In a video conference system in which at least a pair of terminal devices transmits and receives an image through a network, each of the terminal devices includes a face detection unit that detects a face from a first image which is a image captured by a camera, and a generation unit that generates a image in which a image of the face detected by the face detection unit is arranged around a image region of a second image, which is a image of a material used for a conference, in accordance with a relative position of the face in the first image.

The present disclosure provides a height measurement method based on monocular machine vision. The method includes: picking up, by an RGB camera arranged on the head of a robot, a two-dimensional identifier from the head to feet of a person under measurement; calculating, by the robot, a homography matrix of a current visual field according to four corner points on the visual location identifier; acquiring a head image region by segmenting the image, and calculating pixel coordinates of a head vertex; and calculating a height of the person under measurement. The height measurement method based on monocular machine vision according to the present disclosure is simple in operation and calculation. The height of a person under measurement may be measured by himself or herself with no assistance from others. The measurement method features non-contact. The method further improves the measurement precision, and enhances the measurement speed.

The present invention provides a classifying method of classifying an article into one of a plurality of groups based on an image of the article, comprising determining an evaluation method for obtaining an evaluation value of an image by using at least some of sample images, obtaining evaluation values for the sample images by the determined evaluation method, changing the evaluation method so as to increase a degree of dissimilarity in an evaluation value range for sample images between the plurality of groups by changing a evaluation value of at least one sample image having a singular evaluation value among the sample images, obtaining an evaluation value for the image of the article using the changed evaluation method, and classifying the article into one of the plurality of groups based on the evaluation value for the image of the article.

A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

A method, non-transitory computer readable medium and apparatus for detecting a window in a display are disclosed. For example, the method includes scanning pixels within each frame of a plurality of frames that is displayed via a sliding window, extracting one or more features from each pixel within the sliding window, applying a classification function to classify a subset of potential pixels comprising the each pixel within the sliding window as corner pixels based on the one or more features that match predefined features associated with the corner pixels and detecting the window based on the corner pixels and additional pixels within a boundary defined by the corner pixels.

An apparatus and method for detecting an object using a multi-directional integral image are disclosed. The apparatus includes an area segmentation unit, an integral image calculation unit, and an object detection unit. The area segmentation unit places windows having a size of x*y on a full image having w*h pixels so that they overlap each other at their edges, thereby segmenting the full image into a single area, a double area and a quadruple area. The integral image calculation unit calculates a single directional integral image for the single area, and calculates multi-directional integral images for the double and quadruple areas. The object detection unit detects an object for the full image using the single directional integral image and the multi-directional integral images.

The present invention relates to an unmanned aerial vehicle having an automatic tracking function and a control method thereof, the unmanned aerial vehicle comprising: an image input unit for acquiring an image of a peripheral image of a subject to be photographed; an object recognition unit for extracting a region of interest using the image acquired through the image input unit, detecting a specific region located within the region of interest to measure coordinates, and recognizing the specific region as an object to be tracked; an object tracking unit for calculating and tracking a position of the object to be tracked recognized by the object recognition unit using a tracking learning detection (TLD) learning algorithm and generating a drive command for driving the unmanned aerial vehicle corresponding to the position; a motion recognition unit for recognizing a motion of the object to be tracked and generating a driving command corresponding to a photographing mode, a moving picture photographing mode, and a return mode; and a drive control unit for driving the unmanned aerial vehicle according to the drive command. Due to this feature, the present invention has an effect of enabling autonomous flight of an unmanned aerial vehicle by recognizing and automatically tracking an object to be tracked.

A face detecting and tracking method includes: acquiring an image and performing a depth detection for the image to obtain a depth value of each pixel of the image; determining one or more face candidate areas based on depth value of each pixel of the image of current frame; performing a face detection to the one or more face candidate areas to determine one or more face boxes of the image of current frame; and determining a tracking box of the image of current frame based on the one or more face boxes and a tracked face box, and tracking the face in the tracking box of the image of current frame.

A face detecting and tracking method includes: acquiring an image and performing a depth detection for the image to obtain a depth value of each pixel of the image; determining one or more face candidate areas based on depth value of each pixel of the image of current frame; performing a face detection to the one or more face candidate areas to determine one or more face boxes of the image of current frame; and determining a tracking box of the image of current frame based on the one or more face boxes and a tracked face box, and tracking the face in the tracking box of the image of current frame.

A method, non-transitory computer readable medium and apparatus for detecting a window in a display are disclosed. For example, the method includes scanning pixels within each frame of a plurality of frames that is displayed via a sliding window, extracting one or more features from each pixel within the sliding window, applying a classification function to classify a subset of potential pixels comprising the each pixel within the sliding window as corner pixels based on the one or more features that match predefined features associated with the corner pixels and detecting the window based on the corner pixels and additional pixels within a boundary defined by the corner pixels.