associated with the inputs; a hierarchy system for assigning an importance to the inputs; a matching system for retrieving matches from a database based on extracted features and importance associated with the extracted features; a feedback system that provides real-time feedback on the canvas to suggest improved inputs for the user; and a ranking system that ranks retrieved matches for output in the interface by incorporating user interaction history.

Abstract: A system for automatically generating an athletic training schedule includes a wearable device having one or more sensors for determining a quantity of athletic training performed by a user wearing the wearable device and an athletic performance of the user. A processor is configured to receive the quantity of athletic training performed and the athletic performance data from the wearable device, and to estimate a relationship between the quantity of athletic training performed and the athletic performance. A selection of a competitive target is received. A performance level of the selected competitive target is estimated. A minimum level of training needed to meet or exceed the estimated performance level is determined based on the estimated relationship between the quantity of athletic training performed and the athletic performance. The athletic training schedule is automatically generated based on the determined minimum level of training.