Abstract: A computing device installed in or mountable to a shopping cart, includes a camera configured to capture an image, and a processor configured to acquire commodity data of a registered commodity, when an object is placed in the shopping cart, perform object recognition on an image of the object captured by the camera, and determine whether the recognized object is a commodity corresponding to the acquired commodity data, and upon determining that the recognized object is not the commodity corresponding to the acquired commodity data, issue a request for displaying a message instructing registration of the recognized object as a registered commodity.

Abstract: A face verification method and apparatus is disclosed. The face verification method includes selecting a current verification mode, from among plural verification modes, to be implemented for the verifying of the face, determining one or more recognizers, from among plural recognizers, based on the selected current verification mode, extracting feature information from information of the face using at least one of the determined one or more recognizers, and indicating whether a verification is successful based on the extracted feature information.

Abstract: A label assigning device of an embodiment includes one or more hardware processors configured to function as a label candidate generation unit, a feature amount pair detection unit, and a label assigning unit. The label candidate generation unit generates a label candidate from association data associated with a content. The feature amount pair detection unit detects a feature amount pair that is a combination of feature amounts having a highest similarity among combinations of a feature amount extracted from a first content and a feature amount extracted from a second content. The label assigning unit assigns, as a label, a common label candidate generated from both first association data associated with the first content and second association data associated with the second content to each feature amount constituting the feature amount pair.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for predicting a passenger flow. A specific implementation of the method includes: acquiring, based on a received prediction request for a passenger flow value of a target traffic route in a preset time period, route information of the target traffic route and a passenger flow sequence corresponding to a preset number of consecutive time periods before a current time; encoding the route information to generate encoded route information; and inputting the encoded route information and the passenger flow sequence into a pre-trained passenger flow prediction model to obtain a passenger flow prediction value of the target traffic route in the preset time period, the passenger flow prediction model being used to represent a corresponding relationship between both the encoded route information and the passenger flow sequence and the passenger flow prediction value of the target traffic route.

Abstract: Systems, methods and products for classifying images according to a visual concept where, in one embodiment, a system includes an object detector and a visual concept classifier, the object detector being configured to detect objects depicted in an image and generate a corresponding object data set identifying the objects and containing information associated with each of the objects, the visual concept classifier being configured to examine the object data set generated by the object detector, detect combinations of the information in the object data set that are high-precision indicators of the designated visual concept being contained in the image, generate a classification for the object data set with respect to the designated visual concept, and associate the classification with the image, wherein the classification identifies the image as either containing the designated visual concept or not containing the designated visual concept.

Abstract: Provided is an electronic equipment offering a plurality of controllable functionalities and that receives information designating a functionality selected amongst this plurality of controllable functionalities. The electronic equipment also receives a plurality of recordings of a same group of at least one user input for this selected functionality. The electronic equipment also applies a trained neural network model on this received plurality of recordings to recognize an input pattern. The electronic equipment also associates this recognized input pattern with this selected functionality, and stores this association in a memory. The electronic equipment also controls this selected functionality based on this stored association.

Abstract: A coding and modulation apparatus and method are presented. The apparatus (10) comprises an encoder (11) that encodes input data into cell words, and a modulator (12) that modulates said cell words into constellation values of a non-uniform constellation. The modulator (12) is configured to use, based on the total number M of constellation points of the constellation, the signal-to-noise ratio SNR in dB and the channel characteristics, a non-uniform constellation from a group of constellations comprising one or more of predetermined constellations defined by the constellation position vector u1 . . . v, wherein v=sqrt(M)/2?1.

Abstract: An ear detection method with deep learning pairwise model based on contextual information belongs to the field of biometric recognition technologies, and addresses a problem that an ear location cannot be found in a large scene, especially in a background image containing a whole body. The method includes: performing preprocessing and object labeling on images; modifying an Oquab network to be a local model for four classes through transfer learning and training the local model; training two pairwise models of head and ear as well as body and head based on the local model; and performing joint detection for an ear through the local model, the two pairwise models and body features. The method uses a hierarchical relationship from large to small to establish contextual information, which can reduce the interference of other features and detect the location of the ear more accurately.

Abstract: This patent includes a method for displaying a 2D image on a non-planar display with a top portion of said non-planar display curving inwards towards a user's viewing position, a bottom portion of said non-planar display curves inwards towards said user's viewing position, a left portion of said non-planar display curves inward towards said user's viewing position and a right portion of said non-planar display curves inward towards said user's viewing position. This type of display can be used as a virtual display for extended reality head display units including virtual reality, augmented reality or mixed reality displays. For a virtual display, advanced features can be performed including stereoscopic viewing the screen with a convergence point shifting technique, zooming and rotating to maximize usability. The virtual display can be modified in its horizontal curvature or vertical curvature per user preference.

Abstract: A method and apparatus for joint image and per-pixel annotation synthesis with a generative adversarial network (GAN) are provided. The method includes: by inputting data to a generative adversarial network (GAN), obtaining a first image from the GAN; inputting, to a decoder, a first feature value that is obtained from at least one intermediate layer of the GAN according to the inputting of the data to the GAN; and obtaining a first semantic segmentation mask from the decoder according to the inputting of the first feature value to the decoder.

Abstract: A wearable apparatus and methods may analyze images. In one implementation, a wearable apparatus for capturing and processing images may comprise a wearable image sensor configured to capture a plurality of images from an environment of a user of the wearable apparatus and at least one programming device. The at least one processing device may be programmed to: perform a first analysis of the plurality of images to detect at least two persons; perform a second analysis of the plurality of images to determine association information related to the at least two detected persons; and update a social representation based on the determined association information.

Abstract: The present disclosure provides a fine-grained image recognition method, an electronic device and a computer readable storage medium. The method comprises the steps of feature extraction, calculation of feature discriminant loss function, calculation of feature diversity loss function and calculation of model optimization loss function. The present disclosure comprehensively considers influences of factors such as a large intra-class difference, a small inter-class difference, and a great influence of background noise of the fine-grained image, and makes constrains such that the feature maps belonging to each class are discriminative and have the features of corresponding class, thus reducing the intra-class difference, decreasing the learning difficulty and learning better discriminative features.

Abstract: The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.

Abstract: The present disclosure describes a method, apparatus, and storage medium for annotating image. The method includes extracting, by a device, a visual feature of an image through a generative adversarial network model, and sequentially inputting M pieces of random noise into the generative adversarial network model. In response to each of the M pieces of random noise being inputted into the generative adversarial network model, the method includes performing a determinantal point process (DPP) on the visual feature of the image and the each random noise through the generative adversarial network model to obtain N tag subsets, and selecting a distinct tag subset from the N tag subsets through the generative adversarial network model. The method also includes outputting M distinct tag subsets through the generative adversarial network model after the M pieces of random noise are inputted into the generative adversarial network model.

Abstract: Disclosed is an electronic apparatus comprising, a memory configured to store instructions; and at least one processor connected to the memory, and configured to detect at least one object of a first-class object or a second-class object included in a target image by the electronic apparatus using an artificial intelligent algorithm to apply the target image to a learned neural network model, and identify and apply an image-quality processing method to be individually applied to at least one detected object, the neural network model is set to detect an object included in an image, as trained based on learning data such as an image, a class to which the image belongs, information about the first-class object included in the image, and information about the second-class object included in the image.

Abstract: Various implementations disclosed herein include devices, systems, and methods for modeling a geographical space for a computer-generated reality (CGR) experience. In some implementations, a method is performed by a device including a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, the method includes obtaining a set of images. In some implementations, the method includes providing the set of images to an image classifier that determines whether the set of images correspond to a geographical space. In some implementations, the method includes establishing correspondences between at least a subset of the set of images in response to the image classifier determining that the subset of images correspond to the geographical space. In some implementations, the method includes synthesizing a model of the geographical space based on the correspondences between the subset of images.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; splitting the first feature map into a plurality of first sub-feature maps according to dimension information of the first feature map and a preset splitting rule, wherein the dimension information of the first feature map comprises dimensions of the first feature map and size of each dimension; performing normalization on the plurality of first sub-feature maps respectively to obtain a plurality of second sub-feature maps; and splicing the plurality of second sub-feature maps to obtain a second feature map of the image to be processed. Embodiments of the present disclosure can reduce the statistical errors during normalization of a complete feature map.

Abstract: The subject disclosure relates to techniques for selecting points of an image for processing with LiDAR data. A process of the disclosed technology can include steps for receiving an image comprising a first image object and a second image object, processing the image to place a bounding box around the first image object and the second image object, and processing an image area within the bounding box to identify a first image mask corresponding with a first pixel region of the first image object and a second image mask corresponding with a second pixel region of the second image object. Systems and machine-readable media are also provided.

Abstract: A method, apparatus and computer program for generating a sub-volume within a 3D dataset in a consistent, repeatable fashion. To accomplish this, geometric object(s) (e.g., 2D planes) are placed at precise anatomic landmarks with precise sizes and orientations. This serves to divide the 3D object into multiple parts (e.g., a first portion of the 3D volume has a first set of voxels and a second portion of the 3D volume has a second set of voxels). This process continues as multiple additional geometric objects are placed so that certain features of the 3D dataset can be extracted (i.e., shown with the best viewing settings). This process when used in conjunction with a radiologist's checklist enables efficient volume-by-volume viewing.

Abstract: The present disclosure relates to systems and methods for evaluating a medical image. The systems and methods may obtain the medical image. The systems and methods may extract a feature of the medical image. The feature may include a histogram of oriented gradients (HOG) feature of the medical image. The systems and methods may determine a degree to which an artifact in the medical image affects recognition of a tissue feature by inputting the feature of the medical image to a determination model.