Abstract: Computer-aided categorization or classification of numerous data records can be controlled and guided through a user interface that accepts user input to produce clustering training data, and that conveys the improved automatic classification results efficiently to the user. Features that facilitate working with thousands or millions of data records are described and claimed.

Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy. Portions of the algorithm that are independent are distributed among a number of cooperating computing nodes so that the full algorithm can be completed in less time.

Abstract: Neural networks of suitable topology are trained with sets of images, where one image of each set depicts a garment and another pair of images of each set depicts an item of apparel from multiple viewpoints, and a final image of each set depicts a model wearing the garment and the other item of apparel. Once trained, the neural network can synthesize a new image based on input images including an image of a garment and a pair of images of another item of apparel. Quantitative parameters controlling the image synthesis permit adjustment of features of the synthetic image, including skin tone, body shape and pose of the model, as well as characteristics of the garment and other items of apparel.

Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy.

Abstract: Neural networks of suitable topology are trained with sets of images, where one image of each set depicts a garment and another pair of images of each set depicts an item of apparel from multiple viewpoints, and a final image of each set depicts a model wearing the garment and the other item of apparel. Once trained, the neural network can synthesize a new image based on input images including an image of a garment and a pair of images of another item of apparel. Quantitative parameters controlling the image synthesis permit adjustment of features of the synthetic image, including skin tone, body shape and pose of the model, as well as characteristics of the garment and other items of apparel.

Abstract: A subject's head position and motion can be tracked by analyzing a series of frames from a monocular camera and mapping distinguishing points visible in the frames onto an elliptical cylinder. The tracking data can be used to control a physical pan/tilt actuator or to reconfigure/reposition virtual objects, images of which can be synthesized and displayed, or composited back into the original frames and displayed.

Abstract: Neural networks of suitable topology are trained with pairs of images, where one image of each pair depicts a garment, and the other image of each pair depicts the garment being worn by a model. Once trained, the neural network can synthesize an image based on a new image of a garment, where the synthesized image could plausibly have appeared in the training set, paired with the new image of the garment. Quantitative parameters controlling the image synthesis permit adjustment of features of the synthetic image, including the skin tone, body shape and pose of the model, accessories depicted in the synthetic image, and characteristics of the garment as depicted, such as length, sleeve style, collar style or tightness.

Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy. Portions of the algorithm that are independent are distributed among a number of cooperating computing nodes so that the full algorithm can be completed in less time.

Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy.

Abstract: Neural networks of suitable topology are trained with pairs of images, where one image of each pair depicts a garment, and the other image of each pair depicts the garment being worn by a model. Once trained, the neural network can synthesize an image based on a new image of a garment, where the synthesized image could plausibly have appeared in the training set, paired with the new image of the garment. Quantitative parameters controlling the image synthesis permit adjustment of features of the synthetic image, including the skin tone, body shape and pose of the model, accessories depicted in the synthetic image, and characteristics of the garment as depicted, such as length, sleeve style, collar style or tightness.

Abstract: A subject's head position and motion can be tracked by analyzing a series of frames from a monocular camera and mapping distinguishing points visible in the frames onto an elliptical cylinder. The tracking data can be used to control a physical pan/tilt actuator or to reconfigure/reposition virtual objects, images of which can be synthesized and displayed, or composited back into the original frames and displayed.