Abstract: Disclosed are devices and systems for treating dry eye syndrome by recording and analyzing video of the anterior eye surface using a binocular illuminator device operating with a computer running a software application. The illuminator includes a projector for projecting a simplified Placido ring pattern onto each eye, and an eye cup for aligning the illuminator with the eye(s). Some embodiments of the illuminator include an onboard camera while other embodiments use the camera system included with a mobile computer such as a smartphone. Also disclosed are methods to use an illuminator and a software application running on a computer to record videos of the anterior eye surface, to perform digital image processing on the videos, and to use the processed images to determine one or more dry eye-related parameters.

Abstract: Embodiments of this application disclose encoding and decoding methods, apparatuses, and devices, a storage medium, and a computer program, which relate to the field of encoding and decoding technologies. In embodiments of this application, in a decoding process, a plurality of feature points are divided into a plurality of groups based on a specified numerical value, and probability distributions of feature points in a same group are determined in parallel to improve decoding efficiency. Correspondingly, in an encoding process, the plurality of feature points are also grouped in a same grouping manner, and first image features of each group of feature points in the plurality of groups are sequentially encoded into a bit stream. To be concise, this solution can break through an efficiency bottleneck caused by serial computing when decoding is performed based on a variational auto-encoder (VAE), thereby effectively improving decoding efficiency.

Abstract: Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Abstract: A method and apparatus for determining a human-face size are provided. The method includes: collecting a first to-be-measured human-face image of a to-be-measured target, wherein the first to-be-measured human-face image includes a first to-be-measured human face of the to-be-measured target and a circular reference object; acquiring a first pixel value of the first to-be-measured human face, and a second pixel value of the circular reference object; in response to a circularity of the circular reference object being greater than a preset circularity threshold, acquiring an angle of inclination of the circular reference object; and according to the first pixel value, the second pixel value, the angle of inclination and an actual size of the circular reference object, determining a first size of the first to-be-measured human face.

Abstract: A semiconductor image processing apparatus including a processing circuitry, the processing circuitry configured to identify a label corresponding to a feature amount included in an input image by using an identifier, learn a model for inferring the feature amount included in the input image and learns the identifier, and perform additional learning of the model based on the input image and the learned identifier.

Abstract: Various examples include systems, apparatuses, and methods to perform an automated visual-inspection of components undergoing various stages of fabrication. In one example, an inspection system includes a number of robots, each having a camera, to inspect a component for defects at various stages of fabrication. Generally, each of the cameras is located at a different geographical location corresponding to the various stages in the fabrication of the component. At least some of the cameras are arranged to inspect all surfaces of the component that are not facing a table upon which the component is mounted. The system also includes a respective data-collection station electronically coupled to each the number of robots and an associated one of the cameras. A master data-collection station is electronically coupled to each of the data-collection stations. Other systems, apparatuses, and methods are disclosed.

Abstract: A method, apparatus, and computer-readable medium for counting objects in an image, including receiving a first image having a first size greater than a size threshold; formatting the first image into a second image having a second size less than the first size; estimating, using a first object counting model, an initial object count in the second image; generating a third image using a first region of the first image in response to the initial object count being greater than an object count threshold, the first region corresponding to a first portion of the initial object count greater than a second portion of the initial object count corresponding to a second region of the first image; compiling an updated object count for the first image based on the updated first portion of the initial object count in the third image; and transmitting a notification based on the updated object count.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a low-resolution image and a text description of the low-resolution image. A mapping network generates a style vector representing the text description of the low-resolution image. An adaptive convolution component generates an adaptive convolution filter based on the style vector. An image generation network generates a high-resolution image corresponding to the low-resolution image based on the adaptive convolution filter.

Abstract: An information management system includes a reception device including a camera that images a face of a participant, and an information management apparatus. The information management apparatus issues a digital key assigned for each participant independently of personal information of the participant and transmits the digital key to the reception device. In a case where the reception device outputs an ID image of the participant, the information management apparatus acquires a face image from the reception device and stores the face image and the digital key in a database in association with each other. In a case where information including the digital key is received from a reading device installed in an event venue, the information management apparatus stores the received information in the database in association with the digital key as behavior information representing a behavior of the participant in the event venue.

Abstract: The present disclosure discloses a detection method and system of power equipment based on a multispectral image, and relates to the field of smart grid information technology. The method includes: obtaining an image of power equipment to be detected, where the image is one of an infrared image, an ultraviolet image, and a visible image; inputting the image into a pre-trained pixel-based power equipment detection model for detection, and performing classified prediction on pixels in the image to obtain a predicted result; and outputting a predicted image based on the predicted result, where the predicted image is power equipment image with background information removed, and is marked with a name of each piece of equipment. With the implementation of the present disclosure, efficiency and accuracy of power equipment detection can be improved.

Abstract: A feature quantity based on the strength of a correlation between each of a plurality of different visual stimulus patterns corresponding to a plurality of different sound sources and a pupil diameter change amount of a user is obtained, and a destination to which the user pays auditory attention for a sound from the sound source is estimated using the feature quantity.

Abstract: In various examples, circadian rhythm-based data augmentation for drowsiness detection systems and applications are provided. Embodiments described herein may produce an estimated circadian rhythm for a test subject and/or vehicle driver or other machine operator or occupant, and use the pattern of that circadian rhythm to correct, confirm, calibrate, or otherwise augment drowsiness assessments derived from video image data. The position of a person in the context of their process C circadian cycle may be used as indication of their level of drowsiness. An estimated process C circadian cycle may be used to generate more accurate ground truth training data for training machine learning models, and may be used by real-time, in-vehicle drowsiness detection systems that infer driver drowsiness levels based on captured images. In various embodiments, a circadian rhythm drowsiness estimate may be used to correct, calibrate, augment, and/or replace a drowsiness score predicted by a machine learning model.

Abstract: Unified framework for analysis and recognition of identity documents. In an embodiment, an image is received. A document is located in the image and an attempt is made to identify one or more of a plurality of templates that match the document. When template(s) that match the document are identified, for each of the template(s) and for each of one or more zones in the template, a sub-image of the zone is extracted from the image. For each extracted sub-image, one or more objects are extracted from the sub-image. For each extracted object, object recognition is performed. This may be done over one iteration (e.g., for a scanned image or photograph) or a plurality of iterations (e.g., for a video). Document recognition is performed based on the one or more templates and the results of the object recognition, and a final document-recognition result is output.

Abstract: A system for automatic annotation of multimodal data comprises: an object of interest; a mobile data collection rig comprising an image sensor and a LiDAR sensor; a plurality of mobile beacons located on the object of interest and on the mobile data collection rig; one or more processors and memory. A method for automatic annotation of multimodal data comprises: receiving ultrasonic time of flight data from a plurality of mobile beacons; estimating position data for an object of interest and position data for a mobile data collection rig based on the ultrasonic time of flight data; computing pose information for the object of interest, the mobile data collection rig, an image sensor, and a LiDAR sensor; collecting image data from the image sensor and LiDAR data from the LiDAR sensor; an automatically annotating the image and LiDAR data based on the computed pose information.

Abstract: Synthetic data generation systems and methods are disclosed for augmenting synthetic scenes using neural networks that are conditioned on depth information. The synthetic data generation system may use a guided latent diffusion model to generate (or augment) synthetic images, which can subsequently be used to train other models to perform tasks such as object detection. Input of the model may be an image rendered by a graphic engine with coarsely rendered objects. When the image is rendered, segmentation masks may also be generated for objects in the image. The synthetic data generation system may generate a monocular depth image. During the image generation phase, the corresponding segmentation mask, depth map, and any guiding textual input serve as constraints for the denoising process. During the denoising step, the synthetic data generation system may also crop and adjust the resolution of individual objects during diffusion to enhance the results.

Abstract: To identify sets of pixels in a first image that correspond to different objects or a background, a first image is provided to a Generative Adversarial Network (GAN). The GAN determines alternate images that retain the structural characteristics of the first image, such as the locations and shapes of objects, while modifying style characteristics, such as the colors of pixels. The images generated by the GAN may then be analyzed, such as by using a k-means clustering algorithm, to determine sets of pixels at the same location that change color in a similar manner across the set of images. A set of pixels that changes in a similar manner across the images generated by the GAN may be used as a mask representing an object or background to enable modification of the image without interfering with other objects.

Abstract: Techniques are disclosed for understanding the visual structure and patterns of documents and detecting anomalies in data of the documents based on the understanding of the visual structure and patterns of the documents. In one aspect, a computer-implemented method is provided that includes accessing a set of documents, converting the set of documents to a set of images in a binary format, generating a common feature template based on the set of images, comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, and outputting the images with at least one anomalous feature.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extrinsic camera calibration using a calibration object. One of the methods includes: determining physical locations of interest points of a calibration object in a calibration object centered coordinate system; determining pixel locations of the interest points in an image of the calibration object captured by a camera; determining, using the pixel locations and the physical locations, a transformation from the calibration object centered coordinate system to a camera centered coordinate system; and determining, using the transformation, a camera tilt angle and a camera mount height of the camera for use in analyzing images captured by the camera.

Abstract: Systems and methods for extracting features from screen images for performing a task mining task are provided. A screen image depicting a user interface of a computing system is received. The screen image is preprocessed to generate a preprocessed screen image and processing results. Image features are extracted from the preprocessed screen image using a first machine learning based network. Text features and control element features are extracted from the processing results using a second machine learning based network. The text features and the control element features are encoded using a third machine learning based network to generate representative features of the screen image. A task is performed on the screen image based on one or more of the image features, the text features, the control element features, or the representative features. Results of the task are output.

Abstract: CNN-based methods for cross-view image geo-localization rely on polar transform and fail to model global correlation. A pure transformer-based approach (TransGeo) is described to address these limitations from a different perspective. TransGeo takes full advantage of the strengths of the transformer related to global information modeling and explicit position information encoding. The claimed invention further leverages transformer input's flexibility and discloses an attention-guided non-uniform cropping method so that uninformative image patches are removed with a negligible drop in performance to reduce computation cost. The saved computation can be reallocated to increase resolution only for informative patches, resulting in performance improvement with no additional computation cost. This “attend and zoom-in” strategy is highly similar to human behavior when observing images.