Abstract: The present disclosure discusses dynamic supervised learning (DSL) and dynamic neural distribution function (DNDF) machine learning architectures and platforms. In contrast to existing ML approaches, DNDF accommodates a whole data structure via a neural network distribution function from which a decision boundary is born out. In particular, a neural network learning algorithm is used to extract a decision boundary while a neural distribution function is a neural data distribution approach wherein one or more decision boundaries are extracted among various distributions. Other aspects may be described and/or claimed.

Abstract: Methods, systems, and storage media are disclosed herein for determining objects in image data that match a plurality of captured images, in which one or more of the captured images from among the plurality of captured images may depict only a portion of an object. A computing device may extract a first feature from at least one block of the plurality of blocks from a captured image; determine a second feature; and determine a third feature. The computing device may determine, as a matching image, a stored image from among a plurality of stored images including an object that has greatest maximum correlation with the third feature. The computing device may similarly determine a matching image for each of the captured images of the plurality of images; and determine a final matching image from among the candidate matching images that best matches the combination of the plurality of captured images.

Abstract: Methods, systems, and storage media are described for detecting objects in image data are provided. In embodiments, a computing device may generate a plurality of blocks from a captured image. Each block may represent a corresponding region of the captured image. The computing device may extract a first feature from at least one block of the plurality of blocks; determine a second feature based at least on the first feature; and determine a third feature based on the second feature. The computing device may determine, as a matching image, a stored image from among a plurality of stored images including an object that has greatest maximum correlation with the third feature. Other embodiments may be described and/or claimed.

Abstract: Methods, systems, and storage media are disclosed herein for determining objects in image data that match a plurality of captured images, in which one or more of the captured images from among the plurality of captured images may depict only a portion of an object. A computing device may extract a first feature from at least one block of the plurality of blocks from a captured image; determine a second feature; and determine a third feature. The computing device may determine, as a matching image, a stored image from among a plurality of stored images including an object that has greatest maximum correlation with the third feature. The computing device may similarly determine a matching image for each of the captured images of the plurality of images; and determine a final matching image from among the candidate matching images that best matches the combination of the plurality of captured images.