Abstract: Presented are concepts for feature identification in medical imaging of a subject. One such concept processes a medical image with a Bayesian deep learning network to determine a first image feature of interest and an associated uncertainty value, the first image feature being located in a first sub-region of the image. It also processes the medical image with a generative adversarial network to determine a second image feature of interest within the first sub-region of the image and an associated uncertainty value. Based on the first and second image features and their associated uncertainty values, the first sub-region of the image is classified.

Abstract: A method including: automatically detecting, using at least one machine learning algorithm, one or more abnormalities depicted in a medical image of a patient; automatically determining whether the one or more abnormalities have remained temporally and unchanged, based on an older medical image of the patient; and upon determining that the one or more abnormalities have remained temporally and spatially unchanged: automatically inpainting the one or more abnormalities in the medical image, and automatically enrich a new training set with the inpainted medical image.

Abstract: A comparator may automatically detect and quantify subtle and/or microscopic variance to a feature of a three-dimensional (“3D”) object in a reproducible manner based on point cloud imaging of that 3D object. The comparator may isolate a first set of data points, that represent the object feature at a first time, in a reference point cloud, and may isolate a second set of data points, that represent the same but altered object feature at a different second time, in a non-reference point cloud. The comparator may detect variance between positional values and visual characteristic values of the second set of data points and the corresponding positional values and visual characteristic values of the first set of data points, and may quantify a change occurring to the object feature between the first time and the second time based on a mapping of the variance to a particular unit of measure.

Abstract: An image identification apparatus includes an acquisition unit configured to acquire an input image, a classification unit configured to calculate a classification score of the input image based on a neural network having a weight coefficient in each layer determined to calculate the classification score indicating a degree of similarity between a training image and an image to be processed, an anomaly determination unit configured to calculate an anomaly score of the input image based on a function approximator constructed by machine learning based on the training image of correct answer, and an identification unit configured to classify the input image into a good image having a high degree of similarity to the training image of the correct answer or a bad image having low degree of similarity to the training image of correct answer based on the classification score and the anomaly score.

Abstract: A method for determining an enclosing surface of a cavity of an organ of a patient includes receiving a plurality of data points including respective position measurements in the cavity. A first iterative process is performed, that progressively divides sub-volumes into smaller sub-volumes, until a density of the data points in each sub-volume falls below a predefined density. A second iterative process is performed, that progressively discards sub-volumes that are fully-surrounded by populated sub-volumes, and discards the data points contained in the discarded sub-volumes. The enclosing surface of the cavity is defined based on the data points remaining after the second iterative process. The enclosing surface of the cavity is presented to a user.

Abstract: The present disclosure relates to a method and device for image correction and a storage medium. The method can include a correction offset for each unit to be corrected in an image is determined, at least one target region in the image is determined, an image weight coefficient for each unit to be corrected in the image is determined according to the at least one target region, a final offset for each unit to be corrected in the image is determined according to the image weight coefficient and the correction offset, and each unit to be corrected in the image is corrected according to the final offset.

Abstract: Embodiments of methods and apparatuses for object detection and of computer devices are disclosed.

Abstract: An information measuring method is used for measuring installation information of a camera disposed over a plane with an installation height and oriented towards the plane with a shooting angle. The information measuring method includes steps of disposing a first reference point and a second reference point on the plane; measuring a first distance and a second distance between the first reference point, the second reference point and a third reference point; capturing an image including the first reference point, the second reference point and the third reference point; analyzing the image to define a first reference line and a second reference line; determining a first angle and a second angle according to the first reference line, the second reference line and a first normal line; and determining the installation height and/or the shooting angle according to the first distance, the second distance, the first angle and the second angle.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing dynamic, stroke-based alignment of touch displays. In one aspect, a method includes obtaining a candidate transcription that an automated speech recognizer generates for an utterance, determining a particular context associated with the utterance, determining that a particular n-gram that is included in the candidate transcription is included among a set of undesirable n-grams that is associated with the context, adjusting a speech recognition confidence score associated with the transcription based on determining that the particular n-gram that is included in the candidate transcription is included among the set of undesirable n-grams that is associated with the context, and determining whether to provide the candidate transcription for output based at least on the adjusted speech recognition confidence score.

Abstract: Methods and systems are disclosed for improvements in aquaculture that allow for increasing the number and harvesting efficiency of fish in an aquaculture setting by identifying and predicting internal conditions of the juvenile fish based on external characteristics that are imaged through non-invasive means.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.

Abstract: When measuring a mass-produced work piece using a measuring X-ray CT apparatus, which is configured to emit X-rays while rotating a work piece that is arranged on a rotary table and to reconstruct a projection image thereof to generate volume data of the work piece, the present invention assigns values to volume data for a predetermined work piece and stores the same as master data; obtains volume data for a mass-produced work piece under identical conditions to the predetermined work piece; measures the volume data and obtains an X-ray CT measured value for the mass-produced work piece; and corrects the X-ray CT measured value for the mass-produced work piece using the master data.

Abstract: A method for compressing an image with bit rate control includes: compressing data of a current block with a compression level to produce compression data of the current block; in response to compression of the current block, accumulating a data size of compressed data of blocks that have been compressed to obtain an accumulated real size; in response to the compression of the current block, accumulating a target block size according to a number of blocks that have been compressed to obtain an accumulated target size; and adjusting the compression level for a next block according to at least the accumulated real size and the accumulated target size.

Abstract: A technology for reconstructing an image of a three-dimensional object. In one example, a projection image dataset can be obtained from an imaging data detector and a reduced image dataset that has a lower quantity of data as compared to a quantity of data of the projection image dataset can be generated from the projection image dataset. An image remainder dataset can be generated to indicate an image difference between the projection image dataset and the reduced image dataset. A first scale image reconstruction of the three-dimensional object can be generated using the reconstruction technique and the reduced image dataset, and a second scale image reconstruction of the three-dimensional object can be generated using the iterative reconstruction technique and the image remainder dataset.

Abstract: The disclosure provides a method, a device and a system for fault detection. The fault detection method includes following steps: acquiring interactive data generated during a working process of a target Internet of Things system, and generating data to be detected based on the interactive data; detecting whether the data to be detected is fault data or not, and performing an emotion analysis on the data to be detected which is judged as the fault data to obtain a corresponding emotion label; and extracting a corresponding fault type from a preset fault emotion mapping table based on the emotion label and outputting the fault type. In this disclosure, the interactive data is analogized to human emotions, and the faults are accurately found through the emotional analysis on the data, thereby achieving the fault detection of the Internet of Things system in a convenient, flexible and accurate manner.

Abstract: There is provided a computed implemented method of automatically generating an adapted presentation of at least one candidate anomalous object detected from anatomical imaging data of a target individual, comprising: providing anatomical imaging data of the target individual acquired by an anatomical imaging device, analyzing the anatomical imaging data by a detection classifier for detecting at least one candidate anomalous object of the anatomical imaging data and computed associated location thereof, computing, by a presentation parameter classifier, at least one presentation parameter for adapting a presentation of a sub-set of the anatomical imaging data including the at least one candidate anomalous object according to at least the location of the candidate anomalous object, and generating according to the at least one presentation parameter, an adapted presentation of the sub-set of the anatomical imaging data including the at least one candidate anomalous object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring of pet feeding. The methods, systems, and apparatus include actions of: obtaining a first reference image of a container corresponding to a full state of the container; obtaining a second reference image of the container corresponding to an empty state of the container; obtaining a sample image of the container; based on the first and second reference images, determining an amount of content in the container from the sample image; and based on the amount of the content being less than a reference amount, notifying a user that the amount of the content is getting low.

Abstract: Aspects described herein include a computer-implemented method and associated system and computer program product. The method includes training a model using a plurality of defect images. Each defect image corresponds to a respective first feature combination of encoded textual features of a predefined set of textual features. The method further includes generating a first synthetic image using the model. The first synthetic image corresponds to a second feature combination of encoded textual features of the predefined set that is distinct from the first feature combinations.

Abstract: An electronic device includes one or more imaging cameras. After a reset of the device or other specified event, the electronic device identifies an estimate of the device's pose based on location data such as Global Positioning System (GPS) data, cellular tower triangulation data, wireless network address location data, and the like. The one or more imaging cameras may be used to capture imagery of the local environment of the electronic device, and this imagery is used to refine the estimated pose to identify a refined pose of the electronic device. The refined pose may be used to identify additional imagery information, such as environmental features, that can be used to enhance the location based functionality of the electronic device.