A control apparatus includes a detection unit configured to detect an object area in an image captured by an image capturing apparatus, a dividing unit configured to divide the image into a plurality of divided areas, a first calculation unit configured to calculate a luminance difference as a difference between a luminance value of the object area and a luminance value of each of the divided areas, a second calculation unit configured to calculate a first amount of change as a difference between luminance values of the object area before and after the image capturing apparatus executes exposure control and a second amount of change as a difference between luminance values of the divided area before and after the image capturing apparatus executes exposure control, and a determination unit configured to determine a photometric area.

Provided are: an amodal segmentation unit that generates a set of first amodal masks indicating a probability that a particular pixel belongs to a relevant object for each of objects, with respect to an input image in which a plurality of the objects partially overlap; an overlap segmentation unit that generates an overlap mask corresponding only to an overlap region where the plurality of objects overlap in the input image based on an aggregate mask obtained by combining the set of first amodal masks generated for each of the objects and a feature map generated based on the input image; and an amodal mask correction unit that generates and outputs a second amodal mask, which includes an annotation label indicating a category of each of the objects corresponding to a relevant pixel, for each of pixels in the input image using the overlap mask and the aggregate mask.

This disclosure describes systems, methods, and devices related to automatic annotation. A device may capture data associated with an image comprising an object. The device may acquire input data associated with the object. The device may estimate a plurality of points within a frame of the image, wherein the plurality of point constitute a 3D bounding to around the object. The device may transform the plurality of points to two or more 2D points. The device may construct a bounding box that encapsulates the object using the two or more 2D points. The device may create a segmentation mask of the object using morphological techniques. The device may perform annotation based on the segmentation mask.

A computerized device for driving assistance comprises a memory (4) designed to receive data point cloud data (8) in which a point cloud associates, for a given instant, points each having coordinates in a plane associated with the point cloud and a value denoting a height. The device furthermore comprises a calculator (6) designed to access the memory (4) and, for a given point cloud, to calculate data on the probability of belonging to a reference surface, associated with each point of the data point cloud, on the one hand, and node data associating a value denoting a height (hi) and two values indicating a slope in a plane associated with the plane of the given point cloud, on the other hand, by determining a Gaussian random conditional field by way of the data point cloud data (8) corresponding to the given point cloud, which Gaussian random conditional field is represented by a mesh of nodes in said associated plane, which nodes are defined by the node data, and to return the data on the probability of belonging to a reference surface and/or at least some of the node data and values denoting a height.

Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: setting each of scores to each of a plurality of sets of unlabeled data with regard to each of labels used in a plurality of sets of labeled data based on a distance of each of the plurality of sets of unlabeled data with respect to each of the labels; and causing a learning model to learn using a neural network by using the plurality of sets of labeled data respectively corresponding to the labels of the plurality of sets of labeled data, and the plurality of sets of unlabeled data respectively corresponding to the scores of the plurality of sets of unlabeled data with regard to the labels.

Various implementations provide a 3D floor plan based on scanning a room and detecting windows, doors, and openings using 2D orthographic projection. Points of a dense set of points (e.g., a dense point cloud) that are close to a plane representing a wall are projected onto the plane and used to identify windows, doors, and opening on the wall. Representations of the detected windows, doors, and openings may then be positioned in a 3D floor plan based on the known position of the wall within the corresponding room, i.e., the location of the wall plane relative to the dense point cloud is known. Other aspects of a 3D floor plan may be detected directly from points of a dense 3D point cloud, windows, doors, and openings may be detected indirectly using projections of the points of the 3D point cloud onto a 2D plane, and the detected aspects may be combined into a single 3D floor plan.

Apparatus for binning an input value into an array of bins, each bin representing a range of input values and the bins collectively representing a histogram of input values, the apparatus comprising: an input for receiving the input value; a memory for storing the array; and a binning controller configured to: derive a plurality of bin values from the input value according to a binning distribution located about the input value, the binning distribution spanning a range of input values and each bin value having a respective input value dependent on the position of the bin value in the binning distribution; and allocate the plurality of bin values to a plurality of bins in the array, each bin value being allocated to a bin selected according to the respective input value of the bin value.

The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.

An example system includes a processor to train a neural network model using annotated training data to generate features. The processor is to select a feature vector of the neural network model. The processor is to execute an inference stage on the annotated training data via the neural network model to generate a first set of values corresponding to the annotated training data for features in the selected feature vector. The processor is to execute the inference stage on unannotated data to generate a second set of values corresponding to the unannotated data for the features in the selected feature vector. The processor is to select an item in unannotated data that matches an uncovered combination of feature values in the annotated training data. The processor is to send the selected item for annotation and receive a corresponding additional annotated item to be added to the annotated training data.