A method for semantic annotation of sensor data may include obtaining sensor data representing an image of a geographic area. The boundary points defining a first polygon in the image of the geographic area may be determined based on the sensor data. An overlap between the first polygon and a second polygon in the image of the geographic area may be detected based at least on the boundary points defining the first polygon. At least one of the first polygon or the second polygon may be modified to remove the overlap between the first polygon and the second polygon. An annotation corresponding to the first polygon may be generated based on the modifying of at least one of the first polygon or the second polygon. The annotation may identify a physical feature within the geographic area. Related systems and computer program products are also provided.

There is provided a learning method and a learning device for performing transfer learning on an object detector that has been trained to detect first object classes such that the object detector is able to detect second object classes. Further, a testing method and a testing device are provided to allow at least part of the first object classes and the second object classes to be detected by using the object detector having been trained through the transfer learning. Accordingly, a detection performance can be improved for the second object classes that cannot be detected through training data set corresponding to the first object classes.

According to one example for outputting image data, an image comprising a surface and an object are captured on a sensor. An object mask based on the captured image is created on a processor. A first composite image based on the object mask and a source content file is created. In an example, the first composite image is projected to the surface.

A line-of-sight detection method includes detecting an eye region of a subject from an image, determining whether a position of a boundary between a pupil and an iris is detected from the image based on a brightness change in the eye region, executing, in accordance with a result of the determining, at least one of first processing in which a position of a center of the pupil is detected based on an outline of the pupil and second processing in which the position of the center of the pupil is detected based on another outline of the iris, and detecting a line of sight of the subject based on the position of the center of the pupil detected by the at least one of the first processing and the second processing.

An example method of detecting an element using an autonomous vehicle includes the following operations: using a sensor on the autonomous vehicle to capture image data in a region of interest containing the element, where the image data represents components of the element; filtering the image data to produce filtered data having less of an amount of data than the image data; identifying the components of the element by analyzing the filtered data using a deterministic process; and detecting the element based on the components.

Receiving an iris image; detecting, among rows of the iris image, rows in each of which a number of consecutive pixels each having a brightness value above a first threshold value is larger than a second threshold value; detecting, among columns of the iris image, columns in each of which a number of consecutive pixels each having a brightness value above the first threshold value is larger than the second threshold value; selecting, among the detected rows, consecutive rows in a vertical direction whose number is larger than a third threshold; selecting, among the detected columns, consecutive columns in a horizontal direction whose number is larger than the third threshold and determining a set of the pixels as a glint if the set of pixels included in the selected rows and the selected columns and each having the brightness value above the first threshold has a predetermined shape.

A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

A boundary map and a first image are received as input. The boundary map indicates a plurality of fields. The boundary map is used to regularize, across a set comprising a plurality of pixels bounded by a first field in the plurality of fields, a set of one or more pixel brightness values. A regularized image is generated as output, where for each pixel in the first image bounded by the first field, a set of values in the corresponding pixel in the regularized image is assigned the set of regularized pixel brightness values.

A first image is received. An initial label is assigned to at least some pixels in the first image, including by assigning a first label to a first pixel. A determination is made, based at least in part on labels of spectral neighbors of the first pixel, that the first pixel's label should be replaced with a different label. The first pixel's label is updated with the different label. The first pixel's label is iteratively refined until convergence.

Human Computer Interfaces (HCI) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCI system to be used to recognize such gestures. Particularly, the HCI system's depth sensor may capture depth frames of the user's movements over time. To discern gestures from these movements, the system may group portions of the user's anatomy represented by the depth data into classes. Features which reflect distinguishing features of the user's anatomy may be used to accomplish this classification. Some embodiments provide improved systems and methods for generating and/or selecting these features. Features prepared by various of the disclosed embodiments may be less susceptible to overfitting training data and may more quickly distinguish portions of the user's anatomy.