Abstract: Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos is provided. In various embodiments, a plurality of medical images containing embedded text are read. The plurality of medical images are clustered into a plurality of clusters. Medical images of a first cluster of the plurality of clusters are ranked based on the frequency of measurement names within the medical images of the first cluster. A candidate tabular template is generated indicative of a layout of measurement name/value pairs within the medical images of the first cluster. According to the candidate tabular template, optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements. The candidate tabular template and the candidate measurements are presented to a user.

Abstract: Embodiments of the present disclosure provide a method for capturing a fingerprint and associated products. The method includes determining a first luminance based on a currently running application and controlling light-emitting units for a first area to emit light with the first luminance. The method further includes capturing the fingerprint when the light with the first luminance illuminates the first area.

Abstract: An image processing method includes performing an object identification process on a plurality of image data, generating layout data on the basis of at least some of the plurality of image data subjected to the identification process and template data, controlling displaying of a layout image on a display on the basis of the layout data, acquiring setting information relating to a priority of a particular object, and extracting a replacement candidate image in response to a request for replacing an image in the displayed layout image. In extracting a replacement candidate image, the replacement candidate image is extracted on the basis of the setting information relating to a priority.

Abstract: Image processing of an image is used to determine the opportunity to view an object. Rather than relying on simple numbers passing an object, the opportunity to view the object is weighted based on attention, which is derived from other objects competing for attention. For the processor to more accurately determine opportunity to view as compared to using geometric information alone, a machine-learned network is used. To deal with changes in obstructions, another machine-learned network may extract obstructions from camera images. Trace data is used to allow for daily variation in base counts of viewers, allowing greater temporal resolution and determination based on information more recently acquired than counts.

Abstract: A method and an apparatus for determining existence of a foreign material on a surface of a fingerprint sensor of a terminal are provided. The method includes the following. A current capacitance value of the fingerprint sensor of the terminal is obtained. A first difference between the current capacitance value and a preset capacitance reference value is calculated. Determine that the foreign material is present on the surface of the fingerprint sensor when the first difference is within a preset foreign-material threshold range. The preset capacitance reference value is a capacitance value tested with no foreign material on the surface of the fingerprint sensor before leaving the factory and is stored in a system in advance. The preset foreign-material threshold range is set in advance according to experimental data of capacitance-change tests performed on the fingerprint sensor when different conductive foreign materials are on the surface of the fingerprint sensor.

Abstract: Provided are mechanisms and processes for performing skeleton detection and tracking via client-server communication. In one example, a server transmits a skeleton detection message that includes position data for a skeleton representing the structure of an object depicted in a first video frame in a raw video stream at a client device. Based on the initial position data, a processor identifies intervening position data for the skeleton in one or more intervening video frames that are temporally located after the first video frame in the raw video stream. A filtered video stream is then presented by altering the raw video stream based at least in part on the first position data and the intervening position data.

Abstract: Systems, methods, and apparatus for identifying and tracking UAVs including a plurality of sensors operatively connected over a network to a configuration of software and/or hardware. Generally, the plurality of sensors monitors a particular environment and transmits the sensor data to the configuration of software and/or hardware. The data from each individual sensor can be directed towards a process configured to best determine if a UAV is present or approaching the monitored environment. The system generally allows for a detected UAV to be tracked, which may allow for the system or a user of the system to predict how the UAV will continue to behave over time. The sensor information as well as the results generated from the systems and methods may be stored in one or more databases in order to improve the continued identifying and tracking of UAVs.

Abstract: A method includes provisioning a set of training data sets, each of the training data sets respectively including an acquisition data set; generating a first medical image for each of the training data sets of at least one first subset of the set of training data sets using the image reconstruction algorithm based on a respective acquisition data set; determining an image processing result for each of the respective first medical images using an image processing algorithm based on the respective first medical image; determining image processing information for each of the respective first medical images relating to a quality of the respective image processing result based on the respective image processing result; and optimizing the image reconstruction algorithm based on a first machine learning algorithm, the at least one first subset of the set of training data sets and the image processing information for the respective first medical images.

Abstract: A method includes passing an original text document through distortion filter generators to generate a training dataset that includes distorted text documents. Each distortion filter generator is configured to distort words or letters of words in phrases of text of a facsimile image in a respective unique manner. A neural network model is trained to recognize each respective distortion and match each respective distortion with each respective distortion filter generator based on the training dataset and the original text document. Image data of one facsimile having at least one text distortion is received and inputted to the trained neural network model. The output of the trained neural network model is coupled to an input of an optical character recognition (OCR) engine. The trained neural network model and the OCR engine convert the received image data of the incoming facsimile corrected for the at least one text distortion to machine-encoded text.

Abstract: A method of object surface matching includes identifying an object in-flight in an image; identifying a feature on the object that is in a first spatial position; comparing the feature with set of template images; identifying a first template image in the set of template images that matches the feature on the object that is in the first spatial position; determining first coordinates for the first spatial position based on the first template image; identifying a second image of the object that includes the feature on the object that is in a second spatial position; identifying a second template image in the set of template images that matches the feature on the object that is in the second spatial position; determining second coordinates for the second spatial position based on the second template image; and generating a spin value for the object based on the first and second coordinates.

Abstract: A method of object detection includes receiving a first image taken from a first perspective by a first camera and receiving a second image taken from a second perspective, different from the first perspective, by a second camera. Each pixel in the first image is offset relative to a corresponding pixel in the second image by a predetermined offset distance resulting in offset first and second images. A particular pixel of the offset first image depicts a same object locus as a corresponding pixel in the offset second image only if the object locus is at an expected object-detection distance from the first and second cameras. The method includes recognizing that a target object is imaged by the particular pixel of the offset first image and the corresponding pixel of the offset second image.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a document image with objects and identifying microblocks corresponding to each object. Analyzing a position of a microblock for collinearity with another microblock based on respective positional characteristics and adjustable collinearity parameters. Collinear microblocks are identified into a macroblock and computational data of a key-value pair is created from the macroblock. A heuristic confidence level is associated with the key-value pair. Also based on data cluster formation, a table may be classified and data extracted.

Abstract: Methods and robots for adjusting object detection parameters, object recognition parameters, or both object detection parameters and object recognition parameters are disclosed. Methods include receiving image data, automatically recognizing an object with an object recognition module based on the image data, determining whether a pose estimation error has occurred, and adjusting at least one object recognition parameter when the pose estimation error has occurred. Methods include receiving image data and automatically detecting a candidate object with an object detection module based on the image data, recognizing an object with an object recognition module based on the detected candidate object, determining whether an object recognition error has occurred, and adjusting the at least one object detection parameter when the object recognition error has occurred.

Abstract: According to one implementation, an image rendering system includes a computing platform having a hardware processor and a system memory storing an image denoising software code. The hardware processor executes the image denoising software code to receive an image file including multiple pixels, each pixel containing multiple depth bins, and to select a pixel including a noisy depth bin from among the pixels for denoising. The hardware processor further executes the image denoising software code to identify a plurality of reference depth bins from among the depth bins contained in one or more of the pixels, for use in denoising the noisy depth bin, and to denoise the noisy depth bin using an average of depth bin values corresponding respectively to each of the reference depth bins.

Abstract: Sensor data is provided to a deep neural network previously trained to detect a feature within the physical environment. Result signals are received from the neural network, and the computing system determines if the feature is present within the physical environment based on the result signals. Responsive to determining that the feature is present, the computing system implements a function of a rule assigned to the feature. Responsive to determining that the feature is not present, the computing system determines whether one or more activation parameters of the neural network have been met indicative of an alternative feature being present within the physical environment. An indication that the activation parameters have been met is output by the computing system, enabling the rule to be extended to the alternative feature.

Abstract: A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.

Abstract: A shape recognition device that recognizes a shape of an object having an indefinite shape and flexibility, and assembled by a robot, the shape recognition device including: an imaging unit that images the object; an image processing unit that recognizes the shape of the object on the basis of the object imaged by the imaging unit; and a simulation processing unit that simulates the shape of the object on the basis of the image of the object imaged by the imaging unit. The simulation processing unit interpolates a recognition result of the shape of the object by the image processing unit, on the basis of a simulation result of the shape of the object.

Abstract: Methods and apparatuses are described for of recognizing handwritten characters in digital images using context-based machine learning. A server captures an image of a document that comprises one or more handwritten data fields, the document associated with a user identifier. The server identifies a field type for each handwritten data field in the image. The server creates a pixel intensity array for each character in each handwritten data field and determines whether a user-specific character map exists for the user identifier. If a map exists, the server retrieves the map and generates digital form data by executing a user-specific handwriting classifier using the map, the pixel intensity arrays, and the field types. If a map does not exist, the server builds a map based upon the pixel intensity arrays and generates digital form data by executing a baseline handwriting classifier using the map, the pixel intensity arrays, and the field types.

Abstract: Disclosed herein are system, method, and computer program product embodiments for verifying a document. An embodiment operates by receiving an initial image of a document and identifying a first imperfection on a feature of the document. Thereafter, the feature of the initial image is segmented into a first and second partially overlapping segment corresponding to a first and second portion of the feature, respectively. Subsequently, the feature's first portion is determined to be free from the first imperfection, and the first imperfection is identified in the feature's second segment. After receiving a second image of the document including the feature's second portion, the feature's second portion in the second image is determined to be free from a second imperfection. As a result, an image of the feature is created based on the first and second portions of the feature derived from the initial and second images, respectively.

Abstract: An image-processing apparatus includes a first-type of sensor, a second-type of sensor, and a control circuitry. The control circuitry receives an input color image frame of a sequence of color image frames from the first-type of sensor and a corresponding input depth image from the second-type of sensor. The control circuitry generates a first foreground mask of an object-of-interest for the input color image frame. The control circuitry then detects a first set of pixels in the generated first foreground mask as misclassified image pixels. The control circuitry updates the background color image and the background depth image of the scene. The control circuitry precisely extracts the object-of-interest from the input color image frame, based on at least the updated background color image and the background depth image of the scene.