A method is disclosed including: receiving raw image data corresponding to a series of raw images; processing the raw image data with an encoder to generate encoded data, where the encoder is characterized by an input/output transformation that substantially mimics the input/output transformation of one or more retinal cells of a vertebrate retina; and applying a first machine vision algorithm to data generated based at least in part on the encoded data.

A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.

A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.

A signal processing appliance is disclosed that will simultaneously process the image data sets from disparate types of imaging sensors and data sets taken by them under varying conditions of viewing geometry, environmental conditions, lighting conditions, and at different times. Processing techniques that emulate how the human visual path processes and exploits data are implemented. The salient spatial, temporal, and color features of observed objects are calculated and cross-correlated over the disparate sensors and data sets to enable improved object association, classification and recognition. The appliance uses unique signal processing devices and architectures to enable near real-time processing.

A computer readable storage medium is provided. When contents of the computer readable storage medium are executed by a processor, multi-photon imaging may be performed on a histopathological section containing tumor environment information, and pathological partitioning of a tumor microenvironment may be further performed through image processing. A value of each collagen feature parameters, such as a morphological feature parameter, an energy feature parameter and a texture feature parameter, may be extracted from a tumor tissue region, an invasive margin (IM) region and a normal tissue (N) region. An inter-region difference and a variation may be calculated according to feature parameters of regions. A collagen feature scoring model may be established. A collagen feature score may be calculated with the collagen feature parameters input to the model.

Disclosed herein are an apparatus and method for classifying clothing attributes based on deep learning. The apparatus includes memory for storing at least one program and a processor for executing the program, wherein the program includes a first classification unit for outputting a first classification result for one or more attributes of clothing worn by a person included in an input image, a mask generation unit for outputting a mask tensor in which multiple mask layers respectively corresponding to principal part regions obtained by segmenting a body of the person included in the input image are stacked, a second classification unit for outputting a second classification result for the one or more attributes of the clothing by applying the mask tensor, and a final classification unit for determining and outputting a final classification result for the input image based on the first classification result and the second classification result.

A friction estimation system for estimating friction-related data associated with a surface on which a vehicle travels, may include a camera array including a plurality of imagers configured to capture image data associated with a surface on which a vehicle travels. The image data may include light data associated with the surface. The friction estimation system may also include an image interpreter in communication with the camera array and configured to receive the image data from the camera array and determine friction-related data associated with the surface based, at least in part, on the image data. The image interpreter may be configured to be in communication with a vehicle control system and provide the friction-related data to the vehicle control system.

The systems and methods disclosed herein include a path integration system that calculates optic flow, infers angular velocity from the flow field, and incorporates this velocity estimate into heading calculations. The resulting system fuses heading estimates from accelerometers, 5 gyroscopes, engine torques, and optic flow to determine self-localization. The system also includes a motivational system that implements a reward drive, both positive and negative, into the system. In some implementations, the drives can include: a) a curiosity drive that encourages exploration of new areas, b) a resource drive that attracts the agent towards the recharging base when the battery is low, and c) a mineral reward drive that attracts the agent 10 towards previously explored scientific targets.

The present invention relates to a cerebral function state evaluation device, which comprises a brain oxyhemoglobin concentration variation acquiring component, acquiring brain oxyhemoglobin concentrations of a stroke patient, who is in a phase of completing finger-nose and heel-knee-tibia tests under instruction, by applying near-infrared spectroscopic brain imaging technology; a brain functional network constructing component; a typical feature acquiring component; and an evaluation model establishing component. The cerebral function state evaluation device evaluates a patient's motor ability based on brain hemoglobin information. By using the proposed evaluation device, an evaluation result can be given only if a patient completes several required actions. The device is inventive and simple to operate, and subjective factors in the process of the scale scoring can be avoided.

An object-detection system configured to form an object-detection model generated by machine learning to detect an object from an image, the object-detection system includes an edge computer configured to extract a feature from a reduced image in which an input image is reduced to a predetermined size, and compress and transmit the feature, and a server configured to decode the feature, and perform object-detection for each of divided features into which the feature is divided in association with respective divided images into which the reduced image is divided with overlapping regions in a first size, the divided features including a second size that depends on a division position in the object-detection model, wherein the predetermined size is determined based on the first size of the overlapping regions and the second size of the divided features, and wherein the object-detection model is divided into the edge computer and the server.