Abstract: Systems, devices and methods operative for identifying a reference within a figure and an identifier in a text associated with the figure, the reference referring to an element depicted in the figure, the reference corresponding to the identifier, the identifier identifying the element in the text, placing the identifier on the figure at a distance from the reference, the identifier visually associated with the reference upon the placing, the placing of the identifier on the figure is irrespective of the distance between the identifier and the reference.

Abstract: This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

Abstract: In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates detection of the determined motion.

Abstract: Disclosed is an intelligent micromanipulation system based on machine vision, including a base, wherein a microscope, a rotating table and two lifting tables are arranged on the base, the microscope is located right above the rotating table, the two lifting tables are arranged at two sides of the rotating table, respectively, and each is provided with a mechanical arm, each of the two manipulators is provided with an end effector, the mechanical arm is capable of driving the corresponding end effector to perform fine adjustment in x, y and z directions, one of the end effectors clamps a suction holding needle in communication with a suction pump, while the other end effector clamps an injection needle in communication with an injection pump; and the rotating table includes a tray, a rotating mechanism and a driving device, and the driving device drives the tray to rotate via the rotating mechanism.

Abstract: The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network.

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: Methods, systems, and articles of manufacture, including computer program products, are provided for image cleanup. In some embodiments, there is provide a method which may include subsampling a first image to a first level image of a multiscale transform; performing, based on a machine learning model, an identification of a foreground portion of the first level image and a background portion of the first level image; generating, based on the identification of the foreground portion and the background portion, a first mask; upscaling the first mask to a resolution corresponding to the first image depicting the foreground item; applying the upscaled first mask to the first image to form a second image depicting the foreground item; and providing the second image depicting the foreground item to a publication system. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: An image processing method includes obtaining a first image and a second image, for the first image and the second image, separately obtaining, through calculation, a gradient saliency feature map of the image, based on the gradient saliency feature maps of the first image and the second image, performing feature matching between the first image and the second image to obtain a dense disparity map of the first image, and based on the dense disparity map of the first image, performing disparity translation on a pixel in the second image corresponding to a pixel in the first image, and fusing the first image and the pixel in the second image.

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.

Abstract: Methods, systems, and computer readable storage media for using image processing to develop a library of facial expressions. The system can receive digital video of at least one speaker, then execute image processing on the video to identify landmarks within facial features of the speaker. The system can also identify vectors based on the landmarks, then assign each vector to an expression, resulting in a plurality of speaker expressions. The system then scores the expressions based on similarity to one another, and creates subsets based on the similarity scores.

Abstract: In one implementation, a method includes obtaining an image. The method includes generating a plurality of denoised images by denoising the image using a respective plurality of denoisers, wherein the plurality of denoisers includes a first denoiser associated with a first level of exposure and a second denoiser associated with a second level of exposure. The method includes generating a combined image by combining the plurality of denoised images.

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: Provided are a beauty processing method and apparatus. The method includes: obtaining an original face image, and extracting original parameter information corresponding to a site feature of a to-be-beautified site; determining, based on the original parameter information, a site beauty parameter corresponding to the to-be-beautified site; and processing the to-be-beautified site based on the site beauty parameter to generate a target face image. Thus, the personalized beauty parameters can be generated to meet the user's personalized beauty needs, improving the accuracy of the beauty parameter and enhancing the beauty effect.

Abstract: A face recognition method for an edge device is provided. Firstly, a recognition data is received from a computing device through a wireless network. Then, a trained model is retrieved from the recognition data. Then, the trained model is stored. Then, a face detection operation is performed to acquire a face image and a face temperature of a detected face. When the face temperature is within a human body temperature range and the face image complies with the trained model, the edge device confirms that the detected face is a face of a real person and the real person is a known user.

Abstract: Chrominance data channels are reconstructed from a color-mosaic image data input and de-noised with guidance from a luminance data channel extracted from that same mosaic data input to produce de-noised chrominance channels. The de-noised chrominance channels are combined with a de-noised version of the luminance channel to yield a primary-color-channel output image.

Abstract: A facial feature matching system comprises a facial feature matching engine. A first user selection of reference facial images is received, and facial features of reference faces are characterized using the facial feature recognition engine comprising a neural network with input, hidden, and output layers. The facial features are weighted. The weighted facial features are used to identify users that have facial features similar to the weighted facial features, wherein the respective reference faces include faces different than the faces of the users. Similarity indicators are generated for the identified users. The generated respective similarity indicators are used to generate a ordering of the identified users which is rendered via the user device. A first user selection of a second user in the ordered identified users is received and the first user and the second user are enabled to communicate over an electronic communication channel.

Abstract: An image processing method including the following steps is provided. An image information of an image is received, wherein the image includes a plurality of blocks and the image information includes a plurality of pixel information of each block. A dual gamma correction is performed on a first group of blocks of the image to obtain one or more corrected blocks and the dual gamma correction is skipped on a second group of blocks of the image to obtain a plurality of uncorrected blocks. A first encoding process is performed on the one or more corrected blocks to obtain a plurality of first encoded blocks. A second encoding process different from the first encoding process is performed on the plurality of uncorrected blocks to obtain a plurality of second encoded blocks.

Abstract: MMTTTTA method and system for face alignment. The method may include obtaining an image processing model set including M (M?2) candidate models, and obtaining a test image including a target face. The method may also include conducting T (T?1) stages of model set updating operation. Each stage of the T stages of model set updating operation may include conducting a performance evaluation to each candidate model of the image processing model set with respect to the test image, and updating the image processing model set by excluding at least one model from the image processing model set based on the performance evaluation. The method may further include designating, after completing the T stages of model set updating operation, at least one candidate model of the image processing model set as a target model, and determining, based on the target model, a result shape as a shape of the target face.

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 of pixel values in the image and perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam. The instructions include further instructions to, when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Abstract: A face image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining face key points and a face deflection angle in a face image; determining a submalar triangle center in the face image according to the face key points and the face deflection angle; determining a submalar triangle region in the face image according to the face key points and the submalar triangle center; and performing color filling on the submalar triangle region. By means of the present disclosure, a submalar triangle region can be accurately positioned, and submalar triangle filling is performed based on the accurately positioned submalar triangle region, thereby obtaining a more natural filling effect.