Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a parameter archival storage system for image processing models. The system is configured for read-optimized compression storage of machine-learning neural-network based image processing models with reduced storage by separately storing weight filter bits. The system is configured to construct weigh parameter objects associated with the plurality of neural network layers of an image processing model, such that the image processing model can be reconstructed from the weigh parameter objects. The system may discard the hierarchical linked architecture of the second image processing model and store the second image processing model at the at least one hosted model versioning system repository by storing only the weigh parameter objects.

Abstract: Embodiments of the present disclosure relate to a scanning device comprising a support base, a pressure panel disposed on an upper surface of the support base, and a plurality of cameras distributed around an outer perimeter of the support base.

Abstract: A method of deblurring an observed image acquired by an image sensor in order to determine an observable image corresponding to the deblurred observed image. The observable image and the observed image each are formed by a set of pixels defined by at least one numerical value. The method involves expressing the observable image as a solution minimizing a Tikhonov functional defined by the observed image, applying a spreading function at the point of the image sensor and applying a wavelet transform operator. The Tikhonov functional is expressed as a sum of at least two terms. The method further involves determining the observable image by implementing an iterative projection algorithm released on a closed convex, at each iteration of which, the successive application of a projection operator associated with each of the terms of the Tikhonov functional.

Abstract: System, methods, and other embodiments described herein relate to training a depth model for monocular depth estimation. In one embodiment, a method includes generating, as part of training the depth model according to a supervised training stage, a depth map from a first image of a pair of training images using the depth model. The pair of training images are separate frames depicting a scene from a monocular video. The method includes generating a transformation from the first image and a second image of the pair using a pose model. The method includes computing a supervised loss based, at least in part, on reprojecting the depth map and training depth data onto an image space of the second image according to at least the transformation. The method includes updating the depth model and the pose model according to at least the supervised loss.

Abstract: Methods, devices, and computer-program products are provided for adding data to a digital video signal in a visually imperceptible manner. For example, a region of pixels can be identified in a video frame, and one or more pixel characteristics of the region of pixels can be determined. Based on the one or more pixel characteristics of the region of pixels, at least one pixel characteristic of a subset of pixels from the region of pixels can be modified in order to encode a set of data into the region of pixels. An output frame can be provided that includes the modified at least one pixel characteristics for the subset of pixels.

Abstract: A method for security and/or automation systems is described. In one embodiment, the method includes identifying image data from a signal, analyzing the image data based at least in part on a first parameter, identifying a presence of an object based at least in part on the analyzing, and detecting an object event based at least in part on the identifying.

Abstract: An apparatus and method for determining image sharpness is provided. According to one embodiment, an apparatus includes a weight device configured to determine a weight map of a reference image; an image sharpening device configured to sharpen the reference image using at least one sharpening method; an edge activity map device connected to the image sharpening device and configured to determine a first edge activity map ?(x, y) for each sharpened image of reference image by the at least one sharpening method; and an edge sharpness metric device connected to the weight device and the first edge activity map device and configured to determine an edge sharpness metric (ESM) for each sharpened image of the reference image by the at least one sharpening method based on the weight map and the edge activity map for each sharpened image of the reference image by the at least one sharpening method.

Abstract: A digital identity, which may include a user interface that may be displayed on a mobile computing device, may be generated to include information extracted from a physical identification card (e.g., driver license or passport), as well as information regarding validation of the physical identification card and of the consumer's identity. The digital identity may be used in place of the physical identification card.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, a normalization device is configured to facilitate normalization of medical images of a coronary region of a subject for an algorithm-based medical imaging analysis. The normalization device can include a substrate and a plurality of compartments positioned within the substrate, each of the plurality of compartments configured to hold a sample of a known material. A first subset of the plurality of compartments can hold at least one sample of a contrast material, a second subset of the plurality of compartments can hold samples of materials representative of materials to be analyzed by the algorithm-based medical imaging analysis, and a third subset of the plurality of compartments hold at least one sample of a phantom material.

Abstract: Point clouds of objects are compared and matched using logical arrays based on the point clouds. The point clouds are azimuth aligned and translation aligned. The point clouds are converted into logical arrays for ease of processing. Then the logical arrays are compared (e.g. using the AND function and counting matches between the two logical arrays). The comparison is done at various quantization levels to determine which quantization level is likely to give the best object comparison result. Then the object comparison is made. More than two objects may be compared and the best match found.

Abstract: Provided is an analysis apparatus (10) including a person extraction unit (11) that analyzes video data to extract a person, a time calculation unit (12) that calculates a continuous appearance time period for which the extracted person has been continuously present in a predetermined area and a reappearance time interval until the extracted person reappears in the predetermined area for each extracted person, and an inference unit (13) that infers a characteristic of the extracted person on the basis of the continuous appearance time period and the reappearance time interval.

Abstract: A facial recognition method is disclosed that is executed by a system comprising an image capture device and a light source able to illuminate a face positioned in front of the device. A plurality of images of a face is acquired, the light source producing a different illumination of the face at each image acquisition, and values representing the face from the plurality of images are extracted. To this aim, a first image of a face is acquired. An SNR value, referred to as the estimated value, of the first image is estimated from a reference SNR value, from image acquisition parameters and from values representing distances between the face and said device. Finally, the estimated value is used in order to determine whether or not the recognition procedure should be executed.

Abstract: Techniques are disclosed for generating tutorial recommendations to users of image editing applications, based on image content. A methodology implementing the techniques according to an embodiment includes using neural networks configured to determine subject matter of a user provided image and to identify objects in the image. The method also includes selecting one or more proposed tutorials from a database of tutorials. The database is indexed by tutorial subject matter and tutorial object content, and the selection is based on a matching of the determined subject matter to the tutorial subject matter and a matching of the identified objects to the tutorial object content. The method further includes calculating an effectiveness score associated with each of the proposed tutorials, the effectiveness score based on application of the proposed tutorial to the image. The method further includes sorting the proposed tutorials for recommendation to the user based on the effectiveness scores.

Abstract: Methods, devices, and computer-program products are provided for adding and decoding data to a digital video signal in a visually imperceptible manner. For example, an encoded video frame can be obtained, and one or more blocks of the encoded video frame can be decoded. Binary data can be added to a subset of pixels from a set of pixels of the one or more blocks. For instance, a pixel component can be modulated to add the binary data. The one or more blocks can be re-encoded using at least one coding mode. The re-encoded one or more blocks can be added to the encoded video frame.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: A method, a device, a terminal and a storage medium for acquiring slant value of slant image are provided. The method for acquiring slant value of a slant image comprises: analyzing the slant image and acquiring coordinate information of a plurality of boundary lines of the slant image; acquiring first slant values of the boundary lines by analysing and calculating the coordinate information; acquiring a correction value; calculating difference values between the first slant values and the correction value respectively; determining the first slant value corresponding to the minimum difference value as the slant value of the slant image. The technical solution of the present disclosure can uniquely determine a slant value of slant image.

Abstract: Medical software tools platforms utilize a surgical display to provide access to specific medical software tools, such as medically-oriented applications or widgets, that can assist surgeons or surgical team in performing various procedures. In particular, an endoscopic camera may register the momentary rise in the optical signature reflected from a tissue surface and in turn transmit it to a medical image processing system which can also receive patient heart rate data and display relevant anomalies. Changes in various spectral components and the speed at which they change in relation to a source of stimulus (heartbeat, breathing, light source modulation, etc.) may indicate the arrival of blood, contrast agents or oxygen absorption. Combinations of these may indicate various states of differing disease or margins of tumors, and so forth. Also, changes in temperatures, physical dimensions, pressures, photoacoustic pressures and the rate of change may indicate tissue anomalies in comparison to historic values.

Abstract: The system is for confirming that a user of a portable terminal device has viewed posted material in a plurality of places by visiting one of the posted places, the system including the portable terminal device and a server, the device including a portable-terminal control unit, a portable-terminal communication unit, an image capturing unit, a portable-terminal storage unit, and a position-information obtaining unit, the server including a server control unit, a server communication unit, and a server storage unit that stores authenticated images about the posted material in the individual posted places in association with position information of the posted places, wherein the portable-terminal control unit sends a viewing confirmation request including a viewed image, the normalization information, and the portable-terminal position information to the server by using the portable-terminal communication unit, and the server control unit determines whether the viewed image is valid on the basis of the request

Abstract: The embodiments of the present disclosure provide a processing method and apparatus for vehicle scene sequence tracking, and a vehicle. The method includes: obtaining a current vehicle speed of a vehicle driving in a first area and at least one frame of a current image taken; determining a vehicle speed ratio interval and a length of a sequence to be tracked according to the current vehicle speed; and performing the scene sequence tracking according to the vehicle speed ratio interval, the length of the sequence to be tracked, the at least one frame of the current image, and pre-stored multiple frames of historical reference images of the vehicle, to obtain position information of the vehicle. The method ensures that an accurate speed scanning range can still be quickly found when the vehicle speed changes greatly, and the speed scanning efficiency is greatly improved when the vehicle speed is relatively stable.

Abstract: High definition, color images, animations, and videos for diagnostic and personal imaging applications are described along with methods, devices and systems for creating the images, as well as applications for using the images, animations and videos.