Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

The invention relates to a device and a method for performing an eye gaze mapping (M), in which at least one point of vision (B) and/or a viewing direction of at least one person (10) in relation to at least one scene recording (S) of a scene (12) viewed by the at least one person (10) is mapped onto a reference (R). At least a part of an algorithm (A1, A2, A3) for performing the eye gaze mapping (M) is thereby selected from multiple predetermined algorithms (A1, A2, A3) as a function of at least one parameter (P), and the eye gaze mapping (M) is performed on the basis of the at least one part of the algorithm (A1, A2, A3).

The invention relates to a device and a method for performing an eye gaze mapping (M), in which at least one point of vision (B) and/or a viewing direction of at least one person (10) in relation to at least one scene recording (S) of a scene (12) viewed by the at least one person (10) is mapped onto a reference (R). At least a part of an algorithm (A1, A2, A3) for performing the eye gaze mapping (M) is thereby selected from multiple predetermined algorithms (A1, A2, A3) as a function of at least one parameter (P), and the eye gaze mapping (M) is performed on the basis of the at least one part of the algorithm (A1, A2, A3).

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

There is provided a method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of the image, a depth value, performing a local background enclosure calculation on the input image to determine salient object information, and generating a visual stimulus to visualise the salient object information using the visual stimulation device. Determining the salient object information is based on a spatial variance of at least one of the multiple points of the image in relation to a surface model that defines a surface in the input image.

There is provided a method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of the image, a depth value, performing a local background enclosure calculation on the input image to determine salient object information, and generating a visual stimulus to visualise the salient object information using the visual stimulation device. Determining the salient object information is based on a spatial variance of at least one of the multiple points of the image in relation to a surface model that defines a surface in the input image.

A theme icon generation method includes obtaining an application icon, where the application icon includes a transparent region and an opaque region, and the opaque region includes an icon background and a first logo graphic; segmenting a first logo graphic from the opaque region; adjusting a size of the first logo graphic to generate a second logo graphic; and fusing the second logo graphic with a theme template to generate a theme icon