Abstract: Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; at least one first line buffer for outputting the image into a line-wise image line; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the output from the first line buffer; and a feature map processor for storing the output from the first convolution operator in units of at least one line, and processing so as to output the feature map stored in units of at least one line into a two-dimensional form, wherein at least one convolution operation operates in the form of a pipeline.

Abstract: In the present invention, a right leg drive RLD monitoring system is employed on a medical computing system/computer, such as an ECG, HEMO and/or EP monitoring, mapping and/or recording system, that includes a number of RLD circuits to be utilized for different procedures or monitoring states to be performed using the system. The RLD monitoring system operates to actively monitor and/or record the feedback voltage to the RLD isolated from the patient. Using the measured feedback voltage data, the RLD monitoring system can identify and determine if the RLD circuit in use is approaching saturation, has reached saturation and the duration the RLD circuit was in saturation. The RLD monitoring system can concurrently and/or subsequently select and/or provide selection information regarding an optimal RLD circuit to be utilized to most effectively perform the desired function of the RLD in the procedure being performed using the monitoring, mapping and/or recording system.

Abstract: Current interfaces for displaying information about items appearing in videos are obtrusive and counterintuitive. They also rely on annotations, or metadata tags, added by hand to the frames in the video, limiting their ability to display information about items in the videos. In contrast, examples of the systems disclosed here use neural networks to identify items appearing on- and off-screen in response to intuitive user voice queries, touchscreen taps, and/or cursor movements. These systems display information about the on- and off-screen items dynamically and unobtrusively to avoid disrupting the viewing experience.

Abstract: An embodiment method includes: mapping a to-be-transmitted optical channel unit signal of n times a benchmark rate to X first optical channel physical link signals; adding a link sequence indicator overhead to each of the X first optical channel physical link signals, to generate X second optical channel physical link signals; and modulating and sending the X second optical channel physical link signals by using X preset optical modules in a one-to-one manner. A rate of the first optical channel physical link signal is mi times the benchmark rate, n?2, X?2, mi?1, and ? i = 1 X m i = n .

Abstract: An optical cross-connect disclosed herein includes an input-end unit, an optical beam-splitting and switching unit, and an output-end unit. The input-end unit is configured to transmit a set of first light beams to the optical beam-splitting and switching unit. The optical beam-splitting and switching unit is configured to split each light beam in the set of first light beams into second light beams, to obtain a set of second light beams. The optical beam-splitting and switching unit is further configured to: perform optical path deflection on each light beam in the set of second light beams based on a preset optical-path offset parameter set, and transmit, to the output-end unit, the deflected second light beams. The output-end unit is configured to output the set of second light beams.

Abstract: A terminal (1050) includes a light receiver (151) that receives a light signal emitted by an apparatus (1000), the light signal including an identifier (SSID) of at least one base station (470); a receiver (153) that performs a reception process on the received light signal to output reception data; a data analyzer (155) that selects one base station based on the identifier of the at least one base station that is included in the reception data; and a radio device (453) that establishes a wireless connection with the selected base station (470) by using the identifier of the base station (470) and wirelessly communicates with the base station (470).

Abstract: Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; at least one first line buffer for outputting the image into a line-wise image line; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the output from the first line buffer; and a feature map processor for storing the output from the first convolution operator in units of at least one line, and processing so as to output the feature map stored in units of at least one line into a two-dimensional form, wherein at least one convolution operation operates in the form of a pipeline.

Abstract: A vehicle assist device includes a camera that captures the surroundings of a host vehicle at different times, and a controller that processes the plurality of images captured at different times by the camera. The controller extracts a stationary object region, which is a region corresponding to a stationary object, from each of a plurality of images captured at different times, aligns the plurality of images based on the movement amount of the stationary object in the image in the stationary object region, performs super-resolution processing using the plurality of aligned images to generate a super-resolution image that exceeds the resolution of the image captured by the camera, and recognizes a road structure based on the super-resolution image.

Abstract: ADAS includes a processing circuit and a memory which stores instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to receive, from a vehicle that is in motion, a video sequence, generate a position image including at least one object included in the stereo image, generate a second position information associated with the at least one object based on reflected signals received from the vehicle, determine regions each including at least a portion of the at least one object as candidate bounding boxes based on the stereo image and the position image, and selectively adjusting class scores of respective ones of the candidate bounding boxes associated with the at least one object based on whether a respective first position information of the respective ones of the candidate bounding boxes matches the second position information.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first plurality of inputs that identify a plurality of geographical locations and a plurality of infrastructure located at the plurality of geographical locations, classifying each of the plurality of geographical locations in accordance with the first plurality of inputs to obtain a plurality of classes, obtaining a second plurality of inputs that identify costs, revenue, profits, or any combination thereof, associated with the plurality of infrastructure, processing the second plurality of inputs in conjunction with the plurality of classes to identify a first plurality of locations included in the plurality of geographical locations to decommission infrastructure included in the plurality of infrastructure, and presenting the first plurality of locations via a device. Other embodiments are disclosed.

Abstract: Embodiments of this application disclose a board, an optical module, a media access control (MAC) chip, a digital signal processor (DSP), and an information processing method. The board in the embodiments of this application includes a MAC chip, a DSP, and an equalizer. The MAC chip is configured to send first information to the DSP at an optical network unit (ONU) online stage, where the first information includes a first ONU identifier. The DSP is configured to receive the first information, and determine a first reference equalization parameter, where the first reference equalization parameter is related to the first ONU identifier. The DSP is further configured to set an equalization parameter of the equalizer to the first reference equalization parameter.

Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.

Abstract: A medical image processing apparatus includes a recognition unit that detects a region of interest from an acquired medical image and classifies the region of interest; a display control unit that causes emphasis display for emphasizing the region of interest and a classification for the region of interest to be displayed in a screen identical to a screen for displaying an observation target included in the medical image; and an observation image switching unit that switches whether to display consecutive images or to display a still image by using a display device. In a case where consecutive image display of an observation image is switched to still image display by the observation image switching unit, the display control unit makes a change for reducing visual recognizability of at least any of the emphasis display or the classification compared with a case of the consecutive image display of the observation image.

Abstract: Embodiments are generally directed to methods and apparatuses for determining a frontal body orientation. An embodiment of a method for determining a three-dimensional (3D) orientation of frontal body of a player comprises: detecting each of a plurality of players in each of a plurality of frames captured by a plurality of cameras; for each of the plurality of cameras, tracking each of the plurality of players between continuous frames captured by the camera; and associating the plurality of frames captured by the plurality of cameras to generate the 3D orientation of each of the plurality of players.

Abstract: A receiver optical sub-assembly, a combo bi-directional optical sub-assembly, a combo optical module, an optical line terminal, and a passive optical network system, where the receiver optical sub-assembly includes a first transistor-outline can, where a light incident hole is disposed on the first transistor-outline can, and where a first demultiplexer, a first optical receiver, a second optical receiver, and an optical lens combination are packaged in the first transistor-outline can.

Abstract: A technique capable of providing a user with information obtained by OCT imaging in a useful way, particularly for assisting in assessment of an embryo effectively. The image processing method includes acquiring original signal data indicating an intensity distribution of signal light from each position in three-dimensional space including a cultured embryo obtained by optical coherence tomography imaging, generating spherical coordinate data based on the original signal data, the spherical coordinate data indicating a relationship between each position in the three-dimensional space represented using spherical coordinates having an origin set inside the embryo and the intensity of the signal light from the position, and outputting the intensity distribution of the signal light as a two-dimensional map based on the spherical coordinate data using two deflection angles of the spherical coordinates as coordinate axes.

Abstract: A deep learning-based image stitching method and apparatus are provided. The deep learning-based image stitching method includes receiving a plurality of images including a first image and a second image stitched to the first image, extracting a feature map of the plurality of images, generating a plurality of flow maps indicating movement information of pixels of the plurality of images, based on the feature map, generating a plurality of warping images in which the pixels of the plurality of images are moved, by applying the plurality of flow maps to the plurality of images, respectively, generating a plurality of weight maps for blending the plurality of warping images, based on the feature map, and generating a stitch image in which the first image and the second image are stitched, by blending the plurality of warping images based on the plurality of weight maps.

Abstract: An image processing apparatus includes a memory configured to store first region detection information of a first frame; and a processor. The processor is configured to: identify a first pixel region corresponding to the first region detection information from a second frame, perform region growing processing on the first pixel region based on an adjacent pixel region that is adjacent to the first pixel region, obtain second region detection information of the second frame, based on the region growing processing, and perform image processing on the second frame based on the second region detection information.

Abstract: An industrialized system and method for rice grain recognition. An optical image is taken and transmitted to a digital platform, wherein the system segments the optical image and extracts and/or measures appropriate grain features from the image describing different aspects of the grain. The image is processed by the system which includes a selector selecting different machine learning structures, applying the different machine learning structures to the extracted features for rice grain recognition, and selecting the best of the applied machine learning structures by a random sampling process. The selected best of the applied machine learning structures is further optimized by varying an appropriate threshold by a threshold trigger based on a confusion matrix comprising. An active learning structure based on the confusion matrix to the user. The system is retrained based on the feedback parameters of the feedback loop.

Abstract: Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.