Abstract: A processor-implemented method includes: generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Abstract: Data for training a machine learning algorithm to detect airborne objects is generated by capturing background images using a camera aboard an aerial vehicle, and generating a trajectory of a synthetic object, such another aerial vehicle, in three-dimensional space. The trajectory is projected into an image plane of the camera, determined from a pose of the camera calculated using inertial measurement unit data captured by the aerial vehicle. Images of the synthetic object may be rendered based on locations of the trajectory in the image plane at specific times. Pixel-accurate locations for the rendered images may be determined by calculating a homography from consecutive images captured using the camera, and adjusting locations of the trajectory using the homography. The rendered images may be blended into the images captured by the camera at such locations, and used to train a machine learning algorithm.

Abstract: The present disclosure describes a method, an apparatus, and a non-transitory computer-readable medium for detecting sensitive text information such as privacy-related text information from a signal and modifying the signal by removing the detected sensitive text information therefrom. The apparatus receives the signal such as an image, a video clip, or an audio clip, and recognizes a text string therefrom. The apparatus then detects, from the text string, a substring based on a similarity between the substring and a regular expression, and modifies the signal by removing information related to the detected substring from the signal.

Abstract: The Selectable Facial Recognition Assistance Method and System (SFRA) allows law enforcement officers, military personnel, and other users the ability to rapidly and accurately identify wanted or known personnel, by combining the ability to select the type of examination, selection of optimized face matching algorithms and expert face examiners. The SFRA allows the user to select “Quick Looks” or “Long Looks” with respect to the level of detail of examination, along with race-based selected algorithms and expert face examiners. The race-based focused examination combining specialized algorithms and examiners will produce the highest confidence levels of match or no-match based on photographs taken, submitted, as well as database photographs. The SFRA will greatly assist users and persons being photographed to avoid false matches and false no-matches. This system will greatly reduce misidentification, profiling, or any other identification weaknesses that the systems and users may have.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: The method includes generating, for each of a plurality of original images, a first artificially degraded image by applying a first image-artifact-generation logic on each of the original images; and generating the program logic by training an untrained version of a first machine-learning logic that encodes a first artifacts-removal logic on the original images and their respectively generated first degraded images; and returning the trained first machine-learning logic as the program logic or as a component thereof. The first image-artifact-generation logic is A) an image-acquisition-system-specific image-artifact-generation logic or B) a tissue-staining-artifact-generation logic.

Abstract: Methods and apparatuses are disclosed to personalize image capture operations of imaging equipment according to models that correspond uniquely to subjects being imaged. According to these techniques, a subject's face may be detected from a first image supplied by an image source and a first model of the subject may be developed from the detected face. The first model of the subject may be compared to another model of the subject developed from other images. Image adjustment parameters may be generated from a comparison of these models, which may control image adjustment techniques that are applied to the newly captured image of the subject. In this manner, aspects of the present disclosure may generate image capture operations that are tailored to characteristics of the subjects being imaged and avoid artifacts that otherwise could cause image degradation.

Abstract: Systems and methods for motion correction in medical imaging are provided in the present disclosure. The systems may obtain at least two image sequences relating to a subject. Each of the at least two image sequences may be reconstructed based on image data that is acquired by a medical imaging device during one of at least two time periods. The subject may undergo a physiological motion during the at least two time periods. The systems may generate, based on the at least two image sequences, at least one corrected image sequence relating to the subject by correcting, using a motion correction model, an artifact caused by the physiological motion.

Abstract: According to embodiments of the present invention, a method, a device and a computer program product for image processing is provided. A computing device obtains an image of a first object, the image presenting a defect of the first object. A computing device obtains defect distribution information indicating respective frequencies of a plurality of predetermined categories of defects presented at corresponding locations in a plurality of training images, the plurality of training images presenting second objects and being used for training a defect classifier. A computing device determines a target category of the defect of the first object by applying the image and the defect distribution information to the defect classifier. A computing device generates one or more correction notifications.

Abstract: An apparatus that estimates a position of each object in image data in which a plurality of objects is imaged, the apparatus includes a first acquisition unit configured to acquire position information indicating positions of joints of the plurality of objects in the image data, a second acquisition unit configured to acquire a score map in which a feature for identifying each object is converted into a numerical value, the score map being output by a pre-trained model in response to input of the image data, and an identification unit configured to identify positions of joints belonging to each of the plurality of objects, based on the position information and the score map.

Abstract: A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

Abstract: According to an embodiment, a reading system includes a first extractor and a reader. The first extractor extracts, from an image in which a meter is imaged, a first region surrounded with a first contour, and a second region surrounded with a second contour positioned outward of the first contour. The reader calculates a first indication based on the first region, calculates a second indication based on the second region, calculates a first score relating to the first indication based on the first region, and calculates a second score relating to the second indication based on the second region.

Abstract: The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

Abstract: The present disclosure provides a computer-implemented method, a device, and a computer program product using a user-guided domain adaptation (UGDA) architecture. The method includes training a combined model using a source image dataset by minimizing a supervised loss of the combined model to obtain first sharing weights for a first FCN and second sharing weights for a second FCN; training a discriminator by inputting extreme-point/mask prediction pairs for each of the source image dataset and a target image dataset and by minimizing a discriminator loss to obtain discriminator weights; and finetuning the combined model by predicting extreme-point/mask prediction pairs for the target image dataset to fool the discriminator by matching a distribution of the extreme-point/mask prediction pairs for the target image dataset with a distribution of the extreme-point/mask prediction pairs for the source image dataset.

Abstract: Systems and methods are provided for provided for automatic evaluation of a human embryo. An image of the embryo is obtained and provided to a neural network to generate a plurality of values representing the morphology of the embryo. The plurality of values representing the morphology of the embryo are evaluated at an expert system to provide an output class representing one of a current quality of the embryo, a future quality of the embryo, a likelihood that implantation of the embryo will be successful, and a likelihood that implantation of the embryo will result in a live birth.

Abstract: Ultrasound image devices, systems, and methods are provided. A clinical condition detection system, comprising a communication device in communication with an ultrasound imaging device and configured to receive a sequence of ultrasound image frames representative of a subject body across a time period; and a processor in communication with the communication device and configured to classify the sequence of ultrasound image frames into a first set of clinical characteristics by applying a first predictive network to the sequence of ultrasound image frames to produce a set of classification vectors representing the first set of clinical characteristics; and identify a clinical condition of the subject body by applying a second predictive network to the set of classification vectors.

Abstract: Embodiments discussed herein facilitate determination of whether lesions are benign or malignant. One example embodiment is a method, comprising: accessing medical imaging scan(s) that are each associated with distinct angle(s) and each comprise a segmented region of interest (ROI) of that medical imaging scan comprising a lesion associated with a first region and a second region; providing the first region(s) of the medical imaging scan(s) to trained first deep learning (DL) model(s) of an ensemble and the second region(s) of the medical imaging scan(s) to trained second DL model(s) of the ensemble; and receiving, from the ensemble of DL models, an indication of whether the lesion is a benign architectural distortion (AD) or a malignant AD.

Abstract: An information processing apparatus comprises a generating unit configured to generate supervised data relating to a topological invariant based on a topological property relating to supervised data corresponding to input data, and a training unit configured to perform, based on a geometric property relating to an output from a classifier to which the input data is input and the supervised data generated by the generating unit, training of the classifier.

Abstract: Embodiments relate to circuitry for temporal processing and image fusion. An image fusion circuit receives captured images, and generates corresponding image pyramids. The generated image pyramids are raster or tiled processed, and stored in memory. A fusion module receives a first and second image pyramids from the memory, and warps and fuses image pyramids to generate a fused image pyramid, which may be used for further processing, and may also be stored back into the memory. The image fusion circuitry is configurable to operate in a plurality of different configuration modes corresponding to different image fusion applications for fusing image pyramids of received images, including two-frame fusion, temporal filtering, infinite impulse response (IIR) temporal processing, and/or finite impulse response (FIR) temporal processing.

Abstract: A method of screening for a cancer in a patient, an integrated method of screening for a cancer in a patient, a computer program product for carrying out the methods, as well as a medical imaging apparatus for carrying out the methods are disclosed. Based upon the above, it is possible that the patient will obtain a more reliable result from a first medical imaging treatment.