Abstract: Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.

Abstract: A three-dimensional data encoding method includes: encoding geometry information of each of three-dimensional points based on one of a first geometry information encoding method of encoding using octree division and a second geometry information encoding method of encoding without using octree division; and generating a bitstream including the geometry information encoded and a geometry information flag indicating whether the encoding was performed based on the first geometry information encoding method or the second geometry information encoding method. In the generating of the bitstream: when the encoding is performed based on the first geometry information encoding method, the bitstream including a parameter set used in octree division is generated; and when the encoding is performed based on the second geometry information encoding method, the bitstream not including the parameter set used for octree division is generated.

Abstract: An anomaly detecting unit detects an anomaly object in a target image. A characteristic amount watching unit watches at least two basic characteristic amounts of the anomaly object, determines whether values of the basic characteristic amounts satisfy a predetermined watching determination condition of any one of predetermined plural anomaly types or not, if it is determined that the values of the basic characteristic amounts satisfy the watching determination condition, determines as an auxiliary characteristic amount for the anomaly object a characteristic amount corresponding to the anomaly type of which the values of the basic characteristic amounts satisfy the watching determination condition, and starts watching a value of the auxiliary characteristic amount. An anomaly type determining unit determines an anomaly type of the anomaly object on the basis of the basic characteristic amounts and the auxiliary characteristic amount currently watched by the characteristic amount watching unit.

Abstract: This image retrieval system comprises: a storage means for storing a plurality of registered images capturing a plurality of objects to be registered; an acquisition means for acquiring a retrieval image including a region of interest; an extraction means for extracting a feature amount from a region, in the retrieval image, other than the region of interest; a retrieval means for retrieving, from the plurality of registered images stored in the storage means, a registered image having a feature amount that matches the feature amount extracted by the extraction means; and an output means for outputting, in a manner that makes it possible to recognize a region corresponding to the region of interest in the retrieval image, all or a portion of the registered image retrieved by the retrieval means.

Abstract: The present disclosure is directed to encoding images. In particular, one or more computing devices can receive data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type. The computing device(s) can receive data representing an image comprising one or more objects of the particular type. The computing device(s) can generate, based at least in part on the data representing the image and the data representing the ML model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the ML model(s) configured to encode the images were trained.

Abstract: An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.

Abstract: The present invention relates to a device, a method and a computer program product for displaying at least one quantitative and/or qualitative information on at least one nutrient content and/or value of a food item. At least one optical sensor is required for acquiring at least one image of the arrangement comprising a food item to be analyzed. Means for analyzing the image of the arrangement comprising the food items are also required and that means are adapted at providing a first segmentation of the at least one image and dividing the arrangement into a plurality of separately identifiable food items. Further that means is adapted at identifying at least one scale information of the at least one image and constructing a virtual model reflective of the arrangement comprising the food item.

Abstract: Some aspects include a method for operating a robot in a workspace, including: capturing, with an image sensor, image data of the workspace including objects within the workspace as the robot moves within the workspace; identifying, with a processor of the robot, at least one characteristic in the image data, wherein the at least one characteristic comprises one of: an edge, a shape, and a color; determining, with the processor, an object type of an object; and instructing, with the processor, the robot to execute at least one action based on the at least one characteristic, wherein the at least one action comprises one of: driving along a modified path and driving around the object.

Abstract: It is provided a method for processing input media data. The method is performed in a media data processor and comprising the steps of: obtaining input media data; identifying, in the input media data, sensitive information; generating replacement information, wherein the replacement information is at least partly anonymised information corresponding to the sensitive information, and the replacement information contains at least one characteristic based on the sensitive information; modifying the input media data by replacing its sensitive information with the replacement information, resulting in a modified media data; and providing the modified media data for further processing.

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using self-attention based neural networks. One of the methods includes obtaining one or more images comprising a plurality of pixels; determining, for each image of the one or more images, a plurality of image patches of the image, wherein each image patch comprises a different subset of the pixels of the image; processing, for each image of the one or more images, the corresponding plurality of image patches to generate an input sequence comprising a respective input element at each of a plurality of input positions, wherein a plurality of the input elements correspond to respective different image patches; and processing the input sequences using a neural network to generate a network output that characterizes the one or more images, wherein the neural network comprises one or more self-attention neural network layers.

Abstract: Machines can operate based on electricity received from a charging rail system installed along a route at a worksite. Sensors on the machines capture image data indicating locations and/or orientations of components of the charging rail system. Machine controllers of the machine, and/or a worksite controller, can monitor the integrity of the charging rail system by detecting possible faults if the locations and/or orientations of one or more components vary by more than a threshold amount from target locations and/or orientations. The machine controllers and/or the worksite controller can also initiate one or more response actions when a possible fault in the charging rail system is detected.

Abstract: An inspection apparatus includes an image distortion estimation unit that estimates a distortion amount between a reference image and an inspection image, an image distortion correction unit that corrects the inspection image and/or the reference image using an estimated distortion amount, and an inspection unit that performs inspection using a corrected inspection image and the reference image or the inspection image and a corrected reference image. The image distortion estimation unit estimates a distortion amount in which only distortion occurring in an entire image can be corrected by adjustment of a correction condition.

Abstract: In some aspects, the disclosure is directed to methods and systems for machine learning-based identification of sepsis in patients, such as those with severe burn injuries. A set of biomarker and vital sign measurements of a population with a known clinical diagnosis may be collected and normalized. A first subset of the modified set of biomarker and vital sign measurements may be used to train and test the algorithm (e.g. a neural network), and a second subset of the modified set of biomarker and vital sign measurements may be used for secondary validation studies.

Abstract: The technology of this application related to an image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image. The first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions. The first and second trained artificial intelligence models are collectively trained end to end.

Abstract: The present disclosures provides systems and methods for generating composite based data for use in machine learning systems, such as for use in training a machine learning system on the composite based data to identify an object of interest. In an aspect, a method of generating composite based data for use in training machine learning systems comprises: receiving a plurality of images, each of the plurality of images having a corresponding label; generating a composite image comprising the plurality of images, each of the plurality of images occupying a region of the composite image; generating a response map for the composite image, the response map having a plurality of response entries, each response entry encoded with a desired label corresponding to a fragment of the composite image, and generating composite data comprising the desired label of a response entry and image data corresponding to the fragment of the composite image.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: The exemplary system, method, and computer-accessible medium for generating a multiclass image segmentation model(s) can include receiving multiple single-class image datasets, receiving a target mask for each of the single-class image datasets, receiving a condition of an object associated with each of the single-class image datasets, and generating the multiclass image segmentation model(s) based on the single-class image datasets, the target masks, and the identification of the target objects. The single-class image datasets can include computer tomography images of abdominal organs. The single-class image datasets can be non-overlapping single-class image datasets. The single-class image datasets can include medical imaging datasets or cityscape datasets.

Abstract: A method for point cloud decoding includes receiving a bitstream. The method also includes decoding the bitstream into multiple frames that include pixels. Certain pixels of the multiple frames correspond to points of a three-dimensional (3D) point cloud. The multiple frames include a first set of frames that represent locations of the points of the 3D point cloud and a second set of frames that represent attribute information for the points of the 3D point cloud. The method further includes reconstructing the 3D point cloud based on the first set of frames. Additionally, the method includes identifying a first portion of the points of the reconstructed 3D point cloud based at least in part on a property associated with the multiple frames. The method also includes modifying a portion of the attribute information. The portion of the attribute information that is modified corresponds to the first portion of the points.