Abstract: Systems, devices, media, and methods are presented for segmenting an image of a video stream with a client device, identifying an area of interest, generating a modified area of interest within one or more image, identifying a first set of pixels and a second set of pixels, and modifying a color value for the first set of pixels.

Abstract: An information processing apparatus includes a processor, and a memory storing a program. The program, when executed by the processor, causes the information processing apparatus to: acquire a medical image obtained by imaging a subject, acquire an inspection result of the medical image, perform inference on the medical image; and control outputting of an inference result of the medical image inference to an external device based on the inspection result of the medical image.

Abstract: The present disclosure discloses methods and systems for detecting tampering of an infrared (IR) security mark in a document. The method includes receiving the document including the IR security mark, wherein the IR security mark further includes one or more security texts and/or images. The document is scanned to generate scanned data. A portion of the scanned data including the IR security mark is segmented into a plurality of blocks such as blocks of size 32*32. Thereafter, a ratio of white to black pixels is calculated for each block. The calculated ratio is compared with a known threshold for each block. Upon comparison, the IR security mark is detected as a tampered security mark.

Abstract: An image view angle conversion method includes: model training data are obtained, the model training data including planar images of a training object at a plurality of different view angles and labels corresponding to respective view angles, where the labels corresponding to the different view angles are different. A pre-designed generative adversarial network model is trained according to the model training data to obtain a view angle conversion network model. A planar image of a target object and labels corresponding to one or more expected view angles of the target object are input into the view angle conversion network model, so that the view angle conversion network model generates planar images of the target object at the expected view angles.

Abstract: A method for training a neural network.

Abstract: System and method of more efficiently identifying and segmenting anatomical structures from 2-D cone beam CT images, rather than from reconstructed 3-D volume data, is disclosed. An image processing system receives, from a cone beam CT device, at least one 2-D x-ray image, which is part of a set of x-ray images taken from a 360 degree scan of a patient with a cone beam CT imaging device. The x-ray image contains at least one anatomical structure such as vertebral bodies to be segmented. The received x-ray is then analyzed in order to identify and segment the anatomical structure contained in the x-ray image based on a stored model of anatomical structures. Once the 360 degree spin is completed, a 3-D image volume from the x-ray image set is created. The identification and segmentation information derived from the x-ray image is then added to the created 3-D image volume.

Abstract: A method is described that includes obtaining a multicomponent seismic data set for a subterranean region of interest and determining, using a computer processor, a PP stacked time-domain seismic image and a PS stacked time-domain seismic image from the multicomponent seismic data set. The method further includes transforming a recording-time axis of at least one of the PP stacked time-domain seismic image and the PS stacked time-domain seismic image to produce a pair of coarsely-registered PP and PS seismic images and filtering at least one of the pair to produce a pair of spectrally-matched PP and PS seismic images. Further, the method includes dynamically warping at least one of the pair of spectrally-matched PP and PS seismic images to produce a pair of fully-registered PP and PS seismic images.

Abstract: Provided is a method and a device for correcting lateral chromatic aberration, a storage medium and a computer equipment. The method includes acquiring system parameters and pre-stored calibration data, obtaining the lateral chromatic aberration of a camera to be corrected by calculating the system parameters, and correcting the LCA by the calibration data. The lateral chromatic aberration of the camera lens may be corrected in terms of hardware and costs are saved.

Abstract: Examples described herein include a graphics processing apparatus that includes a memory device and a display engine coupled to the memory device. The display engine is configured to apply dither on a region of an image and modify a first pixel of an image stored in the memory based on pseudo-random noise to reduce color banding impressions. In some examples, the pseudo-random noise is based on one or more of: local brightness estimation and one or more prior noise levels. In some examples, the display engine is to determine the local brightness estimation based on an average brightness of a pixel region surrounding the first pixel and the first pixel. In some examples, the display engine is configured to bound the pseudo-random noise based on a noise applied to a pixel in a same position as that of the first pixel in a prior frame within a same scene.

Abstract: A system and method to generate flood inundation maps in near real time. The system includes a plurality of computer processing modules: a flood trigger system, a SAR data query system, and a RAPID kernel algorithm system, running in real time, to identify the potential flood zones, query SAR data, and finally compute the inundation maps, respectively. As disclosed herein, the RAPID kernel algorithm is extended to a fully automated flood mapping system that requires no human interference from the initial flood events discovery to the final flood map production.

Abstract: An authentication apparatus and a method to devise an authentication tool is provided for facilitating determination of authenticity or genuineness of an article with reference to a captured image or a purported primary image of an information bearing device on the article. The authenticity or genuineness of an article is determined with reference to whether a captured image is a primary image of an authentic information bearing device using a trained neural network. The neural network is trained using training images comprising imperfect images having varying degrees of image imperfections which are introduced under controlled conditions.

Abstract: A method of inspection and an inspection system for the film deposition process for substrates that includes glass and wafer are disclosed. The inspection system includes multiple camera modules positioned in a load lock unit of a process chamber, such as the camera modules that can capture images of the substrate in the load lock. The images are analyzed by a controller of the inspection system to determine the accuracy of robots in handling the substrate, calibration of the robots based on the analysis, and defects in the substrate caused during the handling and deposition process.

Abstract: An image processing apparatus comprises a setting unit configured to set, based on a detection line that is set in a display screen, a patch region in the display screen, the patch region including a detection region serving as a region for detecting a position of an object, and a counting region encompassed by the detection region, and a counting unit configured to count, from among intersection points between a movement vector in the detection region of an object moving inside the display screen or a locus based on the movement vector and the detection line in the detection region, intersection points located in the counting region.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: A cell sorter includes a base for holding a cell culture plate containing a fluorescently labeled sample of cells, a fluorescence imager for viewing the cell culture plate, through bottom of the cell culture plate, to capture one or more fluorescence images of the fluorescently labeled sample of cells, and a cell extraction module for extracting a cell selected based on the one or more fluorescence images. The cell extraction module includes a needle for hydraulically removing the selected cell from the cell culture plate, and a motorized translation stage for translating the needle in a z-dimension to reach the selected cell from above. The cell sorter further includes a motorized translation stage for translating one of the needle and the cell culture plate in x- and y-dimensions, relative to the other one of the needle and the cell culture plate, to position the needle over selected first cell.

Abstract: A method for diagnosing one or more diseases of the respiratory tract for a patient including the steps of: acquiring cough sounds from the patient; processing the cough sounds to produce cough sound feature signals representing one or more cough sound features from the cough segments; obtaining one or more disease signatures based on the cough sound feature signals; and classifying the one or more disease signatures to deem the cough segments as indicative of one or more of said diseases; wherein the step of obtaining the one or more disease signatures based on the cough sound feature signals includes applying the cough sound features to each of one or more pre-trained disease signature decision machines, each said decision machine having been pre-trained to classify the cough sound features as corresponding to either a particular disease or to a non-disease state or as corresponding to first particular disease or a second particular disease different from the first particular disease.

Abstract: An imaging apparatus, an imaging control method, and an imaging control program are provided. A digital camera includes an imaging element including a first light-receiving region in which a pixel group including focal point detection pixels is arranged and a second light-receiving region in which a pixel group not including the focal point detection pixels is arranged, and a system control unit, in which the system control unit performs a recording imaging control of causing the imaging element to perform imaging in a first exposure time period and a display imaging control of causing the imaging element to perform imaging by setting an exposure time period of the second light-receiving region as the first exposure time period and setting an exposure time period of the first light-receiving region as a second exposure time period.

Abstract: An OCT apparatus of an exemplary aspect includes an OCT scanner, image data generating unit, registration unit, and image data composing unit. The OCT scanner is configured to acquire three dimensional data sets by applying an OCT scan to mutually different three dimensional regions of a sample. The image data generating unit is configured to generate a plurality of pieces of image data from the three dimensional data sets. The registration unit is configured to perform a first registration between the plurality of pieces of image data by applying an image correlation calculation to each of overlap regions in the plurality of pieces of image data. The image data composing unit is configured to compose the plurality of pieces of image data based on a result of the first registration.

Abstract: A computer device obtains a first face image (IMA) and a second face image (IFA). The device obtains M first image blocks corresponding to facial features from the first face image (IMA), and obtains N second image blocks corresponding to facial features from the second face image (IFA). The device transforms the M first image blocks and the N second image blocks to a feature space to generate M first feature blocks and N second feature blocks. The device selects a subset of the first feature blocks and a subset of the second feature blocks according to a specified control vector. The device generates a first composite feature map based the selected subset of the first feature blocks and the selected subset of the second feature blocks. The device inversely transforms the first composite feature map back to an image space to generate a third face image.

Abstract: Introduced here is a technique to create small compressed image files while preserving data quality upon decompression. Upon receiving an uncompressed data, such as an image, a video, an audio, and/or a structured data, a machine learning model identifies an object in the uncompressed data such as a house, a dog, a text, a distinct audio signal, a unique data pattern, etc. The identified object is compressed using a compression treatment optimized for the identified object. The identified object, either before or after the compression, is removed from the uncompressed data. The uncompressed data with the identified object removed is compressed using a standard compression treatment.