Abstract: One example provides a computing device including a logic machine and a storage machine holding instructions executable by the logic machine to implement a depth image processing pipeline including a neural network, the neural network including an edge detecting layer. The neural network is configured to receive input of an active brightness image and receive input of one or more of real data or imaginary data of a complex depth image, the complex depth image corresponding to the active brightness image. The neural network is further configured to, at the edge detecting layer, apply one or more convolutional processes to the active brightness image to identify one or more edge pixels in the active brightness image, and at a second layer, denoise one or more of the real data or the imaginary data of the complex depth image based on the one or more edge pixels identified.

Abstract: A computer-implemented method, comprising: obtaining motion indicators for a plurality of samples of a video stream; obtaining an anomaly state for each of a plurality of time windows of the video stream, each of the time windows spanning a subset of the samples, by (i) obtaining estimated statistical parameters for the given time window based on measured statistical parameters characterizing the motion indicators for the samples in at least one time window of the video stream that precedes the given time window and (ii) determining the anomaly state for the given time window based on the plurality of motion indicators obtained for the samples in the given time window and the estimated statistical parameters; and processing the video stream based on the anomaly state for various ones of the time windows.

Abstract: The invention relates to a method for verifying the positioning of a fibrous preform in a blade, the blade having been obtained by injecting a resin into a mould having the shape of a blade and in which a preform has been placed, the blade extending in an orthonormal blade frame of reference X, Y, Z, the blade comprising a blade root extending longitudinally along an axis X, a vane extending from the blade root along an axis Z, the blade having a thickness defined along an axis Y, the preform comprising glass tracers positioned at the surface of the preform, the centre of the tracers defining a neutral axis located at a height along the axis Z in the direction defined by the axis X, the method comprising the following steps: the acquisition (E31) of tomographic 2D projections of the blade using an imaging system comprising an X-ray source, each projection being acquired at a given orientation of the X-ray source with respect to the blade; the combining (E32, E32a, E32b) of the 2D projections in the direction

Abstract: Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate refined depth maps of digital images utilizing digital segmentation masks. In particular, in one or more embodiments, the disclosed systems generate a depth map for a digital image utilizing a depth estimation machine learning model, determine a digital segmentation mask for the digital image, and generate a refined depth map from the depth map and the digital segmentation mask utilizing a depth refinement machine learning model. In some embodiments, the disclosed systems generate first and second intermediate depth maps using the digital segmentation mask and an inverse digital segmentation mask and merger the first and second intermediate depth maps to generate the refined depth map.

Abstract: A system includes a processor and a memory storing software code. The processor executes the software code to receive a first rendered image having first color and auxiliary buffers, receive a second rendered image having second color and auxiliary buffers, provide a third auxiliary buffer for an intermediate image to be interpolated, and identify one or more context features from each of the color buffers and the auxiliary buffers. The software code further interpolates, based on the context features, the intermediate image to provide an interpolated image, determines, based on the one or more context features from each of the first, second, and third auxiliary buffers, a rendering mask for producing one or more rendered sub-portions of the intermediate image, partially renders, using the rendering mask, the intermediate image to provide a partially rendered image, and blends the interpolated image and the partially rendered image to provide a composite image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: A system and method for extracting full text from source files of any size in such a manner that all text can be rapidly and accurately searched via search engine while maintaining satisfactory system performance. A software tool that integrates with file shares or an enterprise content management system with the ability to merge and serialize the results of the extracted text via surrogate index files. The software tool is configured to handle text from large files in order to extract, store and access the file such that all of the text is searchable, regardless of the size of the original document in a reasonable amount of time. The Analytics Engine will omit this field during search and handle this field separately from other fields.

Abstract: A computer-implemented method for civil engineering is described that includes obtaining a watershed segmentation of a terrain. The watershed segmentation includes basins. The method further includes merging first basins of the watershed segmentation that each verify a smallness criterion, each with a second basin downstream to the first basin.

Abstract: A method for estimating biometric landmark dimensional measurements of a human eye includes, in a possible embodiment, receiving one or more images of the human eye via a host computer. In response to receiving the one or more images, the method includes generating a preliminary set of landmark point locations in the one or more images via the host computer using a deep-learning algorithm, and then refining the preliminary set of landmark point locations using a post-hoc processing routine of the host computer to thereby generate a final set of estimated landmark point locations. Additionally, the biometric landmark dimensional measurements are automatically generated via the host computer using the final set of estimated landmark point locations. A data set is then output that is inclusive of the set of estimated landmark point locations. A host computer that executes instructions from memory to perform the method.

Abstract: A computer vision system including: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: determine a semantic multi-scale context feature and an instance multi-scale context feature of an input scene; generate a joint attention map based on the semantic multi-scale context feature and the instance multi-scale context feature; refine the semantic multi-scale context feature and instance multi-scale context feature based on the joint attention map; and generate a panoptic segmentation image based on the refined semantic multi-scale context feature and the refined instance multi-scale context feature.

Abstract: Various embodiments classify one or more portions of an image based on deriving an “intrinsic” modality. Such intrinsic modality acts as a substitute to a “text” modality in a multi-modal network. A text modality in image processing is typically a natural language text that describes one or more portions of an image. However, explicit natural language text may not be available across one or more domains for training a multi-modal network. Accordingly, various embodiments described herein generate an intrinsic modality, which is also a description of one or more portions of an image, except that such description is not an explicit natural language description, but rather a machine learning model representation. Some embodiments additionally leverage a visual modality obtained from a vision-only model or branch, which may learn domain characteristics that are not present in the multi-modal network.

Abstract: A method for analyzing droplets based on images: obtaining a working dye channel image and a reference dye channel image corresponding to a droplet system (S1); separately performing a preset image processing operation on the working dye channel image and the reference dye channel image (S2); using the working dye channel image or the reference dye channel image as a target image (S3), and determining a region in which each droplet is located from within the target image (S4); and on the basis of the determined region in which each droplet is located, analyzing the droplet system (S5). The method can effectively improve the accuracy of droplet analysis. Further provided are a computer device and a storage medium.

Abstract: Described here are systems and methods for quantifying vessel features in non-contrast microvasculature images obtained with an ultrasound imaging system. Vessel features that are quantified include, but are not limited to, vessel structure features (e.g., number of vessels, vessel density, number of branch points), vessel diameter, and vessel tortuosity. Morphological filtering is used to generate vessel segments from which the quantitative vessel features can be reliably quantified.

Abstract: A method, system and computer program product for divided processing in providing object detection focus is disclosed. The system includes a first video analytics determinator configured to provide, by infrequent employment thereof within the system, a partial object detection focus in first information that informs as to where moving objects are likely and unlikely to appear within video frames in respect of video. The system also includes a second video analytics determinator configured to: informatively employ the first information to determine a refined object detection focus; and provide the refined tracking and/or object detection focus in second information.

Abstract: A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.

Abstract: A system for manufacturing an orthodontic appliance. A scanner for creating a numerical three-dimensional initial reference model of at least a part of a patient's arch including one or more teeth. An image acquisition device for acquiring at least one two-dimensional updated image of the arch. The image acquisition device is positioned and oriented in space, relative to the teeth, under actual acquisition conditions. A computer for creating a numerical three-dimensional objective reference model, corresponding to a desired positioning, at a time point of a treatment, of the teeth, and deforming the initial reference model by moving one or more teeth of the initial reference model, to obtain an updated reference model corresponding to the positioning of the teeth on the two-dimensional updated image. A device for manufacturing an orthodontic appliance, based on a comparison, executed by the computer, between the updated reference model and the objective reference model.

Abstract: A method is disclosed. The method may include receiving an image or video; extracting a plurality of features from the image or video; executing a neural network using the plurality of features to obtain a performance score for the image or video, the neural network comprising an input layer, a plurality of intermediate layers subsequent to the input layer, and a regression layer or a classification layer; extracting values from one or more signals between an intermediate layer and the regression layer or the classification layer; for each of the plurality of features, calculating, based on at least one of the one or more values, an impact score indicating an impact the feature had on the performance score; and generating, based on one or more impact scores for the plurality of features, indications indicating an impact different features of the image or video had on the performance score.

Abstract: A method for context based separation between vehicle pixels and background pixels, which may include receiving a first image and a second image, the first image and the second images are of temporarily adjacent to each other and capture a same vehicle and background content. The method may also include obtaining a first bounding box that surrounds at least a part of the vehicle within the first image; wherein the first bounding has a first width and a first height; obtaining a second bounding box that surrounds at least the part of the vehicle within the second image; wherein the second bounding box has a second width and a second height; obtaining a mapping between pairs of initially matched pixels, wherein each pair comprises a first pixel and a second pixel that correspond to same entity portion, wherein the entity portion is a portion of the vehicle or of the background content.

Abstract: Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides an HILN classification (hemolysis, icterus, lipemia, normal) of the specimen along with a basis for that determination. The method includes assigning a hash code to each training image of a sample specimen used in the characterization training process. In response to an HILN determination for a test specimen, the method can retrieve via the hash code one or more of the closest matching training images upon which the HILN classification is based. The one or more of the closest matching training images can be displayed alongside of the one or more images of the test specimen. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A device and a corresponding system are disclosed. The device may comprise a display. Additionally, the device and/or system may be operable to detect stale images. Further, the stale images may be detected in devices and/or systems where incoming images—such as from an imager—are received at a frame rate different than that of the display. A controller may detect the stale images by assigning hash values to images of each frame displayed by the display; storing novel hash values in a memory; and maintaining a counter. The counter may be maintained such that assigning a repeat hash value increments the counter by one and assigning novel hash values resets the counter to zero. Further, the stale images may be determined based, at least in part, on the counter value.

Abstract: The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby g

Abstract: A multi-task text infilling system receives a digital image and identifies a region of interest of the image that contains original text. The system uses a machine learning model to determine, in parallel: a foreground image that includes the original text; a background image that omits the original text; and a binary mask that distinguishes foreground pixels from background pixels, The system receives a target mask that contains replacement text. The system then applies the target mask to blend the background image with the foreground layer image and yield a modified digital image that includes the replacement text and omits the original text.

Abstract: Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.

Abstract: According an embodiment, a computing device can: identify, in a chat view associated with a video chat session, a first authorized participant and a second authorized participant of the video chat session; render, in the chat view, first visual data indicative of the first authorized participant and second visual data indicative of the second authorized participant based at least in part on identification of the first authorized participant and the second authorized participant, respectively; define, in the chat view, a chat zone indicative of a reference location of the first authorized participant; determine that the first authorized participant moved outside the chat zone; and/or conceal, in the chat view, the first visual data indicative of the first authorized participant based at least in part on determination that the first authorized participant moved outside the chat zone.

Abstract: In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Abstract: A video summary device may determine, based on video data, a plurality of events that occurred during a period of time. The video summary device may determine first measures of relevance of a plurality of portions of the period of time. A first measure of relevance of a portion of time may be determined based on one or more events of the plurality of events. The video summary device may determine second measures of relevance of a plurality of ranges of time between different portions of time of the plurality of portions of the period of time. The second measures of relevance is determined based on the first measures of relevance determined for the different portions of time. The video summary device may determine particular frames of the video data, as a video summary of the video data, based on the second measures of relevance.

Abstract: Techniques for mapping size information associated with a client to target brands, garments, sizes, shapes, and styles for which there is no standardized correlation. The size information associated with a client may be generated by modeling client garments, accessing computer aided drawing (CAD) files associated with client garments, or by analyzing a history of garment purchases associated with the client. Information for target garments may be generated in a similar fashion. A system may then create a standardized scale with a set of sizes for a target, and map a client base size to that standardized size scale. Similar matching and mapping may also be done with shape and style considerations. A recommendation based on the mapping may then be communicated to the client.

Abstract: An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Abstract: An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.

Abstract: This application relates to the field of digital image processing technologies, and discloses an image-text fusion method and apparatus, and an electronic device, to minimize blockage of a saliency feature in an image by a text when the text is laid out in the image, and obtain a higher visual balance degree after the text is laid out in the first image, thereby achieving a better layout effect. According to the method of this application, first, a plurality of candidate text templates and layout positions of a plurality of corresponding texts in an image can be determined, so that a text laid out in the image does not block a visually salient object having a greater feature value, such as a human face or a building.

Abstract: An example of an apparatus to infer joint rotations and positions is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a first joint position and a second joint position of an input skeleton. In addition, the apparatus includes a memory storage unit to store the raw data. Furthermore, the apparatus includes a pre-processing engine to generate normalized data from the raw data. The normalized data is to be stored in the memory storage unit. Also, the apparatus includes an inverse kinematics engine to apply a neural network to infer a joint rotation from the normalized data. The neural network is to use historical data. Training data used to train the neural network includes positional noise.

Abstract: The present invention relates to the field of three-dimensional images, and provides a method for three-dimensional reconstruction of a target object, comprising the following steps: S10: calculating and acquiring a visual angle boundary of the target object; S20: acquiring image information of the target object under multiple visual angles in the visual angle boundary, the image information including initial image information and/or fused image information; S30: finishing three-dimensional reconstruction of the target object according to the acquired image information. By adopting the method for three-dimensional reconstruction of the target object in the present invention, three-dimensional reconstruction of the three-dimensional target at different moments can be carried out in real time, no other links are carried out during the operation, the acquired image information is not lost, real information is directly reproduced, and three-dimensional reconstruction of the real target object is realized.

Abstract: Methods and systems are described for estimating an optimal pupil binarization threshold using adaptive binarization thresholding. The disclosed methods and systems are designed to determine a pupil binarization threshold from a histogram of an eye image. An eye image is obtained and an eye image histogram is computed from the eye image. A pupil region and an iris region are identified in the eye image histogram and the second derivative of the eye image histogram is computed. The pupil binarization threshold is determined based on the second derivative of the eye image histogram, the identified pupil region and the identified iris region and then used to generate a binarized eye image. A pupil contour may be determined from the binarized eye image. The disclosed system may help to overcome challenges associated with robust and precise detection of a pupil contour, for example, in images of varying quality.

Abstract: A method and system determine network based access to restricted systems. The method includes receiving a request for a permission access status of a party seeking access to one of the restricted systems. A database of periodically updated lists of entities is accessed. A name of the party is extracted from the request. A determination is made whether the name does not match one of the entities. The name is decomposed into parts if the name not matching one of the entities. A determination is made whether any of the parts of the name matches one of the entities. A denial of access status is forwarded from the computer server to an external computing device if any of the parts of the name matches one of the entities.

Abstract: Systems and methods for multi-task joint training of a neural network including an encoder module and a multi-headed attention mechanism are provided. In one aspect, the system includes a processor configured to receive input data including a first set of labels and a second set of labels. Using the encoder module, features are extracted from the input data. Using a multi-headed attention mechanism, training loss metrics are computed. A first training loss metric is computed using the extracted features and the first set of labels, and a second training loss metric is computed using the extracted features and the second set of labels. A first mask is applied to filter the first training loss metric, and a second mask is applied to filter the second training loss metric. A final training loss metric is computed based on the filtered first and second training loss metrics.

Abstract: In an image processing method, a detection image and a marked image are obtained. An image segmentation model is applied to segment a first segmented image from the detection image. The first segmented image is corrected according to the marked image to obtain a second segmented image. A size of the second segmented image is adjusted to obtain an adjusted segmented image. The adjusted segmented image is used as a standard segmented image of the detection image. The method improves accuracy of image segmentation and recognition.

Abstract: This disclosure describes one or more implementations of an alpha matting system that utilizes a deep learning model to generate alpha mattes for digital images utilizing an alpha-range classifier function. More specifically, in various implementations, the alpha matting system builds and utilizes an object mask neural network having a decoder that includes an alpha-range classifier to determine classification probabilities for pixels of a digital image with respect to multiple alpha-range classifications. In addition, the alpha matting system can utilize a refinement model to generate the alpha matte from the pixel classification probabilities with respect to the multiple alpha-range classifications.

Abstract: A system and method are capable of ensuring that one or more text strings will be able to be fully rendered in a target area of a user interface or a target area of a graphics file. The system and method determine the number of pixels of first and second reference text that fit in the target area in the horizontal direction and the vertical direction, respectively, determine the number of pixels of string text in the horizontal direction and the vertical direction, and compare the number of pixels in the horizontal direction of the first reference text and the vertical direction of the second reference text respectively to the number of pixels in the horizontal direction and the vertical direction of the text string that is desired to be rendered in the target area to determine whether the text string will fit in the target area.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes include: obtaining one or more design criteria, a model of an object for which a physical structure is to be manufactured, and a set of eigenmodes from a modal analysis of the model of the object; extracting a proper subset of non-zero eigenmodes from the set of eigenmodes, wherein the proper subset of non-zero eigenmodes include at least three lowest valued, non-zero eigenmodes; combining data of the proper subset of non-zero eigenmodes to form a strain energy field for the model of the object; iteratively modifying a generatively designed shape of the model of the object using the strain energy field to drive changes to the generatively designed shape of the model; and providing the generatively designed shape of the model of the object.

Abstract: A system identifying anomalies in an image of an object is first trained using first sets of images corresponding to first anomaly types for the object. A model of the object is formed in a latent space. A label for each anomalous image is used to calculate vectors containing means and standard deviations for each first anomaly types. The means and standard deviations are used to calculate a log-likelihood loss for each first anomaly type. The system is retrained using second sets of images corresponding to second anomaly types for the object. The vectors are supplemented using labels for each second anomaly types. A statistically sufficient sample of information in the means and standard deviations vectors is supplied to the latent space. A log-likelihood loss for each of the first and second anomaly types is calculated based on their respective mean and standard deviation.

Abstract: A max-flow/min-cut solution algorithm for early terminating a push-relabel algorithm is provided. The max-flow/min-cut solution algorithm is used for an application that does not require an exact maximum flow, and includes: defining an early termination condition of the push-relabel algorithm by a separation condition and a stable condition; determining that the separation condition is satisfied if there is no source node s, s?S, in the set T at any time in an operation process of the push-relabel algorithm; determining that the stable condition is satisfied if there is no active node in the set T; and terminating the push-relabel algorithm if both the separation condition and the stability condition are satisfied. The early termination technique is proposed to greatly reduce redundant computations and ensure that the algorithm terminates correctly in all cases.

Abstract: A method for spatial resolution enhanced imaging is provided. The method includes determining a target region of a subject being imaged; estimating an achievable spatial resolution; selecting an increased spatial resolution that is greater than a predetermined spatial resolution and equal to or less than the achievable spatial resolution; and acquiring image data of the target region with the increased spatial resolution. A method for enhancing the contrast between the region of interest and other tissue region in the image data acquired with the increased spatial resolution and other tissue region in image data acquired with the predetermined spatial resolution due to the intrinsic partial volume effect is also provided.

Abstract: An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Abstract: A method and apparatus are provided for detecting respective potential presences of respective different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells (HEp cells), wherein the fluorescence pattern types include different antinuclear antibody fluorescence pattern types. A method is also provided for detecting potential presences of different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells by means of digital image processing, as well as a computing unit, a data network device, a computer program product and a data carrier signal therefor.

Abstract: A method includes receiving, from a camera, one or more frames of image data of a scene comprising a background and one or more three-dimensional objects, wherein each frame comprises a raster of pixels of image data; detecting layer information of the scene, wherein the layer information is associated with a depth-based distribution of the pixels in the one or more frames; and determining a multi-layer model for the scene, the multi-layer model comprising a plurality of discrete layers comprising first and second discrete layers, wherein each discrete layer is associated with a unique depth value relative to the camera. The method further includes mapping the pixels to the layers of the plurality of discrete layers; rendering the pixels as a first image of the scene as viewed from a first perspective; and rendering the pixels as a second image of the scene as viewed from a second perspective.

Abstract: A device including a field programmable gate array is configured to set a coherent clock time frame for data captured. The gate array creates metadata that is operable for capturing a state of each of a plurality of faces of an information space. A geodesic lens comprises a geodesic equation that creates metadata from the captured data. An anamorphic display engine is used to transduce a plurality of visual dimensions of related foreground and background objects and render the objects relative motion in time.

Abstract: A system, method, and/or computer storage medium encoded with a computer program are disclosed for providing an interactive virtual event session for respective pluralities of devices substantially in real-time. A data communication session is provided, to which a plurality of computing devices operated by participants of an event session connect. Respective audio-video feeds including content captured by respective cameras and microphones configured with the plurality of computing devices are received. Moreover, at least some of the content in at least two of the respective audio-video feeds is adjusted, including by executing editing processes. Furthermore, a virtual setting for the event session is provided to each of the plurality of computing devices, and modified to include at least the adjusted content. Changing views of the adjusted content and the virtual setting is provided to each of the plurality of computing devices, using artificial intelligence.

Abstract: A method, computer program, and computer system are provided for image segmentation. Image data, such as biological image data, is received. One or more objects associated with the received image data is detected. One or more regions of interest are determined within the receive image data corresponding to one or more segments based on the detected objects.