Abstract: Methods and processing modules upsample a block of input pixels to determine a block of upsampled pixels. At least one of the upsampled pixels is a diagonal pixel, wherein a diagonal pixel is at a position that is not in any of the rows nor in any of the columns of input pixels in the block of input pixels. Indications of image gradients are determined for the block of input pixels. The determined indications of image gradients are used to determine one or more weighting parameters which are indicative of weights of a diagonal kernel. The upsampled pixels of the block of upsampled pixels are determined by applying kernels to the block of input pixels, wherein the diagonal pixel in the block of upsampled pixels is determined by applying the diagonal kernel to the block of input pixels in accordance with the determined one or more weighting parameters.

Abstract: Methods and processing modules apply adaptive sharpening, for a block of input pixels for which processing is performed, to determine a block of output pixels. A block of non-sharp processed pixels is obtained based on the block of input pixels, the block of non-sharp processed pixels being for representing a non-sharp version of the block of output pixels. A block of sharp processed pixels is obtained based on the block of input pixels, the block of sharp processed pixels being for representing a sharp version of the block of output pixels. One or more indications of contrast for the block of input pixels is determined. Each of the output pixels of the block of output pixels is determined by performing a respective weighted sum of: (i) a corresponding non-sharp processed pixel in the block of non-sharp processed pixels and (ii) a corresponding sharp processed pixel in the block of sharp processed pixels.

Abstract: A computer-implemented method allows a wait time to be determined automatically for a queue area. A series of images showing the environment are received over time. A wait time associated with the queue area is determined by detecting a location of a object corresponding to a person in a first one of the images; associating the object with an identifier uniquely identifying the object in the first one of the images matching objects in later images; and determining the wait time based on times associated with an image in which an object associated with the identifier enters the queue area through the defined entrance area and later one of the images in which an object associated with the identifier exits the queue area through the defined exit area. An indication of the wait time is output. Machine learning may be used to extract features of an object from an image.

Abstract: An aspect includes a computer-implemented method that quantifies variations in surgical approaches to medical procedures. Surgical videos documenting multiple cases of a medical procedure are analyzed to identify variations in surgical approaches used by service providers when performing the medical procedure. According to some aspects, the variation in surgical approaches is quantified.

Abstract: An apparatus for extracting image noise includes an encoder that encodes a first image and a second image in a time-series image to output an encoding feature map. The apparatus also includes a noise information extraction layer that generates a first noise prototype vector to an M-th noise prototype vector of the first image and the second image, and outputs first noise information with reference to at least a part of the first noise prototype vector to the M-th noise prototype vector and the encoding feature map. The apparatus additionally includes a decoder that decodes the first noise information to output second noise information. The apparatus further includes a parameter updater that outputs a loss with reference to the first image, the second image, and the corresponding second noise information, and updates parameters of the encoder, the noise information extraction layer and/or the decoder through back propagation using the loss.

Abstract: A method of verifying the identity of a mail item, the method comprising obtaining a first visual representation of at least a portion of a mail item, determining a first postal identifier based on the first visual representation, obtaining a second visual representation of at least a portion of the mail item, determining a second postal identifier based on the second visual representation, comparing the second postal identifier with the first postal identifier, and enabling processing of the mail item based on the comparison step.

Abstract: There is provided a method and corresponding system for image reconstruction based on energy-resolved x-ray data. The method comprises collecting (S1) at least one representation of energy-resolved x-ray data, and performing (S2) at least two basis material decompositions based on said at least one representation of energy-resolved x-ray data to generate at least two original basis image representation sets. The method further comprises obtaining or selecting (S3) at least two basis image representations from at least two of said original basis image representation sets, and processing (S4) said obtained or selected basis image representations by data processing based on machine learning to generate at least one representation of output image data.

Abstract: A method for reconstructing images, an electronic device and a storage medium is provided. In the method, the electronic device obtains an image to be reconstructed of an object captured by a target camera device, and obtains a sample image of the object. A radial basis function network is trained based on the image to be reconstructed and the sample image. An image reconstruction model is obtained by converting the radial basis function network based on a deconvolution algorithm, the image reconstruction model is configured for reconstructing images captured by the target camera device. By performing the method, a clarity of the image to be reconstructed can be improved.

Abstract: Provided are a device, a method, and a system for supporting biometric image finding/diagnosis, the device comprising: a processor; and a memory including one or more instructions implemented to be executed by the processor, wherein the processor; extracts a first attribute information from a first biometric image of an object on the basis of a machine learning model; changes the first attribute information of the first biometric imaged by mapping adversarial noise to the first biometric image, so as to generate a second biometric image having second attribute information; and displays the first biometric image having the first attribute information and the second biometric image having the second attribute information on a display unit.

Abstract: A blood analyser and related methods, in particular a method of analysing a blood sample is disclosed. The blood analyser comprises a memory, an interface, and one or more processors. The blood analyser is configured to obtain image data of a prepared blood sample; select a first image associated with a first image plane of the prepared blood sample from the image data; characterize the first image, wherein the characterization of the first image comprises to determine a first set of cell regions belonging to the first image plane; select a first distal image associated with a first distal image plane on a distal side of the first image plane, and determine a first distal set of distal cell regions associated with the first set of cell regions; and determine a first cell parameter for each cell region of the first set of cell regions based on the first distal set of distal cell regions.

Abstract: A reticle inspection and purging method comprises following steps. A first reticle is moved from a first load port of a lithography tool to a reticle inspection tool located outside the lithography tool. The first reticle is inspected using the reticle inspection tool located outside the lithography tool. Whether the first reticle is acceptable for exposure is determined based on the inspection result. In response the determination determines that the first reticle is not acceptable for exposure, the first reticle is purged.

Abstract: In the present disclosure, an appropriate inference is made using a recurrent-type neural network in image restoration processing. An input-image-acquisition unit acquires input image data. A learning-parameter-acquisition unit acquires a learning parameter to learn an image restoration network. An initialization determination unit acquires learning-sequence-length-information from the learning parameter and generates initialization information specifying a frame where two degradation restoration units perform initialization. Each degradation restoration unit receives one frame (current frame) of the input image data, sees the initialization information, determines whether to initialize recurrent information in a current frame inference, makes an inference about the current frame using initial image data or an inference result about a preceding frame immediately before the current frame as recurrent information, and outputs the inference result to a synthesis unit and the degradation restoration unit itself.

Abstract: An end-to-end instance-separable semantic-image joint compression system and method, comprising: image encoder and decoder, mask encoder and decoder, union encoder, embedding extraction module, semantic graph encoder and decoder, entropy coding module, union entropy model, image entropy model and mask entropy model. This invention can fully utilize the redundancy between images and their semantics (including instance masks, bounding boxes, categories and relationships) to improve rate-distortion performance; at the same time, this invention allows instances to be independently coded, facilitating the decoder side to retrieve the semantics and image of a specific object.

Abstract: A method includes obtaining pre-weld image data of the electric motor stator from one or more image sensors; performing a welding process in response to obtaining the pre-weld image data, obtaining post-weld image data of the electric motor stator from the one or more image sensors in response to performing the welding process, obtaining epoxy image data of the electric motor stator from the one or more image sensors in response to obtaining the post-weld image data, performing a difference-based image processing routine based on the post-weld image data and the epoxy image data to generate a digital twin of the electric motor stator, and determining one or more epoxy characteristics of the electric motor stator based on the digital twin.

Abstract: A laundry treatment appliance includes a cabinet; a wash tub; a wash basket; and a controller provided within the cabinet to direct a laundry operation. The laundry operation includes receiving a plurality of wash requests from a plurality of unique users, wherein each wash request of the plurality of wash requests comprises an image of a unique laundry load; analyzing, by one or more computing devices using a machine learning image recognition model, each image to evaluate one or more laundry load characteristics of a first unique laundry load and a second unique laundry load; matching a first unique user and a second unique user from the plurality of users, the first user and the second user having a matching laundry load characteristic from the one or more laundry load characteristics; and generating a joint washing cycle subsequent to matching the first and second unique users.