Abstract: A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; reconstructing, based on the data, a first attribute value of a first duplicate point from among a plurality of duplicate points corresponding to a single geometry position; obtaining at least one prediction residual corresponding to at least one remaining attribute value of at least one remaining duplicate point from among the plurality of duplicate points; reconstructing the at least one remaining attribute value based on the reconstructed first attribute and the at least one prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first attribute value and the reconstructed at least one remaining attribute value.

Abstract: Video analytics are used to evaluate interactions between humans and animals and identify possible occurrences of animal abuse in an objective manner. The video analytics system processes successive video frames to identify objects of interest (e.g., humans, animals, tools/weapons, etc.), creates mathematical models of such objects (e.g., essentially stick-figure models), analyzes movements of such objects (e.g., the speed and/or directional motion of an object or portion of an object such as an arm or leg), determines mathematically and objectively whether or not the analyzed movements meet predetermined criteria for possible abuse (e.g., wherein the predetermined criteria can be mathematical models defining ethical and unethical movements), and outputs relevant information via a user interface (e.g., a list of possible abuse instances identifying the time and probability of possible abuse, from which the user can select an instance in order to view the corresponding video for human analysis).

Abstract: Systems, methods and devices for performing analysis of surfaces, external and internal, including public works. The invention employs analytical software that provides results independent of the limitations of the operator.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a depth image, comprising obtaining data representing a first image generated by a first sensor and a second image generated by a second sensor, wherein each of the first and second images includes a plurality of pixels; determining, for each pixel of the plurality of pixels included in the first image, whether the pixel is a boundary pixel associated with a boundary of an object that is represented in the first image; determining, from a plurality of candidate penalty values and for each pixel in the first image, an optimized penalty value for the pixel; generating an optimized cost function for the first image based on the optimized penalty values for the plurality of pixels; and generating a depth image for the first image based on the optimized cost function.

Abstract: A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

Abstract: Image processing and analysis technique includes using a computer apparatus to assess a patient's computed tomography (CT) or magnetic resonance images for pathology and then automatically generate a prescription based at least in part on that assessment.

Abstract: There is provided with an image identification apparatus. An extraction unit extracts a feature value of an image from image data using a Neural Network (NN). A processing unit identifies the image based on the feature value extracted by the extraction unit. The NN comprises a plurality of calculation layers connected hierarchically. The NN includes a plurality of sub-neural networks for performing processing of calculation layers after a specific calculation layer. Mutually different data from an output of the specific calculation layer are respectively inputted to the plurality of sub-neural networks.

Abstract: An image processing apparatus is provided that includes: an obtaining unit configured to obtain a first medical image of a subject; and an image quality improving unit configured to generate a second medical image with image quality higher than image quality of different regions including a first region and a second region that is different from the first region in the obtained first image, using the obtained first image as input data that is input into a learned model.

Abstract: A document capture server receives a document image from a document capture client and processes the image into an electronic document containing textual content. During capture, the document capture server determines a graphical layout of the document, extracts keywords from the document, classifies the document accordingly, and calls an artificial intelligence (AI) platform to gain insights on the textual content. The AI platform analyzes the textual content and returns additional, insightful data such as a sentiment of the textual content. The document capture server can validate the additional data, integrate the additional data in a process or workflow, and/or provide the textual content and the additional data to a content repository or a computing facility operating in an enterprise computing environment. The document capture server can provide validated data to the AI platform to improve future analyses by the AI platform.

Abstract: The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

Abstract: A system and method for determine a parameter for medical data processing are provided. The method may include obtaining sample data, the sample data may comprise at least one of projection data or a scanning parameter. The method may also include obtaining a first neural network model. The method may further include determining the parameter based on the sample data and the first neural network model. The parameter may comprise at least one of a correction coefficient or a noise reduction parameter.

Abstract: The present invention belongs to the field of image processing and computer vision, and discloses an acceleration method of depth estimation for multiband stereo cameras. In the process of depth estimation, during binocular stereo matching in each band, through compression of matched images, on one hand, disparity equipotential errors caused by binocular image correction can be offset to make the matching more accurate, and on the other hand, calculation overhead is reduced. In addition, before cost aggregation, cost diagrams are transversely compressed and sparsely matched, thereby reducing the calculation overhead again. Disparity diagrams obtained under different modes are fused to obtain all-weather, more complete and more accurate depth information.

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

Abstract: Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: There is provided a computer-implemented method and apparatus for determining temporal behaviour of an object. The method comprises receiving track data indicative of a plurality of tracks. Each track identifies an association between features corresponding to an object in each of a plurality of images forming image data representative of one or more objects. The method further comprises identifying, in at least some of the plurality of tracks, one or more faults, wherein each fault is associated with at least one respective fault image of the image data. The method further comprises iteratively performing a correction process on the track data.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.

Abstract: Certain aspects of the disclosure provide systems and methods for training an information extraction transformer model architecture directed to pre-training a first multimodal transformer model on an unlabeled dataset, training a second multimodal transformer model on a first labeled dataset to perform a key information extraction task processing the unlabeled dataset with the second multimodal transformer model to generate pseudo-labels for the unlabeled dataset, training the first multimodal transformer model based on a second labeled dataset comprising one or more labels, the pseudo-labels generated, or combinations thereof to generate a third multimodal transformer model, generating updated pseudo-labels based on label completion predictions from the third multimodal transformer model, and training the third multimodal transformer model using a noise-aware loss function and the updated pseudo-labels to generate an updated third multimodal transformer model.

Abstract: A method of performing convolution in a neural network with variable dilation rate is provided. The method includes receiving a size of a first kernel and a dilation rate, determining at least one of size of one or more disintegrated kernels based on the size of the first kernel, a baseline architecture of a memory and the dilation rate, determining an address of one or more blocks of an input image based on the dilation rate, and one or more parameters associated with a size of the input image and the memory. Thereafter, the one or more blocks of the input image and the one or more disintegrated kernels are fetched from the memory, and an output image is obtained based on convolution of each of the one or more disintegrated kernels and the one or more blocks of the input image.