A system, article, and method to provide facial expression recognition using linear relationships within landmark subsets.

A method for performing registration plate detection includes: performing image processing on image data of a predetermined region in an image to generate an edge image; and performing registration plate detection according to the edge image and the image data of the predetermined region to determine a location of a registration plate image within the predetermined region, for performing a post-processing corresponding to the registration plate image. The step of performing the image processing includes: performing gradient calculations on image data of the predetermined region to generate a gradient image, where the gradient image includes gradient data of the predetermined region; performing edge threshold estimation on the gradient data of the predetermined region, to update an edge threshold through a set of iterative calculations; and generating the edge image according to the edge threshold and according to selective gradient data of the predetermined region.

A method for annotation of skin images includes receiving a plurality of dermatoscopic images. Each of the dermatoscopic includes a region of lesion skin and a region of normal skin. A first convolutional neural network is trained according to an interior of the region of lesion skin using each of the plurality of dermatoscopic images. A second convolutional neural network is trained according to a boundary between the region of lesion skin and the region of normal skin. An additional dermatoscopic image is acquired. The first and second convolutional neural networks are used to identify a region of lesion skin within the acquired additional dermatoscopic image.

An object recognizer of an image processing apparatus separately extracts a first feature point and a second feature point of a first line segment and a second line segment of which a variation is equal to or greater than a variation threshold value and the first feature point and the second feature point of the first line segment and the second line segment of which the variation is smaller than the variation threshold value, and determines a corresponding point in a second captured image corresponding to the first feature point of the first line segment of which the variation is equal to or greater than the variation threshold value as the second feature point of the second line segment corresponding to the first line segment of which the variation is equal to or greater than the variation threshold value to recognize an object.

A method is provided for assigning a bounding box to an object in an environment of a vehicle. Data related to objects located in the environment of the vehicle are acquired via a sensor. Based on the data, a respective spatial location and a respective size of a plurality of preliminary bounding boxes are determined such that each preliminary bounding box covers one of the objects at least partly. A respective velocity of each preliminary bounding box is estimated based on the data. A subset of the plurality of preliminary bounding boxes being related to a respective one of the objects is selected, where the subset is selected based on the respective velocity of each of the preliminary bounding boxes. A final bounding box is assigned to the respective one of the objects by merging the preliminary bounding boxes of the corresponding subset.

An eye open or closed state detection method, in addition to an eye opening feature, an iris shape feature and/or a vertical direction feature are added to identify the eye open or closed state, where the eye opening feature is used to represent an eye opening degree, the iris shape feature is used to represent a shape of an iris of an eye, and the vertical direction feature is used to represent a change degree of an eyelid curve.

An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

A method and apparatus for determining lane center line are provided. The method may include obtaining a target lane in a first road segment from a database, the first road segment being a road segment having a changed lane quantity, the target lane being an added or subtracted lane, two side lane lines of the target lane being respectively connected to two side lane lines of a first lane at two endpoints of a first end of the target lane, the two side lane lines of the target lane intersecting at an intersection point at a second end of the target lane and the intersection point being connected to a side lane line of a second lane.

An image processing device comprises pixel group setting part that sets plurality of pixel groups including first and second pixel groups, which are not connected to each other, based on image data of first image which shows target object including linear portion; pair setting part that performs setting pair of first and second pixel groups including pixel within predetermined range from first pixel group to calculate connecting degree of second pixel group with respect to first pixel group; and calculation part that calculates connecting degree of pair, wherein predetermined range is search range set based on first distance between first and second pixel groups, or search range set on based on first angle formed between first line segment corresponding to first pixel group and second line segment corresponding to second pixel group, and calculation part calculates first distance or first angle using first distance or first angle.

Methods and systems are provided for detecting a defect in a solar panel. The method includes initially imaging, via an infrared camera, a group of solar panels. Then, identifying, via a computer system configured for solar panel defect detection, the individual solar panels in the group of solar panels. Finally, identifying, via evaluation of an infrared image obtained by the infrared camera, a defect in at least one of the group of solar panels.