Abstract: Techniques for performing viewing verification using a plurality of classifiers are disclosed. Images of an individual may be obtained concurrently with an electronic display presenting one or more images. Image classifiers for facial and head pose analysis may be obtained. The images of the individual may be analyzed to identify a face of the individual in one of the plurality of images. A viewing verification metric may be calculated using the image classifiers and a verified viewing duration of the screen images by the individual may be calculated based on the plurality of images and the analyzing. Viewing verification can involve determining whether the individual is in front of the screen, facing the screen, and gazing at the screen. A viewing verification metric can be generated in order to determine a level of interest of the individual in particular media and images.

Abstract: A method may include obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining a second set of projection data based on the first set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing a second image based on the second set of projection data; and training a first neural network model based on the first image and the second image. In some embodiments, the trained first neural network model may be configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level and corresponding to a higher dose level than the third image.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for electronic query engine for an image processing model database. The system is configured is configured for constructing a model abstraction layer for machine-learning neural-network based image processing models configured for selection, mutation and construction of the image processing models. Here, the system is configured to receive and process a user input query comprising a plurality of discrete input language elements, wherein each of the plurality of discrete input language elements comprises a character string. The system is also configured to construct a second image processing model by mutating a first image processing model, in accordance with the discrete input language elements.

Abstract: An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a first frame of a global plane for at least a portion of a marker grid in a space. The tracking system is configured to receive a first coordinate in the global plane for a first corner of a marker grid, to determine a second coordinate in the global plane for the first marker on the marker grid, and to determine a third coordinate in the global plane where the second marker on the marker grid. The tracking system is further configured to determine a first pixel location for the first marker, to determine a second pixel location for the second marker, and to generate a homography based on the second coordinate for the first marker, the third coordinate for the second marker, the first pixel location, and the second pixel location.

Abstract: An electronic device and a control method therefor are disclosed. A method for controlling an electronic device according to the present invention comprises the steps of: receiving a current frame; determining a region, within the current frame, where there is a movement, on the basis of a prior frame and the current frame; inputting the current frame into an artificial intelligence learning model on the basis of the region where there is the movement, to obtain information relating to at least one object included in the current frame; and determining the object included in the region where there is the movement, by using the obtained information relating to the at least one object. Therefore, electronic device can rapidly determine an object included in a frame configuring a captured image.

Abstract: A method for image processing includes the following. Edge pixel points in the image to be processed are determined by performing edge detection on the image to be processed. The number of the edge pixel points in the image to be processed is determined as a first pixel number. Determine that a moire pattern exists in the image to be processed when the first pixel number is larger than a first number threshold.

Abstract: Remote computing analysis for cognitive state data metrics is performed. Cognitive state data from a plurality of people is collected as they interact with a rendering. The cognitive state data includes video facial data collected on one or more local devices from the plurality of people. Information is uploaded to a remote server. The information includes the cognitive state data. A facial expression metric based on a plurality of image classifiers is calculated for each individual within the plurality of people. Cognitive state information is generated for each individual, based on the facial expression metric for each individual. The cognitive state information for each individual within the plurality of people who interacted with the rendering is aggregated. The aggregation is based on the facial expression metric for each individual. The cognitive state information that was aggregated is displayed on at least one of the one or more local devices.

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: An imaging system and method compensate lens distortion for an image. The system identifies lens distortion among one or more distortions, and extracts a Point Spread Function (PSF) for the lens distortion. Additionally, the system extracts a total PSF associated with total distortion in the input image and separates a PSF for the lens distortion from the total PSF. The system updates the pre-estimated kernel with the PSF of the lens distortion when a rank of the PSF matches with the predefined rank value, and one or more difference values determined between one or more singular values of the PSF and one or more singular values of the pre-estimated kernel is within a pre-defined threshold limit. Thus, quality of each output image obtained from the system may be maintained irrespective of error introduced by wear and tear on the lens of the system over a period of time.

Abstract: The present invention relates to an image processing apparatus and a mobile robot including the same. The image processing apparatus according to an embodiment of the present invention includes an image acquisition unit for obtaining an image and a processor for performing signal processing on the image from the image acquisition unit, and the processor is configured to group super pixels in the image on the basis of colors or luminances of the image, calculate representative values of the super pixels and perform segmentation on the basis of the representative values of the super pixels. Accordingly, image segmentation can be performed rapidly and accurately.

Abstract: A method, an apparatus, a device and a computer-readable storage medium for recognizing aerial handwriting are provided. The method may include detecting a palm region of a user in a two-dimensional grayscale image; detecting a fingertip in the two-dimensional gray-scale image based on the palm area; determining a spatial trajectory of the fingertip based on a set of two-dimensional gray-scale images, the set of two-dimensional gray-scale images including the two-dimensional gray-scale image; and recognizing handwritten content of the user based on the spatial trajectory. A two-dimensional gray-scale image is used to recognize and track the spatial trajectory of the fingertip, which may speed up aerial handwriting recognition, and has low processing performance requirements for the device, while also ensuring high accuracy.

Abstract: Embodiments of the present disclosure provide a video processing method, a video processing device and a related non-transitory computer readable storage medium. The method includes the following. Frame sequence data of a low-resolution video to be converted is obtained. Pixel tensors of each frame in the frame sequence data are inputted into a pre-trained neural network model to obtain high-resolution video frame sequence data corresponding to the video to be converted output by the neural network model. The neural network model obtains the high-resolution video frame sequence data based on high-order pixel information of each frame in the frame sequence data.

Abstract: A non-transitory computer-readable recording medium stores therein a calculation program that causes a computer to execute a process including setting, with regard to each of a plurality of frame images included in moving image data on a captured subject, a time interval region having a predetermined time width in time sequence for detection map information relating a detection result of each of types of feature values to a time sequence of the frame images, acquiring, with regard to each of a plurality of positions in the time interval region, a distribution state of the detection result in the time interval region in a case where a position of the set time interval region is changed in the time sequence in the detection map information, and calculating a distributed representation indicating a set of points obtained by projecting the distribution state in each of the positions onto a low-dimensional space.

Abstract: Implementations generally relate to determining the position of a tool tip of a surgical instrument and the orientation of the surgical instrument. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting a surgical tool in the at least one image frame. The method further includes determining a position of a tip of the surgical tool. The method further includes determining an orientation of the surgical tool.

Abstract: A computer-implemented method of training a depth uncertainty estimator comprises receiving, at a training computer system, a set of training examples, each training example comprising (i) a stereo image pair and (ii) an estimated disparity map computed from at least one image of the stereo image pair by a depth estimator. The training computer system executes a training process to learn one or more uncertainty estimation parameters of a perturbation function, the uncertainty estimation parameters for estimating uncertainty in disparity maps computed by the depth estimator. The training process is performed by sampling a likelihood function based on the training examples and the perturbation function, thereby obtaining a set of sampled values for learning the one or more uncertainty estimation parameters.

Abstract: Systems and methods for converting low resolution images into high resolution images are provided and can include (1) processing a compressed version of images with a first set of heuristics and rules of an artificial intelligence model to produce an intermediate version of the images with an intermediate resolution that is greater than the compressed resolution by first enhancing the images and subsequently upscaling the images as enhanced from the compressed resolution to the intermediate resolution and (2) processing the intermediate version of the images with a second set of heuristics and rules of the artificial intelligence model to produce a final version of the images with a final resolution that is greater than the initial resolution and the intermediate resolution by first upscaling the images from the intermediate resolution to the final resolution and subsequently enhancing the images as upscaled to produce the final version of the images.

Abstract: Methods, systems, and devices for generating a persistent world-space ground (or floor) segmentation map (or “texture”) for use in augmented or virtual reality 3D experiences.

Abstract: Techniques for synthesizing intermediary frames for long exposure images are disclosed. Video stream data, comprising a plurality of arriving frames, is received. An indication that the video stream data should be processed into a long exposure image that incorporates one or more synthesized frames is received. A set of arriving frames is used to generate at least one synthesized frame. At least one received frame is blended with the at least one generated synthesized frame to form a long exposure image. The long exposure image is provided as output.

Abstract: An image processing device 10 includes: an extraction means 11 which extracts sub patches from a given plurality of input images combined pose probabilities; a filtering means 12 which filters the extracted sub patches and the pose probabilities; and a combining means 13 which combines the filtered sub patches and the filtered pose probabilities.

Abstract: An image generation means 81 generates an image using a generator. A discrimination means 82 discriminates whether an object image includes a feature of a target image, using a discriminator. A first update means 83 updates the generator so as to minimize a first error representing a degree of divergence between a result of discriminating a generated image using the discriminator and a correct answer label associated with the generated image, the generated image being the image generated using the generator. A second update means 84 updates the discriminator so as to minimize a second error representing a degree of divergence between each of respective results of discriminating the generated image, a first actual image including the feature of the target image, and a second actual image not including the feature of the target image using the discriminator and a correct answer label associated with a corresponding image.