Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A discriminator of a training model is trained to discriminate between original training images without artificial subsurface data and modified training images with artificial subsurface data. A generator of the training model is trained to: replace portions of original training images with the artificial subsurface data to form the modified training images, and prevent the discriminator from discriminating between the original training images and the modified training images.

Abstract: A method for recognizing a key time point in a video includes: obtaining at least one video segment by processing each image frame in the video by an image classification model; determining a target video segment in the at least one video segment based on a shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by an image detection model; and based on a distance between the location of the first object and the location of the second object in the image frame satisfying a preset condition, determining a time point of the image frame as the key time point of the video.

Abstract: The present disclosure relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm and an application thereof. Disclosed is an electronic device. The electronic device comprises: a memory in which a first filter for identifying an input image is stored; and a processor for rotating between a plurality of elements included in the memory in which the first filter is stored and a plurality of elements included in the first filter, obtaining at least one second filter by scaling a filter region including at least some of the plurality of elements, and identifying the input image on the basis of a result value obtained by performing convolution on a pixel value included in the input image with each of the first filter and the second filter.

Abstract: A medical device that includes a sensor including a filter array. The sensor is configured to capture a raw image and the filter array is configured to filter the raw image into a frame of raw pixel values including first, second, and third pixel values. The medical device includes non-transitory computer readable medium storing demosaicing instructions that, when executed, causes a processor to exclude the first pixel values from the frame of raw pixel values. The processor generates estimated second pixel values at locations of the excluded first pixel values and the third pixel values on the frame, and generates estimated third pixel values at locations of the excluded first pixel values and the second pixel values on the frame. The processor creates a processed image having a partial-resolution frame from the second pixel values, estimated second pixel values, third pixel values, and estimated third pixel values.

Abstract: The present invention relates to a diagnosis assistance system for assisting diagnosis for a plurality of diseases based on a fundus image, the diagnosis assistance system including: a fundus image obtaining unit configured to obtain a fundus image; a first processing unit configured to, for the fundus image, obtain a first result related to a first finding of a patient using a first neural network model, a second processing unit configured to, for the fundus image, obtain a second result related to a second finding of the patient using a second neural network model, a third processing unit configured to determine, on the basis of the first result and the second result, diagnostic information on the patient, and a diagnostic information output unit configured to provide the determined diagnostic information to a user.

Abstract: A saliency map generation method and an image processing system using the same are provided. The method includes the following steps. An original underwater image is received. A blurring processing is performed on the original underwater image to generate a defocus map. The defocus map is input to an auxiliary convolutional network model to obtain multiple feature maps of the defocus map. The original underwater image and the feature maps are input to a main convolutional network model to generate a saliency map of the original underwater image.

Abstract: The present disclosure relates to an apparatus and method for optimizing inverse tone mapping on basis of a single image to reconstruct into a high quality High Dynamic Range (HDR) image using a Low Dynamic Range (LDR) image and a recording medium for performing the method, and includes inputting a LDR image to Inverse Tone Mapping (ITM) operator, inputting a result value by the ITM operator to Tone Mapping (TM) operator, comparing a result value by the TM operator with a threshold for HDR luminance determination, and selecting the result value by the TM operator which is smaller than the threshold as an HDR image.

Abstract: A system for adapting an image noise removal model based on a device processing capability receives, from a computing device, a request to adapt an image noise removal module for the computing device. The system compares a processing capability of the computing device with a threshold processing capability. The system determines whether the processing capability is greater or smaller than the threshold processing capability. In response to determining that the processing capability is greater than the threshold processing capability, the system sends a version of the image noise removal module that is adapted for computing devices with processing capabilities less than the threshold processing capability, where the version of the image noise removal module is adapted to have a number of neural network layers less than a threshold number of neural network layers.

Abstract: An information processing apparatus includes circuitry configured to: acquire, as a first image, a read image of an image formed on a medium; generate a second image based on document data; detect reference points from the second image; compare the first image with the second image to calculate an amount of positional deviation of the medium for each of the reference points; detect, based on the amount of positional deviation, a malfunction of a conveying device that conveys the medium; cause an image forming apparatus to print a correction chart for correction of conveyance of the medium, in response to detection of the malfunction of the conveying device; calculate, from a read image of the correction chart, an amount of fluctuation in conveyance of the medium performed by the conveying device; and calculate a correction value for the calculated amount of fluctuation in conveyance of the medium.

Abstract: A neural network system for generating a value estimation is provided. A computing device analyzes one or more digital media streams. A computing device identifies one or more parameters of the object in one or more digital media streams. A computing device collects information of an object in one or more digital media streams. A computing device generates a precited value estimation of the object via invocation of additional multi-level input collection in a distributed cognitive system. A computing device communicates a report associated with the predicted value estimation of the object to a user of a computing device.

Abstract: A surveying apparatus comprising a controller (CPU), the controller (CPU) being configured to: receive an image stream representing a video sequence; determine a camera pose for a second image in the image stream relative a first image in the image stream; match the first image with the second image, based on the cam-era pose; and generate a three dimensional model based on the image match.

Abstract: A system and method apply an input noisy ultrasonic test (UT) scan image to an input layer of a convolutional neural network, generate a feature map using a convolutional layer, pool the feature map using a pooling layer, apply the pooled feature map to a fully connected layer, generate a de-noised UT scan image, and output the de-noised UT scan image from an output layer.

Abstract: An information processing apparatus includes a purpose setting unit configured to set an examination purpose of radiography, a selection setting unit configured to select processing of a radiation image corresponding to the examination purpose and set the processing in an editing area of a display unit, and a generation unit configured to generate a processing procedure corresponding to the examination purpose based on the set processing.

Abstract: A filter process is performed on point cloud data using a representative value of the point cloud data for each local region obtained by dividing a three-dimensional space. A two-dimensional plane image on which the point cloud data subjected to the filter process is projected is encoded, and a bitstream is generated. The present disclosure can be applied to, for example, an information processing apparatus, an image processing apparatus, electronic equipment, an information processing method, a program, or the like.

Abstract: Systems and methods are disclosed for controlling image annotation. One method includes acquiring a digital representation of image data and generating a set of image annotations for the digital representation of the image data. The method also may include determining an association between members of the set of image annotations and generating one or more groups of members based on the association. A representative annotation from the one or more groups may also be determined, presented for selection, and the selection may be recorded in memory.

Abstract: In an embodiment, a method includes receiving a digital image; generating, by at least one processor, a resulting digital image by processing the digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upsampling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the digital image and employed between the downsampling stage and the upsampling stage; and outputting, by the at least one processor, the resulting digital image to an output device.

Abstract: A drug screening system and methods of using the same are disclosed. The drug screening system identifies and highlights drugs or other contraband substances enclosed within mailpieces based on score imagery computed from selected wavelengths based on chemical signatures. A wide-field, multispectral short-wave infrared imaging system allows for dissolved, concealed drug screening of mailpieces that improve inspection efficiency and accuracy.

Abstract: Systems and methods for correcting image distortion in accordance with embodiments of the invention are illustrated. One embodiment includes a method for correcting distorted images. The method includes steps for receiving a set of term coefficients associated with a lens that captured a set of one or more distorted images, and mapping each of the set of distorted images to a new target image based on the set of term coefficients.

Abstract: Provided is a mixed reality-based experience simulator including an imaging part including first and second cameras installed on an experience object to capture images of different viewpoints from the experience object, a display part worn by a participant and configured to display a mixed reality image of the experience object to the participant, and an image processing unit configured to provide the mixed reality image to the display part and configured to generate the mixed reality image such that images captured by the first and second cameras are included in the mixed reality image. The mixed reality-based experience simulator according to the present invention generates a mixed reality image on the basis of images captured by a plurality of cameras installed on an experience object and provides the generated mixed reality image to a participant so that a more realistic experience is provided.