Abstract: A computer-implemented method for road layout prediction is provided. The method includes segmenting, by a first processor-based element, an RGB image to output pixel-level semantic segmentation results for the RGB image in a perspective view for both visible and occluded pixels in the perspective view based on contextual clues. The method further includes learning, by a second processor-based element, a mapping from the pixel-level semantic segmentation results for the RGB image in the perspective view to a top view of the RGB image using a road plane assumption. The method also includes generating, by a third processor-based element, an occlusion-aware parametric road layout prediction for road layout related attributes in the top view.

Abstract: A technique for geospatial tracking includes receiving live video from a first source, rendering a map that encompasses a geographical region imaged by the live video, and displaying the map along with the live video, thereby enabling a human user or computer to associate objects in the video with geographical features on the map. The technique further includes receiving geotagged content from a second source and displaying a representation of the geotagged content along with the video and the map.

Abstract: There is provided a computer-implemented method of estimating lung volume from radiographic images. The method comprises: registering (102) a two dimensional radiographic image (12) of a patients chest to a three dimensional radiographic image (14) of the patients chest to estimate data describing projection geometry (16) of an imaging setup used to capture the two dimensional radiographic image; using the projection geometry to estimate (104) at least one radiographic magnification factor (18) relating to the imaging setup; and calculating (106) an estimated lung volume (20) using the two dimensional radiographic image and the at least one radiographic magnification factor.

Abstract: A method of registering two sets of medical image data taking into account scene changes can include providing a first and a second medical image data set by means of a medical device, subdividing the first and second medical image data sets into an equal number of sub-images, performing a number of individual registrations between the first and second medical image data sets with respective optimization of a similarity measure, identifying the sub-images that have a scene change, and performing a final registration between the first and the second medical image data set by masking out the identified sub-images or a masked out sub-image combination. For each individual registration, at least one sub-image of the first and/or second medical image data set can be masked out by means of a random process when determining the respective measure of similarity.

Abstract: A learning device allocates which feature value of a sub-object is extracted by a module from a group of sub-objects constituting an object of an image to each of modules that extract feature values of the object in the image in a deep neural network that is a learning target. After that, the learning device performs first learning to perform learning of the respective modules so that the respective modules are capable of precisely picking up regions of sub-objects allocated to the modules using information indicating the regions of the sub-objects in an image for each of images and second learning to perform learning of the respective modules so that analysis precision of an image analysis is further improved using a result of the image analysis based on the feature values of the sub-objects picked up by the respective modules.

Abstract: Systems, devices, and methods are disclosed for generating an authenticity score for a creative work using digital image data of one or more creative works. A system receives, via a network, first digital image data of a first creative work. The system determines from the first digital image data, a first set of feature variables, each corresponding to a characteristic of the first creative work. The system determines, via an artificial intelligence model and with input including the first set of feature variables and a comparison dataset, an authenticity score for the first creative work. The artificial intelligence model generates, based on the portion of first feature variables of the first creative work that match corresponding portions of the comparison dataset, an output indicative of an authenticity score. The system communicates, via the network, an authenticity score of the first creative work to a client device.

Abstract: A system includes a host device to provide a content signal, an end device to receive the content signal, and a branch of amplifiers connected between the host device and the end device to carry the content signal from the host device to the end device. The host device may receive a first signal from the branch of amplifiers connected to the end device, and extract, from the first signal, first characteristics of the content signal measured at one or more points in the branch of amplifiers. The host device may compare the first characteristics of the content signal to second characteristics. The host device may determine, based on the comparison, adjustments for one or more settings of a first amplifier of the amplifiers, and send a second signal to the branch of amplifiers to communicate the adjustments for the one or more settings to the first amplifier.

Abstract: This application relates to the field of wireless communications technologies, and discloses a satellite location information transmission method, and related apparatus, system, and storage medium. The method includes: receiving satellite location information sent by at least one satellite base station, where the satellite location indicates a location of a satellite base station in a satellite orbit; determining a target satellite base station among the at least one satellite base station based on the received satellite location information; and initiating a random access process to the target satellite base station. The technical solutions provided in the embodiments of this application can reduce UE power consumption.

Abstract: In an embodiment, a method for estimating a composition of food includes: receiving a first three-dimensional (3D) image; identifying food in the first 3D image; determining a volume of the identified food based on the first 3D image; and estimating a composition of the identified food using a millimeter-wave radar.

Abstract: A thermal image-based hybrid object detection method using a YOLO model and an object tracking technique includes: a) a step of obtaining a plurality of thermal image data by means of a thermal image camera; b) a step of extracting an object region from a thermal image data of the thermal image camera using an installed YOLO model and then creating an object region coordinate data by returning coordinates of the object region by means of an image processor; and c) a step of outputting a first image reflecting an object region coordinate data to each of thermal image data received from the image processor by means of an output device when the output device requests transmission of the first image.

Abstract: There is provided an identification method acquiring a first image, a pixel value of each of pixels of which represents a distance from a first position to an imaging target object including an identification target object, acquiring a second image captured from the first position or a second position different the first position, a pixel value of each of pixels of the second image representing at least luminance of reflected light from the imaging target object, identifying a type of the identification target object based on the second image, and calculating, based on the first image, an indicator value indicating a reliability degree of an identification result of the type of the identification target object based on the second image.

Abstract: The present disclosure relates to an image processing method and apparatus, a screening system, and a computer-readable storage medium. An image processing method includes: acquiring an image to be processed; extracting a first feature of the image to be processed by using a first feature extractor; performing first classification on the image to be processed according to the first feature by using a first classifier; under the condition that a classification result is a first result, extracting a second feature of the image to be processed by using a second feature extractor; performing second classification on the image to be processed according to the second feature by using a second classifier; and outputting the classification result of the second classification.

Abstract: Techniques for determining detection boxes representing objects in an environment using pixel clustering are disclosed herein. Autonomous vehicle sensors can capture data in an environment that may include separate objects, such as large and/or articulated vehicles. In an example, the data can include a plurality of pixels that are associated with a large object. A vehicle computing system can generate a detection box for each pixel of the plurality of pixels and can determine at least one averaged detection box representing the large object. In an example, the vehicle computing system can control the vehicle based in part on the at least one averaged detection boxes.

Abstract: Systems and techniques are provided for generating one or more models. For example, a process can include obtaining a plurality of input images corresponding to faces of one or more people during a training interval. The process can include determining a value of the coefficient representing at least the portion of the facial expression for each of the plurality of input images during the training interval. The process can include determining, from the determined values of the coefficient representing at least the portion of the facial expression for each of the plurality of input images during the training interval, an extremum value of the coefficient representing at least the portion of the facial expression during the training interval. The process can include generating an updated bounding value for the coefficient representing at least the portion of the facial expression based on the initial bounding value and the extremum value.

Abstract: The present disclosure provides a method and a device for retina image recognition, an electronic equipment, and a storage medium, the method including: acquiring a retinal image; classifying the retinal image by using a first neural network model to obtain an image classification result; if the image classification result meets a preset condition, segmenting the retinal image by using a second neural network model to obtain an image segmentation result; generating a recognition result of the retinal image according to the image segmentation result and in combination with a decision tree model.

Abstract: Embodiments may include novel techniques to improve detection of objects in images, for example, in Digital Breast Tomography and that are applicable to ensembles of detectors. For example, a method may comprise generating a plurality of candidate bounding boxes for each of a plurality of image slices of imaged tissue, each generated candidate bounding box having a probability score, collecting at least some of the generated candidate bounding boxes for each slice, generating a heat map of the filtered candidate bounding boxes and filtering the candidate bounding boxes in the heat map based on a first threshold of values in the heat map, performing Non-Maximum Suppression on the heat map filtered candidate bounding boxes, and outputting at least one bounding box based on the probability score.

Abstract: An apparatus for controlling docking with a spacecraft includes at least one camera for generating at least one image pixel stream of the spacecraft. A field programmable gate array (FPGA) receives the at least one image pixel stream from the at least one camera. The FPGA texture processes the at least one pixel stream to generate at least one texture map for the at least one image pixel stream. A processor receives the at least one texture map from the FPGA and calculates texture map statistics responsive to the generated at least one texture map, generates thresholding results for the at least one pixel image stream responsive to the generated at least one texture map, determines a bus centroid of the spacecraft responsive to the generated thresholding results and outputs the determined bus centroid of the spacecraft. A docking controller controls docking of with the spacecraft responsive to the determined bus centroid of the spacecraft.

Abstract: Systems, devices, and methods are disclosed for generating an authenticity score for a creative work using digital image data of one or more creative works. A system receives, via a network, first digital image data of a first creative work. The system determines from the first digital image data, a first set of feature variables, each corresponding to a characteristic of the first creative work. The system determines, via an artificial intelligence model and with input including the first set of feature variables and a comparison dataset, an authenticity score for the first creative work. The artificial intelligence model generates, based on the portion of first feature variables of the first creative work that match corresponding portions of the comparison dataset, an output indicative of an authenticity score. The system communicates, via the network, an authenticity score of the first creative work to a client device.

Abstract: Devices, systems and processes for the detection of unsafe cabin conditions that provides a safer passenger experience in autonomous vehicles are described. One example method for enhancing passenger safety includes capturing at least a set of images of one or more passengers in the vehicle, determining, based on the set of images, the occurrence of an unsafe activity in an interior of the vehicle, performing, using a neural network, a classification of the unsafe activity, and performing, based on the classification, one or more responsive actions.

Abstract: Detection of pathological abnormalities in tissue samples and/or pluralities of cells is a highly specialized and time-consuming effort, usually performed by a select group of clinicians and technical personnel. Described herein are methods for more automatable, consistent and comprehensive cell sample analysis to deliver a rapid, reliable and detailed classification, e.g., diagnosis, of the status of cells present in a sample, particularly, but not limited to cancer diagnosis.