A moving object detection device includes: an image capturing unit with which a vehicle is equipped, and which is configured to obtain a captured image by capturing a view in a travel direction of the vehicle; a calculation unit configured to calculate, for each of first regions which are unit regions of the captured image, a first motion vector indicating movement of an image in the first region; an estimation unit configured to estimate, for each of one or more second regions which are unit regions each including first regions, a second motion vector using first motion vectors, the second motion vector indicating movement of a stationary object which has occurred in the captured image due to the vehicle traveling; and a detection unit configured to detect a moving object present in the travel direction, based on a difference between a first motion vector and a second motion vector.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.

Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.