Abstract: In general, intelligent fuel dispensers are provided. In at least some implementations, an intelligent fuel dispenser can determine customer identities and/or other characteristics and provide customized fueling sessions based on the determined customer identities and/or other characteristics. In at least some implementations, the fuel dispenser includes a touchless interface allowing customers to complete fueling sessions with minimal physical contact with the fuel dispenser.

Abstract: In general, intelligent fuel dispensers are provided. In at least some implementations, an intelligent fuel dispenser can determine customer identities and/or other characteristics and provide customized fueling sessions based on the determined customer identities and/or other characteristics. In at least some implementations, the fuel dispenser includes a touchless interface allowing customers to complete fueling sessions with minimal physical contact with the fuel dispenser.

Abstract: A method of determining volumetric data of a predetermined anatomical feature is described. The method comprising determining volumetric data of one or more anatomical features present in a field of view of a depth sensing camera apparatus, identifying a predetermined anatomical feature as being present in the field of view of the depth sensing camera apparatus, associating the volumetric data of one of the one or more anatomical features with the identified predetermined anatomical feature, and outputting the volumetric data of the predetermined anatomical feature. An apparatus is also described.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry calculates noise intensity from first data acquired during a first scan. The processing circuitry calculates a denoising intensity used for a denoising process based on the noise intensity and the difference between an imaging condition for the first scan and an imaging condition for a second scan, the denoising process being applied to second data obtained by the second scan.

Abstract: Exemplary embodiments are directed to a system for selective enhancement of skin features in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more skin imperfections in the original image, generate a neural network mask of the original image for the one or more skin imperfections in the original image, generate one or more source patches based on the original image, and, replace the one or more skin imperfections in the original image with the one or more source patches to generate a patched skin image.

Abstract: Exemplary embodiments are directed to a system for selective enhancement of an object in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, apply one or more enhancements to the objects associated with the neural network mask, the one or more modules generating an enhanced image including the one or more enhancements to the objects, and generate a combined image, the combined image including the original image combined with the one or more enhancements to the objects of the enhanced image.

Abstract: An image processing apparatus includes a first filter unit and a first composition unit. The first filter unit generates a plurality of results of filter processing for brightness by individually applying each of a plurality of filters for brightness with different frequency bands to a first brightness component image indicating a distribution of brightness components in a first image. The plurality of filters for brightness are spatial filters based on a visual characteristic of a human related to contrast detection. The first composition unit generates brightness contrast influence information indicating a distribution of degrees of influence of brightness contrast in the first image by compositing together the plurality of results of filter processing for brightness.

Abstract: Methods, systems, and apparatus for image processing are provided. In one aspect, a method includes: acquiring an image to be processed that involves a target object, extracting image noise information and contour information of the target object from the image to be processed, generating a noise distribution image based on the image noise information and the contour information of the target object, and obtaining a target image by performing noise reduction on the image to be processed with the noise distribution image.

Abstract: The present disclosure relates to a method of motion segmentation (100) in a video stream. The method comprises the steps of: acquiring (101) a sequence of image frames; dividing (102) a first frame (401) into a plurality of image blocks (403); comparing (103) each image block (403) against a corresponding reference image block (404) and providing a measure of dissimilarity; for image blocks having a measure of dissimilarity less than a threshold: discarding (104a) the image blocks, and for image blocks having a measure of dissimilarity greater than the threshold: keeping (104b) the image blocks and further dividing the image blocks into a new plurality of image blocks (405); repeating the steps of dividing (102) and comparing (103) until a stop condition is met (105a); generating (106) a motion mask (407) indicating areas of movement (408).

Abstract: A fluorescent in-situ hybridization imaging and analysis system includes a flow cell to contain a sample to be exposed to fluorescent probes in a reagent, a fluorescence microscope to obtain sequentially collect a plurality of images of the sample at a plurality of different combinations of imaging parameters, and a data processing system. The data processing system includes an online pre-processing system configured to sequentially receive the images from the fluorescence microscope as the images are collected and perform on-the-fly image pre-processing to remove experimental artifacts of the image and to provide RNA image spot sharpening, and an offline processing system configured to, after the plurality of images are collected, perform registration of images having a same field of view and to decode intensity values in the plurality of images to identify expressed genes.

Abstract: A medical image processing apparatus includes: a first captured image acquisition unit configured to acquire a first captured image obtained by capturing light from an observation target irradiated with light in a first wavelength band, the observation target emitting fluorescence when irradiated with excitation light in a second wavelength band different from the first wavelength band; a second captured image acquisition unit configured to acquire a second captured image obtained by capturing the fluorescence from the observation target irradiated with the excitation light; a first image processor configured to execute image processing on the first captured image; and a second image processor configured to execute image processing including blurring processing of blurring an image on the second captured image.

Abstract: An electronic device includes at least one imaging sensor and at least one processor coupled to the at least one imaging sensor. The at least one imaging sensor is configured to capture a burst of image frames. The at least one processor is configured to generate a low-resolution image from the burst of image frames. The at least one processor is also configured to estimate a blur kernel based on the burst of image frames. The at least one processor is further configured to perform deconvolution on the low-resolution image using the blur kernel to generate a deconvolved image. In addition, the at least one processor is configured to generate a high-resolution image using super resolution (SR) on the deconvolved image.

Abstract: An image processing apparatus includes a memory and a processor coupled to the memory and configured to perform acquiring object area information representing an image area of a target object in an image from an image database, using the object area information to calculate a distance map representing distance values from a contour of the target object inside the image area, and generating, based on the distance map, a line of points representing a curved line reflecting a global shape of the target object.

Abstract: A method of depth-of-field simulation, including receiving a plurality of images, predicting a layer of interest mask of the plurality of images, determining a plurality of mean brightness anchor values of a respective plurality of layers of interest window arrays, setting a plurality of layers of interest set of binary codes, determining a hamming distance between plurality of layers of interest set of binary codes, determining a cost volume based on the hamming distance, resampling a vertical cost based on a vertical ordinal direction the cost volume, resampling a horizontal cost based on a horizontal ordinal direction the cost volume, determining an all-in-focus layer based on the vertical cost and the horizontal cost, determining an out-of-focus layer based on the vertical cost and the horizontal cost and determining a depth of the all-in-focus layer and out-of-focus layer.

Abstract: A neural processor includes one or more neural engine circuits and a planar engine circuit. The neural engine circuits can perform convolution operations of first input data with one or more kernels to generate a first output. The planar engine circuit receives second input data that corresponds to a version of the first input data. The planar engine circuit also receives third input data that includes fourth input data and fifth input data stored together in a dimension of third input data. The planar engine circuit performs a first elementwise operation between a version of the second input data and a version of the fourth input data to generate intermediate data. The planar engine circuit performs a second elementwise operation between the intermediate data and a version of the fifth input data to generate a second output.

Abstract: The present disclosure relates to systems and methods for image processing. The method may include receiving an original image including a plurality of pixels; determining a plurality of smoothed images by applying a plurality of filtering kernels to the plurality of pixels of the original image, wherein each of the plurality of filtering kernels may be associated with a respective kernel size; determining a plurality of enhanced images by comparing the original image with the plurality of smoothed images; and generating a target image based on at least one of the plurality of enhanced images and at least one of: the original image or at least one of the plurality of smoothed images.

Abstract: A method and a device for encoding/decoding images are disclosed. The method for encoding images comprises the steps of: deriving a scan type of a residual signal for a current block according to whether or not the current block is a transform skip block; and applying the scan type to the residual signal for the current block, wherein the transform skip block is a block to which transform for the current block is not applied and is specified on the basis of information indicating whether or not transform for the current block is to be applied.

Abstract: Disclosed is a recognition and training method and apparatus. The apparatus may include a processor configured to input data to a neural network, determine corresponding to a multiclass output a mapping function of a first class and a mapping function of a second class, acquire a result of a loss function including a first probability component that changes correspondingly to a function value of the mapping function of the first class and a second probability component that changes contrastingly to a function value of the mapping function of the second class, determine a gradient of loss corresponding to the input data based on the result of the loss function, update a parameter of the neural network based on the determined gradient of loss for generating a trained neural network based on the updated parameter. The apparatus may input other data to the trained neural network, and indicate a recognition result.

Abstract: A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities.