Abstract: Systems and methods are provided for updating data in a computer network. An exemplary method includes: receiving input data from at least one device; performing an extraction operation on the input data to extract at least one feature; producing at least one feature vector based on the at least one feature; performing a similarity analysis between the at least one feature vector and a plurality of other feature vectors from a plurality of autoencoders; selecting a first autoencoder from the plurality of autoencoders demonstrating significant similarity with at least one feature vector; determining whether the input data exhibits a recurring drift or a new drift; and training a new autoencoder using at least a portion of the input data.

Abstract: In a method for distributing scheduling of a task within a plurality of nodes, a plurality of interconnected nodes is provided, each interconnected node accessing its own node database of performance data about the other nodes in the plurality. A first node in the plurality determines a task to be run by a node other than the first node, by a time deadline. A request to execute the task is sent from the first node to each node in a set of worker nodes. If task execution is completed before the time deadline, the set of worker nodes is notified. A penalty is associated at the first node, if the first node does not receive an indication that task execution was completed before time deadline. Performance data is updated for each worker node, indicating whether there was task execution success by the time deadline.

Abstract: A modeling engine (ME) for generating or “bootstrapping” a three dimensional edge model (3DEM) from two stereo pairs of images and correcting engine (CE) for correcting a camera model associated with an image are provided. The ME back projects edge detected images into 3DEMs using camera models associated with the stereo images, updates and stereo updates voxel probabilities in the 3DEM s with back projections of the edge detected images, and then merges the 3DEMs together to create a “sparse” 3DEM. The CE calculates a registration solution mapping an edge detected image of an image to an expected edge image. The expected edge image is a projection of a 3DEM using a camera model associated with the image. The CE corrects the camera model by applying the registration solution to the camera model based on determining whether the registration solution is a high confidence registration solution.

Abstract: A modeling engine (ME) for generating or “bootstrapping” a three dimensional edge model (3DEM) from two stereo pairs of images and correcting engine (CE) for correcting a camera model associated with an image are provided. The ME back projects edge detected images into 3DEMs using camera models associated with the stereo images, updates and stereo updates voxel probabilities in the 3DEM s with back projections of the edge detected images, and then merges the 3DEMs together to create a “sparse” 3DEM. The CE calculates a registration solution mapping an edge detected image of an image to an expected edge image. The expected edge image is a projection of a 3DEM using a camera model associated with the image. The CE corrects the camera model by applying the registration solution to the camera model based on determining whether the registration solution is a high confidence registration solution.

Abstract: A modeling engine (ME) for generating or “bootstrapping” a three dimensional edge model (3DEM) from two stereo pairs of images and correcting engine (CE) for correcting a camera model associated with an image are provided. The ME back projects edge detected images into 3DEMs using camera models associated with the stereo images, updates and stereo updates voxel probabilities in the 3DEMs with back projections of the edge detected images, and then merges the 3DEMs together to create a “sparse” 3DEM. The CE calculates a registration solution mapping an edge detected image of an image to an expected edge image. The expected edge image is a projection of a 3DEM using a camera model associated with the image. The CE corrects the camera model by applying the registration solution to the camera model based on determining whether the registration solution is a high confidence registration solution.

Abstract: A modeling engine (ME) for generating or “bootstrapping” a three dimensional edge model (3DEM) from two stereo pairs of images and correcting engine (CE) for correcting a camera model associated with an image are provided. The ME back projects edge detected images into 3DEMs using camera models associated with the stereo images, updates and stereo updates voxel probabilities in the 3DEMs with back projections of the edge detected images, and then merges the 3DEMs together to create a “sparse” 3DEM. The CE calculates a registration solution mapping an edge detected image of an image to an expected edge image. The expected edge image is a projection of a 3DEM using a camera model associated with the image. The CE corrects the camera model by applying the registration solution to the camera model based on determining whether the registration solution is a high confidence registration solution.