Abstract: Estimating a three-dimensional (3D) pose of an object, such as a hand or body (human, animal, robot, etc.), from a 2D image is necessary for human-computer interaction. A hand pose can be represented by a set of points in 3D space, called keypoints. Two coordinates (x,y) represent spatial displacement and a third coordinate represents a depth of every point with respect to the camera. A monocular camera is used to capture an image of the 3D pose, but does not capture depth information. A neural network architecture is configured to generate a depth value for each keypoint in the captured image, even when portions of the pose are occluded, or the orientation of the object is ambiguous. Generation of the depth values enables estimation of the 3D pose of the object.

Abstract: A method for processing images is provided. The method includes: acquiring a first image by smoothing a skin region of a target object in an original image; determining a skin texture material matching with a face area of the target object; acquiring a facial texture image of the target object by rendering the skin texture material; and acquiring a second image by fusing the facial texture image with the first image.

Abstract: In some embodiments, a method receives a first textual description of content and converts the first textual description of content to a first image representation. The method compares a similarity between the first image representation and a second image representation for candidate metadata. The candidate metadata is associated with a second textual description of content. The method determines whether the first textual description of content is associated with the second textual description of content based on the comparison of similarity of the first image representation and the second image representation.

Abstract: Methods, apparatus, and systems are provided for obfuscating facial identity in images by synthesizing a new facial image for an input image. A base face is detected from or selected for an input image. Facial images that are similar to the base face are selected and combined to create a new facial image. The new facial image is added to the input image such that the input image includes a combination of the base face and the new facial image. Where no base face is detected in the input image, a base face is selected from reference facial images based at least on pose keypoints identified in the input image. After a new facial image is generated based on the selected base face, a combination of the new facial image and the base facial image are added to the input image by aligning one or more pose keypoints.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for utilizing tools and techniques for identifying process rules for automated execution of instances of a process workflow. One example method includes extracting rules from a machine learning model for prediction of execution results of process workflow instances. Metrics defining coverage and accuracy of the rules are calculated. The rules are evaluated according to the metrics and are reduced to a first set of rules that are provided for further evaluation. A rule from the first set of rules is determined to be incorporated into process rules defined for the process workflow at a process execution engine. The process rules associated with execution of the process workflow are updated to include the first rule and to generate a process result automatically according to the first rule when the instance complies with prerequisites defined at the first rule.

Abstract: The present disclosure provides a method for extracting a face detection image, wherein the method includes: obtaining a plurality of image frames by an image detector, performing a face detection process on each image frame to extract a face area, performing a clarity analysis on the face area of each image frame to obtain a clarity degree of a face, conducting a posture analysis on the face area of each image frame to obtain a face posture angle, generating a comprehensive evaluation index for each image frame in accordance with the clarity degree of the face and the face posture angle of each image frame, and selecting a key frame from the image frames based on the comprehensive evaluation index. Such that the resource occupancy rate during image data processing may be reduced, and the quality of the face detection process may be improved.

Abstract: The present invention provides an artificial intelligence includes a memory configured to store a plurality of pollution logs; a learning processor configured to classify the plurality of pollution logs into at least one pollution log group based on a similarity between pollution information; a map generator configured to generate an indoor area map to which location of each of at least one pollution log group is mapped; and a processor which determines a cleaning method for each of the at least one pollution log group and performs cleaning for each of the at least one pollution log group according to the determined cleaning method.

Abstract: An image processing method converts an original image represented in an original color gamut to a target image represented in a target color gamut. The image processing method comprises: A) calculating a set of primary color direction deviations between a set of original primary color directions of the original color gamut and a set of target primary color directions of the target color gamut, wherein each of the set of primary color direction deviations corresponds to a primary color; B) determining, for each pixel in the original image, a corrected color coordinate of the pixel based on a set of offsets of an original color coordinate of the pixel in the original color gamut relative to the set of original primary color directions and the set of primary color direction deviations; and C) mapping, for each pixel in the original image, the corrected color coordinate of the pixel into the target color gamut to generate a target color coordinate of the pixel in the target color gamut.

Abstract: An approach for extracting non-textual data from an electronic document is disclosed. The approach includes receiving a request to extract a file and converting the file into pixels. The approach creates a pixel map of the converted file and determines one or more density clusters of the pixel map based on image clustering method. Furthermore, the approach determines one or more coordinates of the one or more density clusters and determines one or more candidate information regions based on the one or more coordinates, density of the one or more density clusters. Finally, the approach extracts one or more textual data based on the one or more candidate information regions and outputs the extracted one or more textual data.

Abstract: A method for transferring a container configured to hold at least one article used in semiconductor fabrication is provided. The method includes moving a transferring mechanism to a first position that is adjacent to the original space; producing an image of an edge of the container that is adjacent to the original space using an optical receiver before the container is moved to a destination space; and performing an image analysis of the image to determine whether to move the container to the destination space.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, determine a first measure based on a first number of pixels corresponding to the first object in the first image and determine a relative distance measure by comparing the first measure based on the first number of pixels corresponding to the first object to a second measure based on a second number of pixels corresponding to a second object in the first image.

Abstract: A method includes obtaining multiple images of a scene including a first image associated with a first exposure value and a second image associated with a second exposure value. Each image includes image data in each of multiple channels of a color space, and the first exposure value is greater than the second exposure value. The method also includes decomposing each channel of each image into a base layer and a detail layer. The method further includes generating for each image, a blending weight map for the base layer and a blending weight map for the detail layer. The method includes combining each base layer and each detail layer of each channel of each image based on the blending weight map for the base layer for the image and the blending weight map for the detail layer of the image to obtain an HDR image of the scene.

Abstract: A method for determining an absolute depth map to monitor the location and pose of a head (100) being imaged by a camera comprises: acquiring (20) an image from the camera (110) including a head with a facial region; determining (23) at least one distance from the camera (110) to a facial feature of the facial region using a distance measuring sub-system (120); determining (24) a relative depth map of facial features within the facial region; and combining (25) the relative depth map with the at least one distance to form an absolute depth map for the facial region.

Abstract: A method and a device of correcting image distortion, a display device, a computer readable medium and an electronic device. The method of correcting image distortion includes: forming a correction grid on an imaging screen of a display device, the correction grid including a plurality of to-be-adjusted grid points; forming a circle on the correction grid; and moving the to-be-adjusted grid point on and outside the circle, to correct image distortion.

Abstract: A system or method may be used to perform image processing. An example method may include extracting a plurality of sub-images corresponding to a plurality of regions of an image. A rating classification may be generated, using a machine learning trained model, for each of the plurality of sub-images, for example including a no-rating classification for at least one sub-image of the plurality of sub-images. The method may include creating, using the rating classification for each of the plurality of sub-images, a color map overlay of a grayscale image corresponding to the image. The color map overlay may include colors corresponding to respective rating classifications for the plurality of regions or a region without a color corresponding to the at least one sub-image having the no-rating classification. The method may include outputting the color map overlaid on the grayscale image.

Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: A method for face modeling and related products are provided. The method includes the following. Multiple first images of a face of an object at multiple different angles are obtained when the face rotates, where each first image is obtained at a different angle. Face detection is performed on any first image of the multiple first images, and the any first image is divided into a first face region and a first background region. Multiple depth images corresponding to the multiple first images are obtained and each of the multiple depth images is segmented according to the first face region and the first background region to obtain multiple face region images. Three-dimensional (3D) face modeling is performed according to the multiple face region images to obtain a 3D face image.

Abstract: The embodiments disclose a method and device for liveness detection and a computer-readable storage medium. The method includes that: a first image collected by a first image sensor of a binocular camera is acquired, and a second image collected by a second image sensor of the binocular camera is acquired: binocular matching processing is performed on the first image and the second image to obtain parallax information of the first image and the second image; and a Iiveness detection result is obtained according to the parallax information. By adopting the embodiments of the present disclosure, the accuracy of liveness detection can be improved.

Abstract: The present disclosure relates to a process and a system for assessing the performance of cleaning operations by utilizing an automatic read out routine for test strips subjected to a cleaning operation. Furthermore, the present disclosure relates to a computer program product capable of performing the process.

Abstract: Apparatus for binning an input value into one of a plurality of bins which collectively represent a histogram of input values, each of the plurality of bins representing a corresponding range of input values, the apparatus comprising: an input for receiving an input value; a noise source configured to generate an error value according to a predetermined noise distribution; and a binning controller configured to mix the received input value with the error value so as to generate a modified input value and to allocate the modified input value to the bin corresponding to that modified input value.