A method of diagnosing breast cancer, the method comprising: acquiring a contrast enhanced region of interest (CE-ROI) comprised in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; generating a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI; and using a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

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.

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.

Methods, non-transitory computer-readable storage mediums and systems for classifying features including labels or fields in digital images of forms and identifying any unclassified features utilizing a bitmap approach are provided. Each spatial template includes a set of features and a set of bitmaps, each bitmap indicating a spatial relationship between each feature and respective remaining features in each spatial template. The operations include selecting a spatial template from the spatial templates, classifying each extracted feature as one of features in the spatial template. The operations include generating at least one bitmap representing a spatial relationship for each unclassified feature in relation to classified features, comparing the at least one bitmap with each of a set of bitmaps in the selected spatial template, and identifying the at least one unclassified feature as one of features in the spatial template based on a result of the comparison.

Systems and methods for detecting document contours in a digital image. An example method may involve: receiving an image comprising one or more documents and detecting a set of lines in the image; identifying a plurality of intersection points corresponding to the set of lines; determining a vanishing point based on the plurality of intersection points; generating a candidate polygon based on the set of lines; evaluating the candidate polygon based on a parameter associated with the vanishing point; and indicating the candidate polygon represents a contour of one of the one or more documents.

A method can include illuminating an interior of a fuel tank with one or more light pulses, and receiving reflected returns of the one or more light pulses at a light sensor array. The method can further include producing, by a processing device, three-dimensional image data of the interior of the fuel tank based on the received reflected returns, producing, by the processing device, a fuel measurement value representing an amount of fuel contained in the fuel tank based on the three-dimensional image data, and outputting, by the processing device, an indication of the fuel measurement value.

A compression system for compressing a video received from an imaging device, the compression system including a transformation estimation device configured to estimate, based on pixel transformations between a first frame and a second frame of the video, a transformation matrix, an encoding device configured to encode the second frame as an increment with the first frame based on the transformation matrix generated by the transformation device, a compression device configured to compress the increments into compressed data, and a reconstruction device configured to reconstruct the first frame and the second frame using the transformation matrix generated by the transformation estimation device.

A method for determining lane markers includes providing a camera at a vehicle so as to have a field of view exterior of the vehicle. An image processor processes a frame of image data captured by the camera to determine intensity gradient information of captured image data and to determine lane markers. Contrast values at both sides of a center region of the determined lane marker are determined via processing of the intensity gradient information. An angle of the determined lane marker relative to the direction of travel of the vehicle is determined responsive to the determined contrast values. Processing of a subsequent frame of captured image data is adjusted responsive to the determined angle of the determined lane marker relative to the direction of travel of the vehicle.

A vanishing point correction method may include the steps of: (a) extracting a contour candidate point of an object considered as the forward vehicle from the image taken by the camera; (b) tracking a movement of the contour candidate point over time; (c) determining whether the contour candidate point belongs to the vehicle; (d) setting the contour candidate point to a vehicle contour point of the forward vehicle, when it is determined at the step (c) that the contour candidate point belongs to the vehicle; (e) calculating a variation in width between vehicle contour points at the same height among the vehicle contour points; and (f) calculating a variation of the vanishing point.