Abstract: Systems, apparatuses, and methods for detecting and mitigating scaling artifacts caused by high chromatic colors in adjacent pixels are disclosed. A blend factor calculation circuit determines if high chromatic colors are in close proximity to each other in a set of pixel data of an image or frame. The blend factor calculation circuit generates a blend factor value to suppress artifacts which are introduced when filtering the set of pixel data when the set of pixel data has high chromatic colors in close proximity. In one scenario, the blend factor calculation circuit calculates pixel component difference values of adjacent pixels and uses a value calculated based on the difference values as an input to a transfer function. The output of the transfer function is a blend factor value which determines how filtering is blended between a plurality of filters.

Abstract: A system and method for correcting image data is disclosed. The method includes receiving one or more documents from one or more electronic mediums. The method further includes determining a primary character and one or more alternate characters corresponding to the mis-captured character image, extracting one or more confident instances of the primary character and the one or more alternate characters from the one or more documents and generating one or more scores corresponding to the primary character and the one or more alternate characters. Further, the method includes predicting a correct character corresponding to the mis-captured character image by using a trained image prediction-based ML model and automatically replacing the mis-captured character image with the predicted correct character.

Abstract: An inspection system is provided and includes a camera and controller. The controller can include one or more processors in communication with the camera and receive a plurality of images of a target captured by the camera. The controller can also determine, using a first computer vision algorithm, a first prediction and corresponding confidence level for substantially all of the images. The controller can select a subset of the images having the first prediction confidence level greater than or equal to a first prediction threshold value. The controller can additionally determine, using a second computer vision algorithm, a second prediction and corresponding second prediction confidence level for each of the selected images. The at least one second prediction can require more time to determine than the at least one first prediction. The controller can output the second prediction and the second prediction confidence level for each of the selected images.

Abstract: An industrial tomography apparatus comprising a tomography scan device configured to perform tomography scans of the products placed in the scanning zone and an electronic processing unit programmed to generate a virtual three-dimensional tomography model of a product scanned by the tomography scan device, and to perform a procedure for inspecting industrial products of known type, each comprising a packet or a container. During the inspection procedure the electronic processing unit inspects the virtual three-dimensional tomography model to assess internal features of the product and/or the shape of the product at predetermined zones and determines whether or not those internal features and/or respectively that shape, correspond to a product with intact packet or container. The apparatus implements a corresponding method for checking the integrity of packets or containers of industrial products which have known features.

Abstract: A system for automated surface anomaly detection includes at least one processor which is configured to retrieve from non-volatile memory a 3D control image; capture a 3D target image depicting at least the portion of the exterior of a target object; generate 2D target planar images of the target object based on the 3D target image, using a first plurality of virtual cameras; generate 2D control planar images of the control object based on the 3D control image using a second plurality of virtual cameras, the 2D control planar images corresponding to the 2D target planar images of the target object; detect at least one difference between the 2D target planar images and the 2D control planar images; and generate and cause to be displayed an output image comprising a depiction of the target object with the at least one difference indicated.

Abstract: An imaging system of the present disclosure includes: an event detection sensor that detects an event; an object recognition unit that performs recognition processing for an object detected as the event on the basis of an event signal output from the event detection sensor; a motion vector estimation unit that estimates a motion vector of the object on the basis of a recognition result by the object recognition unit; and a control unit that outputs warning information for warning that a dangerous state has occurred on the basis of an estimation result by the motion vector estimation unit.

Abstract: A method and system are provided. The method includes receiving an input frame, performing high frequency noise reduction (HFNR) on the received input frame, performing low frequency noise reduction (LFNR) on the received input frame, and obtaining a hybrid denoised clean frame by fusing an output of the performed HFNR and an output of the performed LFNR.

Abstract: The invention addresses the problem of improving labor of a registration operation of a product and enabling a checkout operator to recognize a recognition result. In order to solve the problem, the invention provides a registration apparatus (10) including: an image acquisition unit (11) that acquires an image obtained by imaging a placement surface of a table, on which a product is placed; an analysis unit (12) that recognizes the product included in the image, a registration unit (14) that registers the recognized product as a checkout target, and an output unit (13) that outputs a name of the recognized product by voice.

Abstract: A method for reconstructing a MRI image may include: receiving MRI measurement data sets f1 to fN, each data set being acquired from an examination object based on a different MRI protocol of an MRI system; receiving MRI images u10 to uN0 corresponding to the MRI measurement data sets f1 to fN; performing one or more translation and rotation transformations on the MRI images u10 to uN0; applying one or more trained functions: to the transformed MRI images u10 to uN0, using a neural network, and to the MRI images u10 to uN0, using a forward-sampling operator; performing one or more inverse translation and rotation transformations on an output of the neural network; and generating at least one output MRI image uT based on an output of the forward-sampling operator, the inversely transformed output of the neural network, and the input MRI images u10 to uN0.

Abstract: A method for accelerating hyperspectral video reconstruction includes steps of: acquiring, according to a spectral video and an RGB video captured by a hyperspectral video camera, a calibration matrix of the spectral video and the RGB video; sorting the calibration matrix to generate an ordered calibration matrix; converting, according to the ordered calibration matrix, the spectral video and the RGB video into a data matrix in a parallel manner; acquiring all related calibration points of a reconstruction region according to the ordered calibration matrix; and, reconstructing a hyperspectral video in a parallel manner according to the related calibration points and the data matrix. The related calibration points are acquired by sorting the calibration matrix, such that the number of times the calibration matrix is traverse is reduced, and the computation amount of hyperspectral video reconstruction is decreased.

Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.

Abstract: An image processing method, a device and a non-transitory computer-readable storage medium thereof are provided. The method includes that a first image of a target object, a second image of the target object, and a third image of the target object are acquired. Exposure of the first image is less than exposure of the second image. The exposure of the second image is less than exposure of the third image. Further, brightness of the first image is increased to acquire a fourth image. Moreover, ghost elimination is performed on the second image and the fourth image is taken as a first reference frame to acquire a second reference frame. Furthermore, ghost elimination is performed on the third image based on the second reference frame to acquire a fifth image.

Abstract: A method for determining authenticity of a video in a surveillance system, whereby a sequence of image frames of a scene is captured, and an object is tracked. A current image quality measure in an image area corresponding to the tracked object is determined in at least a first and second image frame. chosen such that the object has moved at least a predetermined distance between the first and second image frames. A current image quality measure variation for the object is determined, the image quality measure variation describing the image quality measure as a function of position of the object in the image frames. The current image quality measure variation is compared to a known image quality measure variation. In response to the current image quality measure variation deviating from the known pixel density variation by less than a predetermined amount, it is determined that the video is authentic.

Abstract: There is provided an inspection method that includes determining, as a reference blur component, the magnitude of the blur component of a range, the range being a range where a number of first inspection images among the plurality of inspection images falling within the range is the largest among a plurality of ranges. The inspection method includes correcting, based on the reference blur component, a second inspection image among the plurality of inspection images having the magnitude of the blur component falling outside the range. The inspection method includes comparing the first inspection image and the corrected second inspection image with a reference image, the first inspection image having the magnitude of the blur component falling within the predetermined range, the reference image being generated in advance for the measurement object.

Abstract: An image processing device, including a first corrector configured to generate a first corrected image obtained by correcting a local intensity of an input image based on a complexity of an intensity distribution of the input image and an intensity value of an area around a pixel; and a second corrector configured to generate a second corrected image obtained by correcting a global intensity of the first corrected image based on a histogram of the input image.

Abstract: A method and apparatus for classification using a neural network. A classification apparatus includes at least one processor and a memory. The memory is configured to store a classifier and a preprocessor including a defensive noise generator. The at least one processor generates defensive noise from an input image through the defensive noise generator in the preprocessor, generates a combined image by combining the input image and the defensive noise, and outputs a classification result by inputting the combined image to the classifier.

Abstract: An information processing system, which determines a partial area used in an erectness determination which determines whether a determination target image is erect based on a determination feature in the partial area in an image having a predetermined format and a position of the partial area in a state in which the image having the predetermined format is erect, includes processor to receive input of a plurality of learning images having the predetermined format, extract area common to the plurality of learning images in an erect state as a candidate for the partial area, determine common area reliability indicative of a degree to which the candidate for the partial area is suitable as the partial area used in the erectness determination, and determine the partial area used in the erectness determination from the candidate for the partial area, based on the common area reliability.

Abstract: Systems and methods of the present disclosure physical object-based passwords by registering an object password including first object representations, first background scene representations, and a first presentation sequence from first image data. Receiving second image data and a second presentation sequence that is a second order in which the user has presented second physical objects to an image acquisition device, and detecting in the second image data second object representations of the second physical objects, and second background scene representations. Computing a probability that the user is a permissioned user by inputting the first image data and the second image data into a machine learning model configured to compute the probability based on comparing the object password and the second image data. Tagging the user as the permissioned user or non-permissioned user based on the probability.

Abstract: A method is provided for inspection of an item comprising acquiring a projection image of the item using a radiation imaging system and obtaining a plurality of simulated projection images of the item or a component thereof, based on a simulation of a numerical three-dimensional model. A relative orientation of the item with respect to the imaging system is determined by comparing the projection image to the plurality of simulated images, and at least one angle of rotation is determined by taking into account a viewing angle and the relative orientation. The method further comprises moving the item and/or the imaging system in accordance with the at least one angle of rotation and acquiring a further projection image of the item.

Abstract: Disclosed herein is a method and a system for reconstructing a three-dimensional image of an object, based on stitched images of the object obtained using multiple beams.