Abstract: The present invention relates to the field of medical monitoring, and in particular non-contact monitoring of one or more physiological parameters in a region of a patient during surgery. Systems, methods, and computer readable media are described for generating a pulsation field and/or a pulsation strength field of a region of interest (ROI) in a patient across a field of view of an image capture device, such as a video camera. The pulsation field and/or the pulsation strength field can be generated from changes in light intensities and/or colors of pixels in a video sequence captured by the image capture device. The pulsation field and/or the pulsation strength field can be combined with indocyanine green (ICG) information regarding ICG dye injected into the patient to identify sites where blood flow has decreased and/or ceased and that are at risk of hypoxia.

Abstract: A relative position calculator calculates relative position and attitude of a vehicle based on images captured by an image capturing apparatus on the vehicle. An absolute position calculator extracts a marker from an image captured by the image capturing apparatus, and calculates absolute position and attitude of the vehicle, based on position and attitude of the one extracted marker. A corrector calculates the corrected position and attitude, not using the absolute position and attitude calculated based on the position and attitude of the marker when a difference or ratio of an apparent height and width of the marker in the image is equal to or smaller than a threshold, but using the absolute position and attitude calculated based on the position and attitude of the marker when the difference or ratio is larger than the threshold.

Abstract: Apparatus and methods for the stitch zone calculation of a generated projection of a spherical image. In one embodiment, a non-transitory computer-readable apparatus comprising a storage apparatus, the storage apparatus comprising instructions configured to, when executed by a processor apparatus, cause a computerized apparatus to identify a stitch line associated with an equatorial area of a plurality of spherical images; re-orient the plurality of spherical images in accordance with the stitch line; and project the re-oriented plurality of spherical images to a selected image projection type.

Abstract: Provided are a segmentation system and method of an ascending aorta and a coronary artery from coronary CT angiography (CCTA) using a hybrid approach, which relates to a technology of extracting only the shape of the coronary artery and ascending aorta from the input coronary CT medical image.

Abstract: This application describes cross-scale point cloud segmentation network architecture for and exemplary systems that utilize such network architecture for semantic segmentation of a point cloud. An embodiment of the network architecture includes an encoding path comprising a plurality of sequentially connected encoding nodes, a decoding path following the encoding path and comprising a plurality of sequentially connected decoding nodes, and a plurality of data links respectively corresponding to a plurality of levels of feature resolution, in which each of the plurality of data links connects one of the plurality of encoding nodes and one of the plurality of decoding nodes that have a same level of feature resolution.

Abstract: A system and a method for providing visual assistance to an individual suffering from motion sickness. The system includes: a sensor configured to measure movement data of a vehicle in real time; an artificial horizon device designed to generate an image of an artificial horizon in real time on the basis of the movement data of the vehicle; and a wearable augmented reality device designed to display the image of the artificial horizon in real time to an individual who is wearing the wearable device and is a passenger in the vehicle.

Abstract: Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.

Abstract: A system and method are disclosed for low-light image enhancement using denoising preprocessing with wavelet decomposition AI-based techniques to enhance image quality of low-light images. Subsampled images are created from a raw input image. A wavelet decomposition process is performed on each subimage to create multiple frequency domain subimages. Each frequency domain subimage is input into a corresponding neural network. The output of each corresponding network is input to an inverse wavelet module. The output of the inverse wavelet module is a denoised image that is input to an image signal processing pipeline, where additional processing may be performed on the denoised image.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: Systems and methods relate generally to performing a machine learning task on training documents to generate an output. In an example method, a pretrained Sentence Bidirectional Encoder Representational Transformers (“S-BERT”) model is obtained. The training documents are scanned by a plurality of scanners. Content of the training documents is recognized with character recognition. The content is templated responsive to the character recognition. The content is processed with the pretrained S-BERT model for training thereof. A trained S-BERT model is generated from the processing of the content as the output. The trained S-BERT model is configured to automatically categorize and assemble non-training documents into original configurations thereof.

Abstract: Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Abstract: Today, artificial neural networks are trained on large sets of manually tagged images. Generally, for better training, the training data should be as large as possible. Unfortunately, manually tagging images is time consuming and susceptible to error, making it difficult to produce the large sets of tagged data used to train artificial neural networks. To address this problem, the inventors have developed a smart tagging utility that uses a feature extraction unit and a fast-learning classifier to learn tags and tag images automatically, reducing the time to tag large sets of data. The feature extraction unit and fast-learning classifiers can be implemented as artificial neural networks that associate a label with features extracted from an image and tag similar features from the image or other images with the same label. Moreover, the smart tagging system can learn from user adjustment to its proposed tagging. This reduces tagging time and errors.

Abstract: A method for operating a processing unit of a vehicle for processing sensor data of several different sensors with an artificial neural network, wherein a set of volume data cells is provided as a volumetric representation of different volume elements of an environment, and when sensor data is generated by the sensors the sensor data is transferred to the respective volume data cells using an inverse mapping function, wherein each inverse mapping function is a mapping of a respective sensor coordinate system of the sensor to an internal volumetric coordinate system corresponding to the world coordinate system, and by the transfer of the sensor data each volume data cell receives the sensor data that are associated with this volume data cell according to the inverse mapping function from each sensor, wherein the received sensor data from each sensor are accumulated in the respective volume data cell as combined data.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions.

Abstract: An inspection support apparatus includes: an image acquisition unit (11) that acquires an image obtained by photographing a concrete structure that is an inspection target; a damage region extraction unit (12) that extracts from the acquired image, a damage region (water leakage, free lime, etc.) appearing on a surface of the concrete structure; a causal part detection unit (17) that detects, in a case where the damage region is extracted, a causal part (a crack, a construction joint, a joint, a peeling part, etc.) causing damage from the image on the basis of a result of extraction of the damage region; and an output unit (19) that outputs the result of extraction of the damage region and a result of detection of the causal part.

Abstract: A system and method for determining a noise-attenuated wellbore image is disclosed. The method includes obtaining a plurality of training images of a first wellbore wall portion, where each training image includes a first signal component and a first noise component, and training, using the plurality of training images, an artificial neural network to estimate the first signal component of one of the plurality of training images. The method further includes obtaining an application image of a second wellbore wall portion, including a second signal component and a second noise component, and determining the noise-attenuated wellbore image by applying the trained artificial neural network to the application image, wherein the noise-attenuated wellbore image comprises the second signal component.

Abstract: A system, method, and non-transitory computer readable medium for ichnological classification of geological images are described. The method of ichnological classification of geological images includes receiving a geological image by a computing device having circuitry including a memory storing program instructions and one or more processors configured to perform the program instructions, formatting the geological image to generate a formatted geological image, applying the formatted geological image to a deep convolutional neural network (DCNN) trained to classify bioturbation indices, and matching the formatted geological image to a bioturbation index class.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.