A system for detecting boundaries of lanes on a road is presented. The system includes an imaging system configured to produce a set of pixels associated with lane markings on a road. The system also includes one or more processors configured to detect boundaries of lanes on the road, including: receive, from the imaging system, the set of pixels associated with lane markings; partition the set of pixels into a plurality of groups, each of the plurality of groups associated with one or more control points; and generate a first spline that traverses the control points of the plurality of groups, the first spline describing a boundary of a lane on the road.

Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

The present disclosure includes systems and methods for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text is determined based on the matching.

The present disclosure includes systems and methods for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text is determined based on the matching.

A method of determining an arrangement for objects comprises obtaining first data representing a first arrangement of first objects in a scene; inputting the obtained first data into a trained machine learning model to determine a first cost value for the first arrangement. The first cost value is indicative of an extent to which the first arrangement differs from an optimum arrangement of the first objects according to a cost function that the machine learning model has been trained to provide. The method comprises determining a second arrangement for the first objects based on the first cost value.

The present invention correlates information, to be processed, about an organ and/or a disease, etc., obtained from a medical image and anatomical/functional medical knowledge information, and enables the information obtained from the medial image to be effectively utilized in medical examination and treatment processes. In a medical image information system (101), an image processing unit (103) processes an image, a graph model creation unit (104) creates a graph data model from the information obtained from the image, a graph data model processing unit (106) acquires a graph data model based on anatomical/functional medical knowledge, compares with each other and integrates the graph data models and stores an integrated graph data model, and a display processing unit (110) displays the integrated graph data model, whereby the effective use of information obtained from the image is made possible.

An object recognition system is provided. The object recognition system for recognizing an object may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, by using a classification tree. The object recognition system may include a classification tree learning apparatus to generate the classification tree.

Methods and systems for analyzing a medical image of a subject's brain are disclosed. Analysis of a medical image of a subject's brain for predictive and diagnostic determination of neurodegenerative disease state. The method comprises parcellating the grey matter in the image of the brain and determining the size of each region to generate an initial pattern of the disease process; applying a diffusion kernel to obtain an output vector; and predicting future changes to the brain based on the output vector. Another method of analyzing a medical image of a subject's brain includes solving for eigen-modes of a connectivity matrix, projecting the eigen-modes onto the initial disease state to produce an output product and diagnosing a disease or lack thereof based on a comparison of the output product to one or more reference standards.

An information processing apparatus includes a memory, an accepting unit, a determining unit, and a selecting unit. The memory stores a template collection. The memory associatively stores, for each template, the template and a degree of first impression similarity indicating an impression of the template. The accepting unit accepts an image. The determining unit determines an impression of the accepted image. The selecting unit selects, from the template collection, a template that is in harmony with the image by using a degree of second impression similarity indicating the impression of the image, and the degree of first impression similarity.