A method comprising obtaining, from a sensor, depth data representing a target object; selecting a model to fit to the depth data; for each data point in the depth data: defining a ray from a location of the sensor to the data point; and determining an error based on a distance from the data point to the model along the ray; when the depth data does not meet a similarity threshold for the model based on the determined errors, selecting a new model and repeating the error determination for the depth data based on the new model; when the depth data meets the similarity threshold for the model, selecting the model as representing the target object; and outputting the selected model representing the target object.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image segmentation and chemical analysis using machine learning. In some implementations, a system obtains a hyperspectral image that includes a representation of an object. The system segments the hyperspectral image to identify regions of a particular type on the object. The system generates a set of feature values derived from image data for different wavelength bands that is located in the hyperspectral image in the identified regions of the particular type. The system generates a prediction of a level of one or more chemicals in the object based on an output produced by a machine learning model in response to the set of feature values being provided as input to the machine learning model. The system provides data indicating the prediction of the level of the one or more chemicals in the object.

The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.

The invention relates to a computer-implemented method of tracking animals is provided. Such a method typically includes recognizing, by using at least one data processor, individual animals in images of a plurality of the animals; and tracking the animals using a probabilistic tracking-by-detection process. In another aspect, a system for recognizing animals is provided. Such a system typically includes an instance detection and part localization module; a visual marker classification module; a fixed-cardinality track interpolation module; and a maximum a posteriori estimation of animal identity module.

An object position estimation device (1) is provided with: a feature extraction unit (10) including a first feature extraction unit (21) which generates a first feature map by subjecting a target image to a convolution computation process, and a second feature extraction unit (22) which generates a second feature map by also subjecting the first feature map to the convolution computation process; and a likelihood map estimation unit (20) including a first position likelihood estimation unit (23) which, by using the first feature map, estimates a first likelihood map indicating the probability that first objects having a first size are present in the target image, and a second position likelihood estimation unit (24) which, by using the second feature map, estimates a second likelihood map indicating the probability that second objects having a second size, which is greater than the first size, are present in the target image.

A biological tissue is classified using a computing system. Image data comprising a plurality of images of an examination area of a biological tissue is received at the computing system. Each of the plurality of images is captured at different times during a period in which topical application of a pathology differentiating agent to the examination area of the tissue causes transient optical effects. The received image data is provided as an input to a machine learning algorithm operative on the computing system. The machine learning algorithm is configured to allocate one of a plurality of classifications to each of a plurality of segments of the tissue.

Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.