A method for facilitating provision of food product information is provided. The method comprises providing a food package, wherein the food package is provided with a visual marker, capturing, by a camera of a user device, authentic image data depicting the visual marker, extracting marker data from the authentic image data, retrieving artificial image data linked to the marker data from an artificial image database, wherein the artificial image data depicts at least one food product component of a food product held in the food package, determining an artificial image spatial reference point based on the visual marker, generating augmented image data by combining the authentic image data, the artificial image data and the artificial image spatial reference point, providing the augmented image data to a screen of the user device.

A spatial orientation determining system includes an imaging device configured for obtaining an image of a surgical site, a surgical tool defining a central axis, a processor, and a memory. The surgical tool configured for operating at the surgical site and disposed thereon, a fiducial marker generated by a machine learning network. The fiducial marker includes a distinct pattern. The memory, includes instructions which when executed by the processor, cause the system to: access an image from the imaging device, the image including a portion of the fiducial marker; determine a spatial positioning of the surgical tool based on at least a visible portion of the fiducial marker and the distinct pattern; and determine, based on the spatial positioning, a positioning, an orientation, and/or a rotational angle of the surgical tool.

A method for collection and dissemination of biologic data, comprising collecting at least one biologic sample by a testing device including thereon an alignment target and including a plurality of immunoassay test strips, wherein the at least one biologic sample contacts a sample pad on at least one of the plurality of immunoassay test strips, assigning correlative values as test results, wherein each test performed on the biologic sample is assigned a different correlative value, receiving the test results at a server disposed on a network, wherein the server has configured thereon a database, assigning a unique identification to the biologic sample, storing the unique identification in the database, storing the test results in the database in association with the unique identification of the biologic sample, and providing access to the database to healthcare organizations for analysis of the test results.

A system includes a sensor and a client. The client receives a set of frames of top-view depth images generated by the sensor. The client identifies a frame of the received frames in which a first contour associated with a first object is merged with a second contour associated with a second object. The client determines, at a first depth in the identified frame, a merged-contour region which is associated with the merged contours. The client detects a third contour at a second depth that is less than the first depth and determines a first region associated with the third contour. The client detects a fourth contour at the second depth and determines a second region associated with the fourth contour. If criteria are satisfied, the client associates the first region with a position of the first object and associates the second region with a position of the second object.

Described herein is a system and computer implemented method for finding similars for a selected clothing image amongst a set of clothing images in an electronic catalog in an online store serving online customers. An object detection model is applied to extract the clothing section within the clothing images to create a preprocessed image. A first machine learning model is applied on the preprocessed image(s) to convert the colors and textures of said preprocessed image into a first set of vector representations. A second machine learning model is applied on the preprocessed image(s) to convert the shapes of said preprocessed image into a second set of vector representations. Operations of mapping nearest vectors, matching attributes, sorting and ranking are performed, and thereafter similar images are displayed to the online customer.

A system and method for visually automated interface integration that includes collecting image data; detecting a device interface source in the image data; processing the image data associated with the device interface source into an extracted interface representation; and exposing at least one access interface to the extracted interface representation.

A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.

A method and apparatus for performing gesture recognition. In one embodiment of the invention, the method includes the steps of receiving one or more raw frames from one or more cameras, each of the one or more raw frames representing a time sequence of images, determining one or more regions of the one or more received raw frames that comprise highly textured regions, segmenting the one or more determined highly textured regions in accordance textured features thereof to determine one or more segments thereof, determining one or more regions of the one or more received raw frames that comprise other than highly textured regions, and segmenting the one or more determined other than highly textured regions in accordance with color thereof to determine one or more segments thereof. One or more of the segments are then tracked through the one or more raw frames representing he time sequence of images.

An angled optical pattern is decoded. To decode an optical pattern imaged at an angle, an area of interest of an image is received. A start line and an end line of the optical pattern are estimated. Corners of the optical pattern are localized. A homography is calculated based on the corners. And a scanline of the optical pattern is rectified based on the homography.

A system and method for reliably determining lanes of a roadway includes an optical sensing arrangement for sensing metallic striping from photoelectric effect. The location of the striping that defines a border of a traffic lane is determined and the location of the striping is displayed on a graphical user interface. The location can be used to provide lane control to ensure the vehicle maintains proper position in a traffic lane, lane warning assistance, collision avoidance, parking control, and guidance for autonomous driving.