Abstract: A video processing circuit includes an input buffer, an online adaptation circuit, and an artificial intelligence (AI) super-resolution (SR) circuit. The input buffer receives input low-resolution (LR) frames and high-resolution (HR) frames from a video source over a network. The online adaptation circuit forms training pairs, and calculates an update to representative features that characterize the input LR frames using the training pairs. Each training pair formed by one of the input LR frames and one of the HR frames. The AI SR circuit receives the input LR frames from the input buffer and the representative features from the online adaptation circuit. Concurrently with calculating the update to the representative features, the AI SR circuit generates SR frames for display from the input LR frames based on the representative features. Each SR frame has a higher resolution than a corresponding one of the input LR frames.

Abstract: A method and system for image-based region detection. Transformation matrices are computed by performing image registration between a target image and each of one or more reference images. Each transformation matrix is for transforming each of the reference images into a coordinate system of the target image. An optimal reference image is selected from among the reference images based on similarity measures between the target image and each reference image. The transformation matrix of the selected reference image is applied to a reference map associated with the reference image in order to generate a target map for the target image. The target map includes region labels indicating regions shown in the target image.

Abstract: A method of generating synthetic images of virtual scenes includes: placing, by a synthetic data generator implemented by a processor and memory, three-dimensional (3-D) models of objects in a 3-D virtual scene; adding, by the synthetic data generator, lighting to the 3-D virtual scene, the lighting including one or more illumination sources; applying, by the synthetic data generator, imaging modality-specific materials to the 3-D models of objects in the 3-D virtual scene in accordance with a selected multimodal imaging modality, each of the imaging modality-specific materials including an empirical model; setting a scene background in accordance with the selected multimodal imaging modality; and rendering, by the synthetic data generator, a two-dimensional image of the 3-D virtual scene based on the selected multimodal imaging modality to generate a synthetic image in accordance with the selected multimodal imaging modality.

Abstract: An information processing device may be configured to communicate with a communication destination device selected as a communication target among a plurality of devices. The information processing device may store, in the storage unit, first identifying information for identifying a device that was selected as a communication destination device among the plurality of devices. The information processing device may execute a connection confirmation process confirming that a communication interface has received connection confirmation information sent by the communication destination device identified by the first identifying information stored in the storage unit when instructions are activated by the information processing device.

Abstract: Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor.

Abstract: A method for determining a quantity of interest related to the density of organic tissue starts with a digital representation of a histological image of the tissue. The digital representation is converted to a binary image, to discriminate pixels that represent tissue of interest in the image. A box filter is applied to values of the pixels of interest to obtain a tissue density value for each pixel of interest. A quantity of interest is computed, based upon the tissue density values for the pixels of interest. A tangible representation of the computed quantity of interest, such as a numerical value, a graph, or a color representation, is displayed or otherwise presented via an interface.

Abstract: Methods, apparatus and systems for improving accuracy and efficiency of wireless monitoring are described. In one example, a described method comprises: transmitting a first wireless signal from a first wireless device with transmit antennas through a wireless multipath channel of a venue; receiving a second wireless signal by a second wireless device with receive antennas through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a motion of an object in the venue; obtaining a number of time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each TSCI is associated with a respective one of the transmit antennas and a respective one of the receive antennas; preprocessing the number of TSCI; and monitoring the motion of the object based on the number of preprocessed TSCI.

Abstract: Techniques are provided for stitching tile images. A local registration can be performed to determine spatial relationships between pairs of adjacent tile images, in which the spatial relationships may be determined using normalized cross correlation (NCC) scores computed within a multi-resolution framework. A global placement can be performed to position all scanned tile images relative to one another. The global placement is determined with weighted least squares utilizing the determined spatial relationships between all adjacent greyscale tile images and the NCC scores as weights.

Abstract: A specific environmental stress on vegetation is calculated highly accurately and relatively easily. For generation of environmental stress information, vegetation information is obtained first using an imaging signal of vegetation. Furthermore, reference vegetation information associated with vegetation information in a state of being free of, for example, a specific environmental stress is obtained. Moreover, as information associated with an environmental stress on the vegetation, difference information between vegetation information acquired from an imaging signal of vegetation in a state of being likely to have the specific environmental stress and the reference vegetation information is obtained.

Abstract: Described herein is a detector for determining a position of an object. The detector includes a sensor element having a matrix of optical sensors, each designed to generate a sensor signal in response to an illumination of its light-sensitive area by a light beam propagating from the object to the detector. The detector also includes an evaluation device configured to select a region of interest of the matrix, respectively determine a sensor signal of at least two optical sensors of the region of interest, and determine a longitudinal coordinate zDPR of the object by evaluating a combined sensor signal Q. The evaluation device is also configured to determine an image of the region of interest from the sensor signals, determine a longitudinal coordinate zDFD of the object by optimizing at least one blurring function fa, and determine combined distance information z considering the longitudinal coordinates zDPR and zDFD.

Abstract: A method for removing satellite trails from images includes the following: obtaining an initial image having stars, locating stars in the image, removing the located stars from the initial image to form a star-removed image, locating the satellite trail in the star-removed image, determining brightness information for the satellite trail in the initial image including trail wobble information, creating a satellite-trail-containing image with only satellite trail brightness information based on the determined brightness information, and subtracting the satellite-trail-containing image from the initial image to remove the satellite trail so as to form a satellite-trail-removed image. Systems for removing satellite trails from images are also provided, as are other aspects.

Abstract: Devices and methods related to unpowered switching module. A method of forming a switching module can include providing a first input terminal of the switching module; providing a second input terminal of the switching module; and providing an output terminal of the switching module configured to output a radio-frequency (RF) component of an input signal received on the first input terminal or the second input terminal in response to the input signal including a positive direct-current (DC) voltage.

Abstract: The disclosure relates to method and system for improving performance of an artificial neural network (ANN) model. The method includes receiving the ANN model and input dataset. The ANN model includes neurons arranged in multiple layers and employing corresponding activation functions. The method further includes assigning a random activation threshold value to each of the corresponding activation functions, determining activated neurons in each layer for a majority of input data in the input dataset based on the random activation threshold value for each of the corresponding activation functions, identifying removable layers based on a number of activated neurons and a pre-defined threshold value, evaluating a relative loss of the ANN model upon removing each removable layer from the ANN model and for a random input data in the input dataset, and deriving a modified ANN model by removing one or more of the removable layers based on the evaluation.

Abstract: A three-dimensional object reconstruction method, applied to a terminal device or a server, is provided. The method includes obtaining a plurality of video frames of an object; determining three-dimensional location information of key points of the object in the plurality of video frames and physical meaning information of the key points, the physical meaning information indicating respective positions of the object; determining a correspondence between the key points having the same physical meaning information in the plurality of video frames; and generating a three-dimensional object according to the correspondence and the three-dimensional location information of the key points.

Abstract: An image processing apparatus includes a first image generator and a second image generator. The first image generator generates a first image, including a predetermined ruled-line image and an inscription image, from a second sheet in a sheet group. The sheet group is obtained by stacking multiple sheets including a single first sheet and the second sheet. The first sheet has inscription information inscribed thereon. The second sheet has the inscription image corresponding to the inscription information transferred thereon and includes the ruled-line image. The second image generator generates a second image in which a surplus image is removed from the first image generated by the first image generator in accordance with a learning model that has learned to remove the surplus image different from the ruled-line image and the inscription image.

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Abstract: Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Abstract: Methods and systems for blending line image data streams from a film scanner are disclosed. In one embodiment, a method is provided including receiving multiple data streams from line scan sensors. An overlap position may then be initially selected, which forms an overlap position between two of the data streams. A difference measure may then be calculated between the two data streams within the overlap area. The overlap positions may then be iteratively altered between a plurality of overlap positions, and additional difference measures may be computed for each overlap position. A blending position may then be selected from the plurality of overlap positions, which may correspond to the overlap position with the smallest difference measure. The data streams may then be blended at the selected blending position.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for compressing a deep learning model. An embodiment of the method includes: acquiring a to-be-compressed deep learning model; pruning each layer of weights of the to-be-compressed deep learning model in units of channels to obtain a compressed deep learning model; and sending the compressed deep learning model to a terminal device, so that the terminal device stores the compressed deep learning model. By pruning each layer of weights of the deep learning model in units of channels, the parameter redundancy of the deep learning model is effectively reduced, thereby improving the computational speed of the deep learning model and maintaining the model accuracy.

Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.