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: 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.

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: Methods and apparatus are described for generating compelling preview clips of media presentations. Compelling clips are identified based on the extent to which human faces are shown and/or the loudness of the audio associated with the clips. One or more of these compelling clips are then provided to a client device for playback.

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.

Abstract: Exemplary embodiments are directed to a system for content-aware enhancement of an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image to detect one or more objects in the original image, generate an in-focus person mask of the original image for one or more in-focus people in the original image, and apply one or more enhancements to areas of the original image excluding the in-focus person mask.

Abstract: When one looks at a face, one cannot help but ‘read’ it: in the blink of an eye, people form reliable impressions of both transient psychological states (e.g., happiness) and stable character traits (e.g., trustworthiness). Such impressions are irresistible, formed with high levels of consensus, and important for social decisions. Disclosed herein is a large-scale data-driven methodology that allows for the easy manipulation of social trait information in hyper-realistic face images. For example, a given face image could be made to look more or less trustworthy by moving a simple slider. Further, this method can not only generate faces, but can ‘read’ faces as well, providing confidence estimates of different social traits for any arbitrary image. The disclosed approach is both fast and accurate, and represents a paradigm shift in facial photo manipulation.

Abstract: A method for detecting a face key point includes: acquiring image data containing a face; detecting a first face key point of the face in the image data; identifying a face type of the face according to the first face key point; performing position correction on the first face key point with reference to the face type; extracting a facial features image containing facial features from the image data based on the first face key point subjected to the position correction; and detecting a second face key point of the face in the facial features image.

Abstract: A beauty prediction method and device based on multitasking and weak supervision, and a storage medium are disclosed. The method includes the steps of pre-processing inputted facial images; allocating the pre-processed images to multiple tasks; extracting shared image features; and obtaining a plurality of classification results via a plurality of classification networks each including a residual network, a standard neural network and a classifier.

Abstract: A method for classifying images of oligolayer exfoliation attempts. In some embodiments, the method includes forming a micrograph of a surface, and classifying the micrograph into one of a plurality of categories. The categories may include a first category, consisting of micrographs including at least one oligolayer flake, and a second category, consisting of micrographs including no oligolayer flakes, the classifying comprising classifying the micrograph with a neural network.

Abstract: Disclosed are a facial beauty prediction method and device based on multi-task migration. The method includes: performing similarity measurement based on a graph structure on a plurality of tasks to obtain an optimal combination of the plurality of tasks; constructing a facial beauty prediction model including a feature sharing layer based on the optimal combination; migrating feature parameters of an existing large-scale facial image network to the feature sharing layer of the facial beauty prediction model; inputting facial images for training to pre-train the facial beauty prediction model; and inputting a facial image to be tested to the trained facial beauty prediction model to obtain facial recognition results.

Abstract: The various embodiments of the present disclosure are directed towards methods for tone mapping High-Dynamic-Range (HDR) image data, as well as controlling the brightness of the image encoded by HDR the image data and/or the tone-mapped image data. HDR image is captured. A tone mapping function for the HDR image data is generated. To generate the tone mapping function, control points are dynamically determined based on an analysis of the HDR image data. The tone mapping function is fit to the control points. The tone mapping function is a non-linear function, and is described by a curve in a plane. The shape of the curve is constrained by a line generated from a portion of the control points. The tone mapping function is applied to the HDR image data. A color-compression is applied to the tone mapped image data to generate Standard Dynamic Range or Low Dynamic Range image data.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.

Abstract: Provided is an image restoration apparatus and method. The image restoration apparatus may store an image restoration model including a convolutional layer corresponding to kernels having various dilation gaps, and may restore an output image from a target image obtained by rearranging a compound eye vision (CEV) image by the image restoration model.

Abstract: An image processing method and an image processing device is provided. The processing device comprises memory and a processor configured to receive a frame of color filtered image data comprising pixels which are spatially multiplexed according to a plurality of different light exposures, resample the color values as different frames of pixels for the plurality of different light exposures, fuse the resampled frames of pixels for the plurality of different light exposures into a frame of pixels according to a HDR format and color interpolate the fused frame of pixels. The processor is configured to interpolate, for each resampled frame, missing pixel color values based on the color values of adjacent resampled pixels in a same resampled frame. The color interpolated fused frame of pixels is processed in an image processing pipeline and converted to a YUV color space.

Abstract: A method for encoding images includes decoding a first encoded image to obtain a first decoded image, where the first decoded image includes a first decoded portion corresponding to a first encoded portion of the first encoded image and a second decoded portion corresponding to a second encoded portion of the first encoded image; decoding a second encoded image to obtain a second decoded image; combining the first decoded image and the second decoded image to obtain a single decoded image; and encoding the single decoded image to obtain a single encoded image that includes a third and a fourth encoded portions. Encoding the single decoded image includes obtaining the third encoded portion of the single encoded image by copying the first encoded portion of the first encoded image; and obtaining the fourth encoded portion of the single encoded image by encoding the second decoded portion using an encoder.