Abstract: A device for locating, by a processing module, an acquisition module relative to a device to be monitored, comprises first processing means arranged in the region of the acquisition module, configured to generate a first position of the acquisition module, and to generate an image, referred to as a high-resolution image, of the device to be monitored; communication modules for transmitting, from the acquisition module to the processing module, the first position and the high-resolution image; and second processing means, arranged in the region of the processing module, configured to generate a second position, from the first position and the high-resolution image.

Abstract: An apparatus includes a display control unit, a receiving unit, an adjusting unit, and a determination unit. The display control unit is configured to display an image showing a result of detection of a defect from a captured image of a structure on a display device. The receiving unit is configured to receive an operation to specify part of the displayed image as a first region and an operation to give an instruction to correct at least part of the detection data corresponding to the first region. The adjusting unit is configured to adjust a parameter to be applied to the first region according to the instruction. The determination unit is configured to determine one or more second regions to which the adjusted parameter is to be applied from a plurality of segmented regions of the image.

Abstract: Computerized systems, and method and computer readable media. The method may include receiving, by a neural network, input face visual information; wherein the neural network comprises multiple convolutional layers, an embedding layer and one or more conversion layers; generating, by the embedding layer, a face recognition (FR) feature vector that comprises multiple FR feature elements; and generating a binary representation of the face recognition features based on the FR feature vector.

Abstract: An edge computing system, computer readable storage medium and method for object detection, including processing circuitry. The processing circuitry is configured with a hybrid CNN and vision transformer backbone network in an object detection deep learning network. The backbone network receives an image, and includes a first convolutional encoder to extract local features from feature maps of the image, a second stage having consecutive second convolutional encoders, a positional encoding layer, split depth-wise transpose attention (SDTA) encoders, consecutive convolutional encoders, a third stage and a fourth stage SDTA encoder. Each of the SDTA encoders perform multi-headed self-attention by applying a dot product operation across channel dimensions in order to compute cross-covariance across channels to generate attention feature maps.

Abstract: An embodiment for detecting the danger of an object in close proximity of a subject is provided. The embodiment may include receiving real-time data from one or more IoT devices in a surrounding environment. The embodiment may also include detecting and classifying one or more subjects and one or more objects in an image from the one or more IoT devices. The embodiment may further include identifying one or more risk factors associated with each object. The embodiment may also include correlating the one or more risk factors associated with each object with the one or more subjects in the image. The embodiment may further include identifying relative positions of the one or more subjects and the one or more objects in the image. The embodiment may also include in response to determining a current position of at least one subject is dangerous, notifying a user of the danger.

Abstract: There is provided a system and method for mask inspection, comprising: obtaining a plurality of images, each representative of a respective part of the mask; generating a CD map of the mask comprising a plurality of composite values of a CD measurement of a POI respectively derived from the plurality of images, comprising, for each given image: dividing the given image into a plurality of sections; searching for the POI in the plurality of sections, giving rise to a set of sections, each with presence of at least one of the POI therein; for each section, obtaining a value of the CD measurement using a printing threshold, giving rise to a set of values of the CD measurement corresponding to the set of sections; and combining the set of values to a composite value of the CD measurement corresponding to the given image.

Abstract: This disclosure relates generally to a method and system for multi-modal image super-resolution. Conventional methods for multi-modal image super-resolution are performed using joint image based filtering, deep learning and dictionary based approaches which require large datasets for training. Embodiments of the present disclosure provide a joint optimization based transform learning framework wherein a high-resolution (HR) image of target modality is reconstructed from a HR image of guidance modality and a low-resolution (LR) image of target modality. A set of parameters, transforms, coefficients and weight matrices are learnt jointly from a training data which includes a HR image of guidance modality, a LR image of target modality and a HR image of target modality. The learnt set of parameters are used for reconstructing a HR image of target modality. The disclosed joint optimization transform learning framework is used in remote sensing, environment monitoring and so on.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to determine an estimated volume of an object captured by a three-dimensional (3D) point cloud. A 3D point cloud comprising a plurality of 3D points and a reference plane in spatial relation to the 3D point cloud is received. A 2D grid of bins is configured along the reference plane, wherein each bin of the 2D grid comprises a length and width that extends along the reference plane. For each bin of the 2D grid, a number of 3D points in the bin and a height of the bin from the reference plane is determined. An estimated volume of an object captured by the 3D point cloud based on the calculated number of 3D points in each bin and the height of each bin.

Abstract: Described herein are methods for generating and using a constrained ensemble of GANs. The constrained ensemble of GANs can be used to generate synthetic data that is (1) representative of the original data, and (2) not closely resembling the original data. An example method includes generating a constrained ensemble of GANs, where the constrained ensemble of GANs includes a plurality of ensemble members. The method also includes analyzing performance of the constrained ensemble of GANs by comparing a temporary performance metric to a baseline performance metric, and halting generation of the constrained ensemble of GANs in response to the analysis. The method also includes generating a synthetic dataset using the constrained ensemble of GANs. The synthetic dataset is sufficiently similar to the original dataset to permit data sharing for research purposes but alleviates privacy concerns due to differences in mutual information between synthetic and real data.

Abstract: A control device including: at least one processor, wherein the processor controls an image projection unit that projects a projection image onto a projection surface of a compression member in a mammography apparatus which irradiates a breast compressed by the compression member with radiation to capture a radiographic image such that a range of an irradiation field of the radiation is displayed by the projection image.

Abstract: A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression.

Abstract: A computer-implemented method for the further training of an artificial-intelligence evaluation algorithm that has already been trained based upon basic training data, wherein the evaluation algorithm ascertains output data describing evaluation results from input data comprising image data recorded with a respective medical imaging facility. In an embodiment, the method includes ascertaining at least one additional training data set containing training input data and training output data assigned thereto; and training the evaluation algorithm using the at least one additional training data set. The additional training data set is ascertained from the input data used during a clinical examination process with a medical imaging facility, which the already-trained evaluation algorithm was used, and output data of the already-trained evaluation algorithm that has been at least partially correctively modified by the user.

Abstract: In one aspect, an information processing apparatus includes a first acquisition module, a first extraction module, a first generation module, a second extraction module, a derivation module, and a first output control module. The first acquisition module acquires an input image to output a first image and a second image. The first extraction module extracts first characteristic point information from the first image. The first generation module generates a third image obtained by reducing a data amount of the second image. The second extraction module extracts second characteristic point information from the third image. The derivation module derives a difference between the first characteristic point information and the second characteristic point information. The first output control module outputs the third image corrected in accordance with the difference as an output image.

Abstract: An apparatus is described which includes two or more colour displays arranged to provide piece-wise continuous illumination of a volume, and one or more cameras arranged to image the volume. The apparatus is configured to control the colour displays and the cameras to illuminate the volume with each of two or more illumination conditions. The apparatus is also configured to obtain two or more sets of images, which include sufficient information for calculation of a reflectance map and a photometric normal map of an object or subject positioned within the volume. Each set of images is obtained during illumination of the volume with one or more corresponding illumination conditions. When viewed from the volume, the apparatus only provides direct illumination of the volume from angles within a zone of a hemisphere, which is less than a hemisphere.

Abstract: A method for processing image information of an imaging sensor of a vehicle in an artificial neural network (“ANN”) is disclosed. The ANN includes at least one encoder and one decoder. The ANN solves a classification task with a plurality of classes and/or a regression task, in which numerical output information quantized according to a plurality of quantization steps is provided. The ANN outputs multiple feature maps at the output interface, wherein allocations of image regions of the image information to classes or numerical output information quantized regarding the image information is/are output by the feature maps in an encoded manner.

Abstract: The invention concerns a method (1) for detecting and tracking objects orbiting around the earth (for example space debris) by means of on-board processing of images acquired by a space platform (for example a satellite, a space vehicle or a space station) by one or more optical sensors, preferably one or more star trackers.

Abstract: The disclosure relates generally to image processing. For example, the invention relates to a method and a device for de-noising an electron microscope (EM) image. The method includes the act of selecting a patch of the EM image, wherein the patch comprises a plurality of pixels, wherein the following acts are performed on the patch: i) replacing the value of one pixel, for example of a center pixel, of the patch with the value of a different, for example randomly selected, pixel from the same EM image; ii) determining a de-noised value for the one pixel based on the values of the other pixels in the patch; and iii) replacing the value of the one pixel with the determined de-noised value.

Abstract: A method for welding a workpiece with a vision guided welding platform. The welding platform comprises a welding tool, and a camera for guiding the movement of the welding tool from a start point to an end point. The method includes the steps of adjusting a focal length of the camera such that a focal plane of the camera is located on a surface of the workpiece and obtaining a surface image of the workpiece. The method further includes the steps of determining a current focal length of the camera, determining a corrected pixel length of a pixel in the surface image and determining the number of pixels between the start point and the end point of each movement of the welding tool. Using the corrected pixel length, a distance between the start and end points is determined and the welding tool is guided to move therebetween.

Abstract: A method of generating a defect image for deep learning and a system therefor are provided. The method and the system are intended to be used in generating training data for an artificial intelligence algorithm. More specifically, the training data are defect images required to train an algorithm that identifies a defect from a product.

Abstract: A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.