Abstract: Provided are methods and apparatuses for positioning face feature points. The method includes: carrying out edge detection on a face image to obtain a face feature line image; and fusing the face image and the face feature line image to obtain position information of face feature points.

Abstract: An image processing apparatus according to the present invention includes at least one memory and at least one processor Which function as: a setting unit configured to he capable of setting any of a plurality of processing modes which include a first processing mode to display an image having a first brightness range and a second processing mode to display an image having a second brightness range which is narrower than the first brightness range and a processing unit configured to generate frame image data, which is a display target, in a first state in which image processing in accordance with a currently set processing mode is performed, and generates a capture image data corresponding to the frame image data in a second state after changing the first state in accordance with the currently set processing mode.

Abstract: A 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: Image processing methods and apparatus are described. The image processing method comprises receiving input of a visible-ray image and an infrared-ray image obtained by photographing a same subject, estimating, based on the visible-ray image, the infrared-ray image and motion information, a blur estimate associated with the visible-ray image, and generating, based on the estimated blur estimate, a corrected visible-ray image.

Abstract: An automated method and apparatus are provided for identifying when a first video is a content-identical variant of a second video. The first and second video each include a plurality of image frames, and the image frames of either the first video or the second video include at least one black border. A plurality of variants are generated of selected image frames of the first video and the second video. The variants are then compared to each other, and the first video is identified as being a variant of the second video when at least one match is detected among the variants.

Abstract: An image processing method for a computer device. The method includes obtaining a to-be-processed image belonging to a first image category; inputting the to-be-processed image into a first stage image conversion model, to obtain a first intermediate image; and converting the first intermediate image into a second intermediate image through a second stage image conversion model. The method also includes determining a first weight matrix corresponding to the first intermediate image; determining a second weight matrix corresponding to the second intermediate image; and fusing the first intermediate image and the second intermediate image according to the corresponding first weight matrix and second weight matrix, to obtain a target image corresponding to the to-be-processed image and belonging to a second image category. A sum of the first weight matrix and the second weight matrix being a preset matrix.

Abstract: Systems, methods, and computer-readable media are provided for foreground image segmentation. In some examples, a method can include obtaining a first image of a target and a second image of the target, the first image having a first field-of-view (FOV) and the second image having a second FOV; determining, based on the first image, a first segmentation map that identifies a first estimated foreground region in the first image; determining, based on the second image, a second segmentation map that identifies a second estimated foreground region in the second image; generating a third segmentation map based on the first segmentation map and the second segmentation map; and generating, using the second segmentation map and the third segmentation map, a refined segmentation mask that identifies the target as a foreground region of the first and/or second image.

Abstract: An image processing apparatus comprises: at least one processor or circuit configured to perform the operations of following units: an obtaining unit configured to obtain a plurality of image signals being composed of image signals shot wider different shooting conditions; a determination unit configured to determine a composition rate in order to composite an image signal that has the predetermined shooting condition with a noise-reduced image that has the predetermined shooting condition, in accordance with an inter-frame change amount and a parameter indicating the predetermined shooting condition; a noise reduction unit configured to composite the image signal with the noise-reduced image using the composition rate to generate a new noise-reduced image; and a composition unit configured to composite the new noise-reduced image and an image signal that has another shooting condition.

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 apparatus for ascertaining, from at least one image, a delimited region and/or a motion path of at least one object includes at least one artificial neural network (ANN) made up of several successive layers. The first layer of the ANN receives as input the at least one image or a part thereof. The second layer supplies as output a boundary line of the delimited region, a linear course of the motion path, or a portion of the boundary line or motion path. The dimensionality of the second layer is lower than the dimensionality of the first layer.

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: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios.

Abstract: The present invention provides an age recognition method, a computer program and an electronic device. The method comprises: acquiring a face image to be recognized; extracting face characteristic points in the face image to be recognized, and characteristic point coordinates of the face characteristic points in the face image to be recognized; extracting face global features from the face image to be recognized according to the characteristic point coordinates; extracting face local features from the face image to be recognized according to the face characteristic points; and determining an age recognition result corresponding to the face image to be recognized according to the face global features, the face local features and an age recognition model obtained by pre-training.

Abstract: In some embodiments, a computing device uses a blue noise sampling operation to identify source pixels from an input image defining respective pixel sets. Each pixel set is associated with a respective weight matrix for a down-scaling operation. The blue noise sampling operation causes an overlap region between first and second pixel sets. The computing device assigns an overlap pixel in the overlap region to the first weight matrix based on the overlap pixel being closer to the first source pixel. The computing device modifies the second weight matrix to exclude the overlap pixel from a portion of the down-scaling operation involving the second weight matrix. The computing device performs the down-scaling operation on the input image by combining the first pixel set into a first target pixel with the first weight matrix and combining the second pixel set into a second target with the modified second weight matrix.

Abstract: In some examples, an apparatus for automated removal of noise in a frequency domain receives an image captured by an image sensor, converts at least a portion of the image into a frequency domain image, identifies a position in the frequency domain image, the position indicating a boundary between target content in the frequency domain image and noise in the frequency domain image, and removes content in the frequency domain image outside the boundary, to produce a noise-attenuated image.

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: A method and an apparatus for generating a face rotation image are provided. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, wherein presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, wherein the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.

Abstract: This disclosure relates to a method for rendering images. First, a user request is received from a user interface to access an image effect renderer recipe, comprising conditional logic and non-visual image data, from an effect repository. Next, at least one image signal is received. Objects are identified within the image signal(s). The image effect renderer recipe is processed via an effect renderer recipe interpreter to generate image processing steps and image processing prioritizations. The image processing steps are then ordered in accordance with the image processing prioritizations. Next, an image processor applies the image processing steps to the identified objects of the image signal(s) to generate at least one processed image signal. The processed image signal(s) are then displayed on a display device.

Abstract: The present disclosure provides a technical solution related to processing images in image transformation by fusion. More particularly, an image of an object of an original picture may be transferred onto an image of a corresponding object in a template picture by fusion. During the transformation by fusion, the processing of fusion may be done on each pixel by using a pixel weight graph, so that the image of the object in the original picture may be better integrated into the template picture with the features of the object in the original picture maintained well in the picture processed by fusion.

Abstract: An image processing apparatus processes image data generated by a reading unit configured to read a document, using at least one of a plurality of types of reading elements that have different spectral sensitivity characteristics and are cyclically arrayed in a first direction, by scanning the document in a second direction vertical to the first direction. The image processing apparatus includes a processing unit configured to perform, on the image data, filter processing for averaging pixel values of pixels at least equal in number to the types of the reading elements in the first direction, wherein the processing unit is implemented by at least one processor or at least one circuit.