Abstract: A machine learning model may be trained to denoise an image. The machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. The machine learning model may include a recurrent neural network. The machine learning model may have a modular architecture including one or more building units. The machine learning model may have a multi-branch architecture. The noise may be identified and removed from the image by an iterative process.

Abstract: Disclosed herein are an apparatus, method, and computer-readable medium for detecting an anomalous behavior event for an environment and transmitting an alert of the event. An implementation may comprise detecting, in a plurality of image frames captured by a camera, when a person enters a region of interest, tracking movements of the person in the region of interest by comparing images of the person in the plurality of image frames, determining attributes of the person, determining environmental contexts for the region of interest, determining whether or not an anomalous behavior is detected based on the movements and the attributes of the person and the environmental contexts, generating an alert when the anomalous behavior is detected, and transmitting the generated alert to user devices of one or more predetermined recipients.

Abstract: Methods, systems, and techniques for utilizing image processing systems to measure damage to vehicles include utilizing an image processing system to generate a heat map of an image of a damaged vehicle, where the heat map is indicative of a damaged area of the vehicle, and determining at least one measurement of the damaged area based on the heat map and a depth of field indicator corresponding to the image. In some embodiments, the image processing system also determines one or more types of damage of the damaged area, and/or also generates a segmentation map of the depicted vehicle and utilizes the segmentation map in conjunction with the heat map to measure damaged areas and locations thereof on the vehicle depicted within the image. In some embodiments, the techniques include determining the depth of field indicator of the image or portions thereof.

Abstract: Systems, apparatuses, and methods for more efficiently and effectively determining the accuracy with which a human evaluates a set of data, as this may reduce the error in the assessment of a model's performance. This can be helpful in situations where human inputs are used to confirm the output of a machine learning generated classification and in situations where it is desired to evaluate the accuracy of data that may have been labeled or annotated by a human curator. This may assist in reducing the need for new validation/test data when evaluating a new model. The system and methods described can be used to evaluate the accuracy of a trained Machine Learning (ML) model, and as a result, allow a comparison between models based on different ML algorithms.

Abstract: A method and computing apparatus for extracting information from a document are provided. The method includes receiving a document, extracting data from the document, assigning the document to a category from among a predetermined plurality of categories based on a result of the extracted data, and generating a structured output by formatting the extracted data based on the assigned category.

Abstract: A mobile radiation generation apparatus that is used for a first radiography system configured to capture a first radiographic image, has a radiation generation unit including a radiation tube configured to emit radiation, is movable with a carriage having wheels, and is driven with a battery includes at least one processor configured to execute first reception processing of receiving the first radiographic image, first computer aided diagnosis processing of executing computer aided diagnosis processing on the first radiographic image, second reception processing of receiving a second radiographic image from a second radiography system different from the first radiography system, second computer aided diagnosis processing of executing the computer aided diagnosis processing on the second radiographic image, and return processing of returning a result of the second computer aided diagnosis processing to the second radiography system.

Abstract: An image attack detection method includes: acquiring an image-to-be-detected, and performing global classification recognition based on the image-to-be-detected to obtain a global classification recognition result; performing local image extraction randomly based on the image-to-be-detected to obtain a target number of local images, the target number being obtained by calculation according to a defensive rate of a reference image corresponding to the image-to-be-detected; performing local classification recognition based on the target number of local images respectively to obtain respective local classification recognition results, and fusing the respective local classification recognition results to obtain a target classification recognition result; and detecting a similarity between the target classification recognition result and the global recognition result, and determining the image-to-be-detected as an attack image when the target classification recognition result and the global classification recognit

Abstract: An image recognition method includes: receiving an input image of a first quality; extracting an input feature of a second quality of the input image from the input image by inputting the input image to an encoding model in an image recognizing model; and generating a recognition result for the input image based on the input feature.

Abstract: An image processing apparatus includes: a memory; and a processor configured to execute one or more instructions stored in the memory to: determine a scale weight for each pixel of original pixels in an original image based on first attribute-related information set by dividing the original image into an object region and a peripheral region, respectively; determine an additional weight for at least one pixel of the original pixels in the peripheral region, based on the first attribute-related information, and second attribute-related information that is based on an amount of a change between pixel values of adjacent pixels; and obtain a transformed image of which a size is changed from the original image, by applying at least one of the scale weight and the additional weight to corresponding pixels of the original pixels to obtain a pixel value of a transformed pixel in the transformed image.

Abstract: Embodiments described herein relate to methods, devices, and computer-readable media to determine a compression setting. An input image may be obtained where the input image is associated with a user account. One or more features of the input image may be determined using a feature-detection machine-learning model. A compression setting for the input image may be determined using a user-specific machine-learning model personalized to the user account based on the one or more features in the input image. The input image may be compressed based on the compression setting.

Abstract: A method for detection of three-dimensional (3D) objects on or around a roadway by machine learning, applied in an electronic device, obtains images of road, inputs the images into a trained object detection model, and determines categories of objects in the images, two-dimensional (2D) bounding boxes of the objects, and parallax (rotation) angles of the objects. The electronic device determines object models and 3D bounding boxes of the object models and determines distance from the camera to the object models according to size of the 2D bounding boxes, image information of the detection images, and focal length of the camera. The positions of the object models in a 3D space can be determined according to the rotation angles, the distance, and the 3D bounding boxes, and the positions of the object models are taken as the position of the objects in the 3D space.

Abstract: A method for recognizing arteries and veins on a fundus image includes: executing a pre-process operation on the fundus image, so as to obtain a pre-processed fundus image; generating a fundus spectral reflection dataset associated with pixels of the pre-processed fundus image, based on the pre-processed fundus image, and a spectral transformation matrix; obtaining a plurality of principle component scores associated with the pixels of the pre-processed fundus image, respectively; and determining, for each of the pixels of the pre-processed fundus image that has been determined as a part of a blood vessel, whether the pixel belongs to a part of an artery or a part of a vein.

Abstract: A controller using image processing to identify produce is disclosed herein. The controller may include one or more memories and one or more processors communicatively coupled to the one or more memories. In some implementations, a controller may receive a trigger associated with presence of an item at a product processing zone. The controller may capture, via an imager, an image representing the item. Accordingly, the controller may apply a first convolutional neural network (CNN) to the image to determine a bounding box associated with the item. The controller may determine that the item within the bounding box satisfies an occlusion threshold and may apply a second CNN to the image to determine a query image representation. Accordingly, the controller may receive, from a database, an indication of one or more candidate items based on the query image representation and indicate, via a user interface, the candidate item(s).

Abstract: Systems and methods of image reconstruction are disclosed. A positron emission tomography (PET) dataset is acquired from a PET imaging modality and a magnetic resonance imaging (MRI) dataset is obtained from an MRI modality. The MRI dataset is registered to the PET dataset. An MRI reconstructed image is generated from the MRI dataset and is registered to the PET dataset. An iterative reconstruction process is applied to the PET dataset using the MRI reconstructed image as guidance. The iterative reconstruction process includes one or more similarity coefficients and a spatially variant adaptive hyperparameter is calculated for each iteration of the iterative reconstruction process. A reconstructed image is output from the iterative reconstruction process.

Abstract: A check processing system detects fraud in real-time by comparing data from presented checks against known check data stored in a Global Check Register (GCR). The system analyzes check images and extracts payee information. Instead of reviewing the entire check for each item of information, a region of interest (ROI) is investigated. The use of an ROI defeats a type of fraud wherein original payee information is placed on the memo line of the check with the payee information changed to a fraudulent payee name. By validating the presented check data against known data points, the system can identify discrepancies and potential fraud in real-time, generating events and notifications for bank employees to take appropriate action in a timely manner.

Abstract: A sensor device is described herein. The sensor device includes a multi-dimensional optical sensor and processing circuitry, wherein the multi-dimensional optical sensor generates images and the processing circuitry is configured to output data that is indicative of hemodynamics of a user based upon the images. The sensor device is non-invasive, and is able to be incorporated into wearable devices, thereby allowing for continuous output of the data that is indicative of the hemodynamics of the user.

Abstract: An apparatus to facilitate video motion smoothing is disclosed. The apparatus comprises one or more processors including a graphics processor, the one or more processors including circuitry configured to receive a video stream, decode the video stream to generate a motion vector map and a plurality of video image frames, analyze the motion vector map to detect a plurality of candidate frames, wherein the plurality of candidate frames comprise a period of discontinuous motion in the plurality of video image frames and the plurality of candidate frames are determined based on a classification generated via a convolutional neural network (CNN), generate, via a generative adversarial network (GAN), one or more synthetic frames based on the plurality of candidate frames, insert the one or more synthetic frames between the plurality of candidate frames to generate up-sampled video frames and transmit the up-sampled video frames for display.

Abstract: A filter process is performed on point cloud data using a representative value of the point cloud data for each local region obtained by dividing a three-dimensional space. A two-dimensional plane image on which the point cloud data subjected to the filter process is projected is encoded, and a bitstream is generated. The filter process can be performed by, for example, an information processing apparatus, an image processing apparatus, electronic equipment, an information processing method, a program, or the like.

Abstract: A computer-implemented method for facilitating opportunistic screening for cardiomegaly includes obtaining a set of computed tomography (CT) images. The set of CT images captures at least a portion of a heart of a patient, and the set of CT images is captured for a purpose independent of assessing cardiomegaly. The method further includes using the set of CT images as an input to an artificial intelligence (AI) module configured to determine a heart measurement based on CT image set input. The method also includes obtaining heart measurement output generated by the AI module and, based on the heart measurement output, classifying the patient into one of a plurality of risk levels for cardiomegaly. The classification is operable to trigger additional action based on the corresponding risk level for the patient.

Abstract: The present invention discloses a denoising model of Monte Carlo rendering based on a Generative Adversarial Network (GAN) and its construction method, including: constructing a training sample and constructing a Generative Adversarial Network (GAN), including Denoising Net and Critic Net, wherein Denoising Net is used to denoise the input noise rendering image and auxiliary features, and output the denoising rendering image, and Critic Net is used to classify the input denoising rendering image and the target rendering image corresponding to the noise rendering image, and output the classification result. The training samples are used to tune the network parameters of the Generative Adversarial Network (GAN). After the tuning is completed, the denoising network determined by the network parameters is used as the Monte Carlo rendering image denoising model. A denoising method and device for the Monte Carlo rendering image are also disclosed, which can realize the denoising of noisy Monte Carlo renderings.