Abstract: Devices and methods related to capturing 360 degree spherical images are provided. A computing device can capture, substantially simultaneously, a first image with the first image sensor and a second image with a second image sensor. The first image sensor can be positioned on a first side of the computing device and oriented at a first orientation with respect to an axis of rotation. The second image sensor can be positioned on a second side of the device substantially opposite the first side of the device and oriented at a second orientation that is axially rotated from the first orientation with respect to the axis of rotation. The computing device can stitch together the first image and the second image to create an output image that captures a 360 degree field of view with respect to the computing device.

Abstract: Embodiments of the disclosure provide a machine learning model for generating a predicted executable command for an image. The learning model includes an interface configured to obtain an utterance indicating a request associated with the image, an utterance sub-model, a visual sub-model, an attention network, and a selection gate. The machine learning model generates a segment of the predicted executable command from weighted probabilities of each candidate token in a predetermined vocabulary determined based on the visual features, the concept features, current command features, and the utterance features extracted from the utterance or the image.

Abstract: A three-dimensional imaging method and system for surface comprehensive geophysical prospecting, the method includes: acquiring detection data of a plurality of two-dimensional profiles of a surface detection site; forming two-dimensional profile resistivity data by geophysical inversion of the detection data; performing three-dimensional coordinate conversion on the two-dimensional profile resistivity data to obtain resistivity data of a three-dimensional coordinate system; and converting the resistivity data of the three-dimensional coordinate system into a three-dimensional model by using a Kriging interpolation method.

Abstract: A method for training an image segmentation model includes calling an encoder to perform feature extraction on a sample image and a scale image to obtain a sample image feature and a scale image feature. The method also includes performing a class activation graph calculation to obtain a sample class activation graph and a scale class activation graph. The method also includes calling a decoder to obtain a sample segmentation result of the sample image, and calling the decoder to obtain a scale segmentation result of the scale image. The method also includes calculating a class activation graph loss and calculating a scale loss. The method also includes training the decoder based on the class activation graph loss and the scale loss.

Abstract: A semantics-based high resolution reconstruction method of a nighttime light remote sensing image includes: constructing a sample data set; the sample data set includes a plurality of data groups, and each data group includes a LR NTL image, and a HR NTL image and light semantics information consistent in spatial position with the LR NTL image; constructing a reconstruction model; performing training and validation on the reconstruction model by using the sample data set to obtain an optimized reconstruction model; and taking a to-be-reconstructed LR NTL image and light semantic information corresponding to the to-be-reconstructed LR NTL image as an input of the optimized reconstruction model, and outputting, by the optimized reconstructed model, a HR NTL image obtained through resolution reconstruction.

Abstract: In one embodiment, an image sensor captures an image and passes the image to an image processing system that detects one or more vehicle objects and one or more human objects, using a single detection function having a single artificial intelligence (AI) model. Each of the detected objects correspond to a portion of the captured image. For each vehicle object, the corresponding image portion is processed to determine a plurality of properties of the vehicle, including, e.g., type (truck, van, sedan, motorcycle, etc.), color, and license number. For each human object, the corresponding portion of the image is processed to determine a plurality of properties of the human, e.g., gender, approximate age, skin tone, eye color, hair color, and facial landmarks for facial recognition. The captured image is annotated to describe the objects and their properties, and annotated image is transmitted to a service for use by an application.

Abstract: A tracking method, for tracking an object based on a computer vision, includes following steps. A series of images is captured by a tracking camera. A first position of a trackable device is tracked within the images. An object is recognized around the first position in the images. In response to the object being recognized, a second position of the object is tracked in the images.

Abstract: A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Abstract: An image inpainting system is described that receives an input image that includes a masked region. From the input image, the image inpainting system generates a synthesized image that depicts an object in the masked region by selecting a first code that represents a known factor characterizing a visual appearance of the object and a second code that represents an unknown factor characterizing the visual appearance of the object apart from the known factor in latent space. The input image, the first code, and the second code are provided as input to a generative adversarial network that is trained to generate the synthesized image using contrastive losses. Different synthesized images are generated from the same input image using different combinations of first and second codes, and the synthesized images are output for display.

Abstract: Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.

Abstract: An image recognition method applied to an electronic device is provided. The method includes constructing a first semantic segmentation network. In response that an initial labeled result of one of a plurality of initial labeled images does not match a preset labeled result, a target image corresponding to the one of the plurality of initial labeled images and a target labeled result of the target image are obtained. A second semantic segmentation network is obtained by training the first semantic segmentation network based on a plurality of the target images and the target labeled result of each target image, and a labeled result of an image to be recognized is obtained by inputting the image to be recognized into the second semantic segmentation network.

Abstract: One embodiment of the present invention sets forth a technique for generating data. The technique includes sampling from a first distribution associated with the score-based generative model to generate a first set of values. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes converting the first set of latent variable values into a generative output.

Abstract: According to an embodiment of the present invention, there is provided a magnetic resonance image processing method including: acquiring a low-quality training image, a first parameter group including at least one scan parameter applied when the training image is acquired, a high-quality label image, and a second parameter group including at least one scan parameter applied when the label image is acquired; and training an artificial neural network model by using the training image, the first parameter group, the label image, and the second parameter group.

Abstract: A method and a computing device for clustering phishing web resources based on images of visual content thereof are provided. The method comprises: receiving references to a plurality of phishing web resources; generating, for a given phishing web resource of the plurality of phishing web resources, at least one image of a visual content of the given phishing web resource; analyzing the at least one image associated with the given phishing web resource, the analyzing comprising identifying contours of elements of the visual content of the given phishing web resource within the at least one image; conducting pairwise comparison between the contours associated with the given phishing web resource and contours of stored clusters of visual content images; and storing, in a database, data indicative of an association between the given phishing web resource and a respective cluster of the at least one image.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an image source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: A three-dimensional data decoding method includes: obtaining three-dimensional space information including encoded three-dimensional points; calculating an angle formed by a line segment connecting a predetermined position in the three-dimensional space information and a first base point and a line segment connecting the first base point and a second base point different from the first base point; determining whether the angle calculated satisfies a predetermined condition; and decoding the encoded three-dimensional points included in the three-dimensional space information when the angle calculated is determined to satisfy the predetermined condition, and not decoding the encoded three-dimensional points included in the three-dimensional space information when the angle calculated is determined not to satisfy the predetermined condition.

Abstract: A judgement method includes: selecting two or more living body judgement processes among a plurality of living body judgement processes; determining an order of executing the two or more selected living body judgement processes; executing the two or more selected living body judgement processes in the determined order; executing, in each of the two or more selected living body judgement processes: a processing of showing a subject an action required; a processing of acquiring a facial image containing a face of the subject when acting in accordance with the shown action; and a processing of judging whether the subject is of living body based on a feature of a section of the face contained in the facial image; and judging that the subject is of living body when judgement results obtained from the two or more selected living body judgement processes satisfy a predetermined condition.

Abstract: In a learning device, a first learning unit performs first machine learning using first training data including a first evaluation result for an evaluation target image to generate a first learned model outputting a second evaluation result for an input image, an evaluation unit uses the first learned model to acquire a plurality of the second evaluation results for a plurality of the input images, a generation unit selects a second image quality parameter from a plurality of first image quality parameters having different values, based on the plurality of the second evaluation results and generates second training data including the selected second image quality parameter, and a second learning unit performs second machine learning using the second training data to generate a second learned model outputting a third image quality parameter used for processing a processing target image.

Abstract: Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

Abstract: A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.