Abstract: An activity recognition system may comprise a local device and a server. The local device may be configured to: obtain a video; determine whether at least one human is present in one or more frames of the video; in response to determining the at least one human present in the video, determine if the at least one human in each of the frames corresponds to the same person; in response to determining that the at least one human in each of the frames corresponds to the same person, determine if the person is a stranger; and in response to determining that the person is not a stranger, transmit at least a portion of the video to the server. The server may be configured to: predict an activity class associated with the person; and trigger an alarm based on the activity class.

Abstract: Methods, apparatus and systems for wireless vital monitoring are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless channel of a venue; a receiver configured for receiving a second wireless signal through the wireless channel; and a processor. The second wireless signal comprises a reflection of the first wireless signal by at least one living being having at least one repetitive motion in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless channel based on the second wireless signal; generating, for each living being of the at least one living being, a vital signal representing all repetitive motions of the living being based on the TSCI; extracting, from the vital signal of each living being, a heartbeat signal; and monitoring, for each living being in the venue, a heart rate variability based on the heartbeat signal.

Abstract: The embodiments herein relate to a method performed by a first node (101) for enabling testing of at least a part (105, 108) of the first node (101) in a deployed network. The first node (101) determines that at least a part (105, 108) of the first node (101) should be tested, and determines a direction in which to transmit or receive a test signal (200) to or from a second node (103). The first node (101) transmits the test signal (200) OTA in the determined direction to the second node (103) if a transmitter of the first node (101) should be tested. The first node (101) receives the test signal (200) OTA in the determined direction from the second node (103) if a receiver of the first node (101) should be tested.

Abstract: An object detection method, a neural network training method, an apparatus, and an electronic device include: obtaining, through prediction, multiple fused feature graphs from images to be processed, through a deep convolution neural network for target region frame detection, obtaining multiple first feature graphs from a first subnet having at least one lower sampling layer, obtaining multiple second feature graphs from a second subnet having at least one upper sampling layer, and obtaining fused graph by fusing multiple first feature graphs and multiple second feature graphs respectively; and obtaining target region frame data according to the multiple fused feature graphs. Because the fused feature graphs better represent semantic features on high levels and detail features on low levels in images, target region frame data of big and small objects in images can be effectively extracted according to the fused feature graphs, thereby improving accuracy and robustness of object detection.

Abstract: Provided are an image processing apparatus, an image processing method, and an imaging apparatus. An image processing apparatus includes a demosaic processing unit configured to apply demosaic processing to a mosaic image having a first number of colors to generate a multispectral image having a second number of colors equal to or more than three but less than the first number of colors.

Abstract: A medical data processing method, performed by a computer (2), for determining error analysis data describing the registration accuracy of a first elastic registration between first and second image data (A, B) describing images of an anatomical structure of a patient, comprising the steps of: —acquiring the first image data (A) describing a first image of the anatomical structure, —acquiring the second image data (B) describing a second image of the anatomical structure, —determining first registration data describing a first elastic registration of the first image data (A) to the second image data (B) by mapping the first image data (A) to the second image data (B) using a registration algorithm, —determining second registration data describing a second elastic registration of the second image data (B) to the first image data (A) by mapping the second image data (B) to the first image data (A) using the registration algorithm, —determining error analysis data describing the registration accuracy of the firs

Abstract: A system for processing images captured by a movable object includes one or more processors individually or collectively configured to process a first image set captured by a first imaging component to obtain texture information in response to a second image set captured by a second imaging component having a quality below a predetermined threshold, and obtain environmental information for the movable object based on the texture information. The first imaging component has a first field of view and the second imaging component has a second field of view narrower than the first field of view.

Abstract: A biological sample system that efficiently images and registers codes corresponding to biological samples depicted in an image. The biological sample system can implement a neural network to detect the codes individually, and further translate each code into an item identifier. The item identifiers are correlated with network server accounts and a user interface can depict the biological samples with data indicating their registration state.

Abstract: A person tracking method, comprising: acquiring N frames in units of time windows; acquiring, in time windows, tracking paths of a target person according to the N frames; and constructing continuous tracking paths by means of continuous time windows, so as to obtain the tracking results of the target person.

Abstract: An approach is provided for predicting a pose error for a sensor system based on a trained machine learning model. The approach, for example, involves receiving images depicting a survey point with a known physical location. The approach also involves determining meta-data associated with the sensor system used to capture the images. The approach further involves generating a ray from the capture location through a pixel location of the survey point on an image plane of each image. The approach further involves calculating an error between the ray generated for the image and the known physical location. The approach further involves training a machine learning model to predict a pose error from image data captured using the sensor system based on the error in combination with features extracted from the image and the meta-data for the image. The approach further involves providing the trained machined learning as an output.

Abstract: A high-frequency module includes a semiconductor chip device that is mounted on an external circuit substrate by wire bonding. A switch forming section, a power amplifier forming section and a low noise amplifier forming section, realized by a group of FETs, which are active elements, are formed in the semiconductor chip device. Flat plate electrodes, which form capacitors are formed in the semiconductor chip device. Conductor wires that connect the external circuit substrate and the semiconductor chip device function as inductors. A group of passive elements that includes inductors and capacitors is formed. As a result, a high-frequency module that can be reduced in size while still obtaining the required transmission characteristic is realized.

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: Various embodiments of the present invention relate to an apparatus and a method for outputting, from an electronic device, at least one piece of content corresponding to an object included in an image, wherein the electronic device comprises: a camera; a display; at least one processor; and a memory electrically connected to at least one processor, and the memory can store, when being executed, instructions for causing at least one processor to: acquire an image including the object through the camera; confirm first information related to a shape of the object through a first area corresponding to the object in the image; confirm second information related to additional information on the object through a second area in the image; and provide, through the display, at least one content corresponding to the first information acquired from an external device on the basis of at least the first information and the second information. Other embodiments are possible.

Abstract: An image processing device which is capable of accurately detect pixels covered by cloud shadows and remove effects of the cloud shadows in an images are provided. The device includes: a cloud transmittance calculation unit that calculates transmittance of the one or more clouds in an input image, for each pixel; a cloud height estimation unit that determines estimation of a height from the ground to each cloud in the input image to detect position of corresponding one or more shadows; an attenuation factor estimation unit that calculates attenuation factors for the direct sun irradiance by applying an averaging filter to the cloud transmittance calculated; and a shadow removal unit that corrects pixels affected by the one or more shadows, based on a physical model of a cloud shadow formation by employing the attenuation factors calculated and the position, and outputs an image which includes the pixels corrected.

Abstract: A computer-implemented method and system may include receiving one or more images of a body; determining one or more initial body measurements of the body based on the one or more images; determining at least one supplemental measurement of a portion of the body, the supplemental measurement determined at least in part using one or more sensors of a mobile device; and applying a correction factor to the one or more initial body measurements of the body based at least in part on the supplemental measurement to produce a corrected body measurement.

Abstract: A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.

Abstract: A scent visualization system may comprise a display apparatus for generating a target image including a target object; an olfactory sensor for detecting a scent; and a scent visualization apparatus for generating target associative visualization information that reminds the scent from the target image received from the display apparatus and sensing information received from the olfactory sensor, and generating an associative image by combining the target associative visualization information and the target image. Therefore, a low-cost, high-efficiency, high-utilization, and high-convenience scent visualization system can be provided.

Abstract: An X-ray imaging system includes the following. An X-ray Talbot imaging apparatus includes an X-ray source, a plurality of gratings, and an X-ray detector. An X-ray is irradiated from the X-ray source through the examined target which is an object and the plurality of gratings and to the X-ray detector to obtain a moire image necessary to generate the reconstructed image of the examined target. A first database shows, for each name or type of material, a correlation between information regarding a signal strength in the reconstructed image generated based on the moire image and quality information of the material included in the examined target. A controller estimates as the evaluation index the quality information in the examined target from the reconstructed image based on information regarding the input name or the type of material and input shape information and the first database.

Abstract: Herein is disclosed a set of instructions, configured to cause one or more processors to receive data representing one or more parameters of an obstacle relative to a vehicle; generate a visual representation of the one or more parameters; and instruct a display controller to display a composite image, the composite image comprising a content image within an inner region of a display and the visual representation within an outer region of the display.

Abstract: A method, computer readable medium, and system are disclosed for performing unconstrained appearance-based gaze estimation. The method includes the steps of identifying an image of an eye and a head orientation associated with the image of the eye, determining an orientation for the eye by analyzing, within a convolutional neural network (CNN), the image of the eye and the head orientation associated with the image of the eye, and returning the orientation of the eye.