Abstract: A display system includes a display device including a plurality of displays to output first content received from a first server, a second server to receive the first content from the first server and to store a plurality of error patterns which is able to occur in the first content, and a photographing device to transmit second content obtained by photographing the display device to the second server. The second server displays whether a defect is present on the display device, by comparing the second content with the plurality of error patterns, when the second content is received from the photographing device.

Abstract: The present invention addresses the problem of providing a technique for determining the authenticity of a product without requiring a special device such as an integrated circuit (IC) tag. A means for solving this problem according to the invention is characterized by determining the authenticity of a target product on the basis of the validity of the association between the body of the product and a surface-treated component that is mounted to the body and that has been validated.

Abstract: A method, a device and a computer program product for image processing are proposed. In the method, a first training image and region information are obtained. The region information indicates a region of a defect in the first training image. A second training image with the defect at least partially removed is generated using an image generator based on the first training image and the region information. The image generator is trained to recover the first training image by replacing pixels included in the region indicated by the region information. The image generator is updated based on the second training image. In this way, the image including the defect can be accurately and efficiently recovered.

Abstract: Methods, apparatus, systems and articles of manufacture of logo recognition in images and videos are disclosed. An example method to detect a specific brand in images and video streams comprises accepting luminance images at a scale in an x direction Sx and a different scale in a y direction Sy in a neural network, and training the neural network with a set of training images for detected features associated with a specific brand.

Abstract: Images are accessed representing a status in a fabrication of a semiconductor chip corresponding to a particular stage in the fabrication. Distortion is removed from the images and actual features of the semiconductor chip are extracted from the images. Synthesized ideal features of the semiconductor chip associated with completion of the particular stage in the fabrication are determined from the one or more images. The actual features are compared to the ideal features to determine whether anomalies associated with the particular stage exist in the semiconductor chip.

Abstract: A captured image of a pattern and a reference image of the pattern may be received. A contour of interest of the pattern may be identified. One or more measurements of a dimension of the pattern may be determined for each of the reference image and the captured image with respect to the contour of interest of the pattern. A defect associated with the contour of interest may be classified based on the determined one or more measurements of the dimension of the pattern for each of the reference image and the captured image.

Abstract: A method for training a model for generating simulation data for training an autonomous driving agent, comprising: analyzing real data, collected from a driving environment, to identify a plurality of environment classes, a plurality of moving agent classes, and a plurality of movement pattern classes; generating a training environment, according to one environment class; and in at least one training iteration: generating, by a simulation generation model, a simulated driving environment according to the training environment and according to a plurality of generated training agents, each associated with one of the plurality of agent classes and one of the plurality of movement pattern classes; collecting simulated driving data from the simulated environment; and modifying at least one model parameter of the simulation generation model to minimize a difference between a simulation statistical fingerprint, computed using the simulated driving data, and a real statistical fingerprint, computed using the real dat

Abstract: A method for controlling an electronic device is provided. The method includes obtaining an image, detecting at least one object in a facial area from the image, and applying a virtual light source effect to the image based on an attribute of the detected object and displaying the image.

Abstract: An image detection method for selecting a representative image of a user is provided. In the image detection method, a plurality of images of the user are obtained, and a plurality of feature parameters of the plurality of images are obtained. A face occlusion analysis is performed on the plurality of images according to the plurality of feature parameters to determine whether the plurality of images clearly show the user's face. A plurality of feature vectors are determined, and a body distribution analysis is performed on the plurality of images according to the plurality of feature vectors to determine a body position and a position type of the user. An image related to the position type is selected according to results of the face occlusion analysis and the body distribution analysis.

Abstract: Training and upscaling a large-sized input image, including: dividing the large-sized input image into a plurality of small-sized sub-pictures; expanding each sub-picture of the plurality of small-sized sub-pictures using target padding pixels to produce an expanded sub-picture; upscaling each sub-picture using an ML-based upscaler to produce an expanded upscaled sub-picture; cropping the expanded upscaled sub-picture to an upscaled size equal to an original size of each sub-picture multiplied by an upscaling factor; repeating expanding, upscaling, and cropping for the plurality of sub-pictures; and concatenating the plurality of cropped sub-pictures to produce an output image.

Abstract: Image processing methods and systems apply filtering operations to images, wherein the filtering operations use filter costs which are based on image gradients in the images. In this way, image data is filtered for image regions in dependence upon the image gradients for the image regions. This may be useful for different scenarios such as when combining images to form a High Dynamic Range (HDR) image. The filtering operations may be used as part of a connectivity unit which determines connected image regions, and/or the filtering operations may be used as part of a blending unit which blends two or more images together to form a blended image.

Abstract: A computer-implemented method and systems are provided for performing machine learning for an image classification task. The method includes overlaying, by a processor, a second image on a first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image. The method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.

Abstract: Systems and techniques are provided for tracking subjects in an area of real space. The system receives a plurality of sequences of images of corresponding fields of view in the real space. The system uses the sequences of images to locate subjects in the area of real space during identification intervals. The system compares a current set of located subjects from a current identification interval with previous sets of located subjects from a plurality of previous identification intervals to track locations of tracked subjects in the area.

Abstract: The present disclosure relates to a method and a device for training an image recognition model and a related device. The method includes: extracting sub-image feature data from a detection frame sub-image of an input image; determining element feature data matching the sub-image feature data from an index element database; and outputting images related to the element feature data as training images for training the image recognition model. The index element database is built in advance based on a plurality of element feature data extracted from a plurality of candidate images.

Abstract: Implementations of the present specification disclose a comparison method and apparatus based on a plurality of face image frames and an electronic device. The method includes: obtaining a plurality of face image frames of a target object; selecting a first face image with a highest quality score from the plurality of face image frames, and adding the first face image to a candidate image set; in response to that a number of images in the candidate image set is less than a predetermined value, iteratively selecting, from the plurality of face image frames, a second face image that has a maximum average difference from all face images in the candidate image set in a predetermined parameter, to add the second face image to the candidate image set; and comparing the face images in the candidate image set with a stored face image of the target object.

Abstract: One embodiment can provide a system and method for determining joinder points on to-be-joined fabric pieces. During operation, the system captures an image comprising the to-be-joined fabric pieces; identifies to-be-joined edges, respectively, on the fabric pieces; for each edge, computes a curve that substantially fits the edge; and determines the joinder points on the edge based on the computed curve. Variance of distance between two consecutive joinder points on the edge is within a predetermined value.

Abstract: An image processing device (100) includes an object detecting unit (103a) that detects objects each appearing in a corresponding one of pieces of image data; an object tracking unit (103b) that tracks the detected objects in a time direction; a descriptor generating unit (104) that generates feature descriptors of the respective objects on the basis of results of the detection and results of the tracking of the objects; a data recording control unit (105) that forms a database from the generated feature descriptors and the pieces of image data of the objects corresponding to the feature descriptors; an image retrieving unit (108a) that retrieves, from the database, object groups that match a retrieval condition and form a pair of object groups appearing in pieces of image data obtained by capturing different regions, each of the object groups corresponding to a corresponding one of the regions; a feature extracting unit (108b) that extracts a feature to be used for checking, from pieces of image data and feat

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: A face recognition method includes capturing a background of an image; after determining that a face exists in the image, determining a face region of interest (ROI) of the face; capturing a foreground of the image with the face; and comparing the face ROI and the foreground of the image to determine whether the face is authentic or not.

Abstract: Systems and methods for assessing a physical structure are provided. Information indicative of an infrared image that includes a portion of the physical structure is received, and one or more indicators within the infrared image exceeding a heat threshold are determined. A plurality of characteristics of the one or more indicators are determined. One or more edges of the portion of the physical structure are detected, and an area associated with an intended use of one or more fasteners associated with the physical structure is determined according to the detected edges. An amount of the one or more indicators corresponding to the area is determined, and the one or more indicators are classified as fasteners according to the determined amount. Based on the classification of the one or more indicators, a condition of the physical structure is determined.