Abstract: A system and method automatically orient geological core samples. The system comprises a camera, a core analyzer module, and a core sample rotation apparatus. The camera is configured to capture a reference image of a cross-section of a reference core sample from a well and to capture a sample image of a cross-section of the to-be-oriented core sample from the well. The core analyzer module comprises code executable in a hardware processor to process the sample image to substantially match the reference image, and to determine an orientation angle of the match. The core sample rotation apparatus is configured to orient the to-be-oriented core sample by the orientation angle. A method comprises steps performed during operation of the system.

Abstract: System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting regions of an environment. One of the methods includes receiving a representation of a scene in an environment; processing the representation using a center prediction neural network to generate: (i) features of the scene in the environment, and (ii) a respective center score corresponding to each of a plurality of locations in the environment; selecting, based on the respective center scores, one or more of the plurality of locations; and for each selected location: processing an input comprising the features of the scene in the environment and data specifying the selected location using a geometry prediction neural network to generate a geometry prediction that represents a geometry of the region that is centered at the selected location.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive recommendations using an MRI acquisition set associated with a common target object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive recommendations based at least in part on an MRI set and utilizing one or more of techniques using image preprocessing models, techniques using image segmentation models, techniques using voxel integrity score generation machine learning models, and techniques using integrity score normalization models.

Abstract: The learning apparatus classifies target domain data into (N-c) classes based on unique features of the target domain data, classifies source domain data into N classes based on unique features of the source domain data, and classifies the target domain data and the source domain data into the N classes based on common features of the target domain data and the source domain data. Also, the learning apparatus calculates a first distance between the common features of the target domain data and the source domain data, and calculates a second distance between the unique features of the target domain data and the source domain data. Next, the learning apparatus updates parameters of a common feature extraction unit based on the first distance, and updates parameters of a target domain feature extraction unit and a source domain feature extraction unit based on the second distance.

Abstract: Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

Abstract: Upon determining a confidence score for challenge biometric data for a user of an object is less than a first confidence threshold, a confirmation confidence score for the challenge biometric data is determined based on a remote confidence score for the challenge biometric data received from a remote computer. Upon determining that the confirmation confidence score is greater than a second confidence threshold, a user status of the user is determined based on an authenticator. The user status is one of authorized or not authorized. The second confidence threshold is less than the first confidence threshold. Upon determining that the confirmation confidence score is less than or equal to the second confidence threshold, the user status is determined based on supplemental biometric data. Object components are controller based on the user status.

Abstract: Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

Abstract: A vehicle occupant monitoring apparatus includes an operation determination unit and an output unit. The operation determination unit is configured to process a captured image obtained by an imaging device configured to capture an image of an occupant in a vehicle. The operation determination unit is further configured to determine a non-contact operation provided by the occupant. The output unit is configured to output a control signal associated beforehand with the non-contact operation that is provided by the occupant and that is determined by the operation determination unit.

Abstract: An image processing apparatus includes an image synthesizer configured to correct input images based on first-correction data to generate corrected images, and to generate a synthesized image by stitching the corrected images together, a determiner configured to determine whether the corrected images are appropriately stitched together in the synthesized image by using a first-trained model learned whether images are appropriately stitched together, a second-correction-data generator configured to generate second-correction data by supplying the input images to a second-trained model learned relationships between correction data used to correct source images to generate corrected images appropriately stitched together and the source images, and an image updater configured to output the synthesized image when a determination result of the determiner is affirmative and output an updated synthesized image generated by causing the image synthesizer to update the synthesized image based on the second-correction da

Abstract: Embodiments of the present disclosure relate to methods, devices and computer-readable storage medium for image processing. A method for image processing comprises obtaining a plurality of images, each of the plurality of images having an initial semantic segmentation label indicating a semantic class of a pixel in the each image; obtaining a plurality of image masks corresponding to the plurality of images, each of the plurality of image masks being used for selecting a target region in a corresponding image of the plurality of images; regenerating respective semantic segmentation labels of the plurality of images based on the plurality of image masks and initial semantic segmentation labels of the plurality of images; and generating a mixed image and a semantic segmentation label of the mixed image based on the plurality of images and the regenerated respective semantic segmentation labels.

Abstract: Systems and methods presented herein are configured to train a neural network model using a first set of photographs, wherein each photograph of the first set of photographs depicts a first set of objects and include one or more annotations relating to each object of the first set of objects; to automatically create mask images corresponding to a second set of objects depicted by a second set of photographs; to enable manual fine tuning of the mask images; to re-train the neural network model using the second set of photographs, wherein the re-training is based at least in part on the manual fine tuning of the mask images; and to identify one or more individual objects in a third set of photographs using the re-trained neural network model.

Abstract: A biometric identification system acquires a multimodal image. The system is trained to determine three embeddings: a first embedding associated with features present in a first modality, an intersection embedding associated with features present in both the first and a second modality, and an XOR embedding associated with features that are not shared between the first modality and the second modality. Once trained, the system may process a multimodal query image to determine query embedding data. Additional information, such as minutiae depicted in the multimodal query image may also be determined. The query embedding data, and in some implementations the additional information, may be compared with enrolled embedding data associated with a previously enrolled user. If the comparison exceeds a threshold value, an identity may be asserted.

Abstract: A user authentication method and a user authentication apparatus acquire an input image including a frontalized face of a user, calculate a confidence map including confidence values, for authenticating the user, corresponding to pixels with values maintained in a depth image of the frontalized face of the user among pixels included in the input image, extract a second feature vector from a second image generated based on the input image and the confidence map, acquire a first feature vector corresponding to an enrolled image, and perform authentication of the user based on a correlation between the first feature vector and the second feature vector.

Abstract: Provided is a system and method for estimating the motion of a target inside a tissue based on surface deformation of the soft tissue. The system consists of an acquisition unit, a reference input unit, two surface extraction units, a target position extraction unit, a feature calculation unit, and a target motion estimation unit. The method includes: the acquisition unit acquires an image Ii of the soft tissue; the surface extraction unit extracts a surface fi of the soft tissue from Ii; the reference input unit acquires a reference image Iref of the soft tissue; the surface extraction unit and the target position extraction unit respectively extract a reference surface fref of the soft tissue and a target reference position tref from Iref, the feature calculation unit calculates deformation feature ?i of fi relative to fref, the target motion estimation unit estimates the target displacement based on ?i and tref.

Abstract: Systems and methods are disclosed for circular stitching of images. For example, methods may include accessing a first image captured using a first image sensor; accessing a second image captured using a second image sensor; determining a cost table for a circular stitching boundary that includes overlapping regions of the first image and the second image; determining an extended disparity profile based on a periodic extension of the cost table and a smoothness criterion, wherein the extended disparity profile has a length greater than the width of the cost table; determining a binocular disparity profile of a length equal to the width of the cost table based on a contiguous subsequence of the extended disparity profile; and stitching the first image and the second image using the binocular disparity profile to obtain a combined image.

Abstract: An image processing system configured to receive an input time-of-flight depth map representing the distance of objects in an image from a camera at a plurality of locations of pixels in the respective image, and in dependence on that map to generate an improved time-of-flight depth map for the image, the input time-of-flight depth map having been generated from at least one correlation image representing the overlap between emitted and reflected light signals at the plurality of locations of pixels at a given phase shift, the system being configured to generate the improved time-of-flight depth map from the input time-of-flight depth map in dependence on a colour representation of the respective image and at least one correlation image.

Abstract: This disclosure relates to an image segmentation method and apparatus and image three-dimensional reconstruction method and apparatus, and to the field of computer technology. The segmentation method includes: dividing pixels in an image to be segmented among different pixel sets, according to color gamut ranges to which pixel values of the pixels belong; determining matching between pixels in each pixel set according to pixel values; and performing image segmentation on the image to be segmented according to the matching.

Abstract: A system of automatically monitoring an electronic billboard to immediately responding a production abnormality and a method thereof are disclosed. In the system, a monitoring device performs an image pattern analysis on a monitoring image to analyze at least one analysis area, and captures the at least one analysis area as a to-be-analyzed image; at least one image analysis method is set to transform the to-be-analyzed image into an image characteristic according to an image pattern of the to-be-analyzed image. When a comparison between the image characteristic and a preset characteristic indicates an abnormality, a monitoring device generates, records and transmits a warning message and/or issues an alarm, thereby achieving the technical effect of automatically monitoring the electronic billboard and immediately responding a production abnormality.

Abstract: A system and a method for compressing an image based on a FLASH in-memory computing array are provided. The system includes: a convolutional neural network for encoding of the FLASH in-memory computing array, a convolutional neural network for decoding based on the FLASH in-memory computing array, and a quantization module; the convolutional neural network for encoding based on the FLASH in-memory computing array is configured to encode an original image to obtain a feature image; the quantization module is configured to quantize the feature image to obtain a quantized image; the convolutional neural network for decoding based on the FLASH in-memory computing array is configured to decode the quantized image to obtain a compressed image.