Abstract: A method for training a neural network to perform assessments of image quality is provided. The method includes: inputting into the neural network at least one set of images, each set including an image and at least one degraded version of the image; performing comparative ranking of each image in the at least one set of images; and training the neural network with the ranking information. A neural network and image signal processing tuning system are disclosed.

Abstract: An apparatus and a method. The apparatus includes a dynamic vision sensor (DVS) configured to generate a stream of events, where an event includes a location and a binary value indicating a positive or a negative change in luminance; a sampling unit connected to the DVS and configured to sample the stream of events; and an image formation unit connected to the sampling unit and configured to form an image for each sample of the stream of events, wherein a manner of sampling by the sampling unit is adjusted to reduce variations between images formed by the image formation unit.

Abstract: An image signal processing (ISP) system is provided. The system includes a neural network trained by inputting a set of raw data images and a correlating set of desired quality output images; the neural network including an input for receiving input image data and providing processed output; wherein the processed output includes input image data that has been adjusted for at least one image quality attribute. A method and an imaging device are disclosed.

Abstract: A method for enhancing at least one image within a series of images is provided. The method includes: selecting the series of images; upscaling each image within the series of images; selecting a reference image among the series of images; performing image registration to align series of images with the reference image; evaluating the series of aligned images for a subset of pixel locations that exhibit high cross-frame variation; performing learning processing to substantially reduce noise and exclude motion biases at the subset of pixel locations; performing pixel fusion from the series of aligned and processed images to produce the super-resolution reference image. A computer program product and an imaging system are disclosed.

Abstract: A method for training a neural network to perform assessments of image quality is provided. The method includes: inputting into the neural network at least one set of images, each set including an image and at least one degraded version of the image; performing comparative ranking of each image in the at least one set of images; and training the neural network with the ranking information. A neural network and image signal processing tuning system are disclosed.

Abstract: A method for enhancing at least one image within a series of images is provided. The method includes: selecting the series of images; upscaling each image within the series of images; selecting a reference image among the series of images; performing image registration to align series of images with the reference image; evaluating the series of aligned images for a subset of pixel locations that exhibit high cross-frame variation; performing learning processing to substantially reduce noise and exclude motion biases at the subset of pixel locations; performing pixel fusion from the series of aligned and processed images to produce the super-resolution reference image. A computer program product and an imaging system are disclosed.

Abstract: A method for configuring a neural network is provided. The method includes: selecting a neural network including a plurality of layers, each of the layers including a plurality of neurons for processing an input and providing an output; and, incorporating at least one switch configured to randomly select and disable at least a portion of the neurons in each layer. Another method in the computer program product is disclosed.

Abstract: An embodiment includes a system, comprising a first memory; a plurality of first circuits, wherein each first circuit is coupled to the memory; and includes a second circuit configured to generate a first output value in response to an input value received from the first memory; and an accumulator configured to receive the first output value and generate a second output value; and a controller coupled to the memory and the first circuits, and configured to determine the input values to be transmitted from the memory to the first circuits.

Abstract: An embodiment includes a system, comprising a first memory; a plurality of first circuits, wherein each first circuit is coupled to the memory; and includes a second circuit configured to generate a first output value in response to an input value received from the first memory; and an accumulator configured to receive the first output value and generate a second output value; and a controller coupled to the memory and the first circuits, and configured to determine the input values to be transmitted from the memory to the first circuits.

Abstract: A Dynamic Vision Sensor (DVS) where pixel pitch is reduced to increase spatial resolution. The DVS includes shared pixels that employ Time Division Multiplexing (TDM) for higher spatial resolution and better linear separation of pixel data. The pixel array in the DVS may consist of multiple N×N pixel clusters. The N×N pixels in each cluster share the same differentiator and the same comparator using TDM. The pixel pitch is reduced (and, hence, the spatial resolution is improved) by implementing multiple adjacent photodiodes/photoreceptors that share the same differentiator and comparator units using TDM. In the DVS, only one quarter of the whole pixel array may be in use at the same time. A global reset may be done periodically to switch from one quarter of pixels to the other for detection. Because of higher spatial resolution, applications such as gesture recognition or user recognition based on DVS output entail improved performance.

Abstract: A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.

Abstract: A system and method for object classification based upon the fusion of a radar system and a natural imaging device using sparse code representation. The radar system provides a means of detecting the presence of an object within a predetermined path of a vehicle. Detected objects are then fused with the image gathered by the camera and then isolated in an attention window. The attention window is then transformed into a sparse code representation of the object. The sparse code representation is then compared with known sparse code representation of various objects. Each known sparse code representation is given a predetermined variance and subsequent sparse code represented objects falling within said variance will be classified as such. The system and method also includes an associative learning algorithm wherein classified sparse code representations are stored and used to help classifying subsequent sparse code representation.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.

Abstract: A system and method for object classification based upon the fusion of a radar system and a natural imaging device using sparse code representation. The radar system provides a means of detecting the presence of an object within a predetermined path of a vehicle. Detected objects are then fused with the image gathered by the camera and then isolated in an attention window. The attention window is then transformed into a sparse code representation of the object. The sparse code representation is then compared with known sparse code representation of various objects. Each known sparse code representation is given a predetermined variance and subsequent sparse code represented objects falling within said variance will be classified as such. The system and method also includes an associative learning algorithm wherein classified sparse code representations are stored and used to help classifying subsequent sparse code representation.