Abstract: A system and method for monitoring a roadway by comparing captured images to image inconsistencies caused by lost or unnecessary motion and for minimizing lost or unnecessary motion of an axle of a vehicle and filtering erroneous pulses of an electrical signal to generate a trigger signal to capture images is presented. The system enables the minimization of image artifacts causing distortion in an image. The system can take images of a target, compute an object pixel size of the image to ensure calibration of longitudinal measurements, and calibrate the rotary encoder with the camera of the machine vision system. The system can enable calibration of the locomotive components in the field to compensate for the misalignment of the machine vision system and provide safe travels.

Abstract: Systems and methods are provided for color-enhancing satellite data in a manner that is specific to the true color ocean signal, i.e., the light that is emanating from the ocean surface. These color enhanced images, in turn, can be used as a scientific research and monitoring tool for studying the coastal ocean.

Abstract: Systems and methods for displaying super saturated color. Image data for display on a display or viewing device with a potential white luminance in a standard system with a maximum luminance is processed such that colors near the white point are reduced to a limited luminance. As the chroma of the displayed color is increased, a luminance attenuation is decreased. The scaling of the reduction is operable to be a linear function, a non-linear function, or any other function.

Abstract: A demosaicing method applied to an image having pixels each corresponding to an initial spectral band, to obtain a demosaiced image with several spectral bands having pixels each corresponding to the initial spectral band or to an interpolated spectral band. The method includes: on the basis of reference patches and for each reference patch: a determination of a set of patches similar to the reference patch, a determination of a covariance matrix for |the set of patches, and for each patch, a calculation of estimates of the interpolated spectral bands according to a block-based deconstruction of the covariance matrix, where the deconstruction into four blocks is according to phasing of each patch, and a determination of the interpolated spectral bands by an aggregation of estimates of the interpolated spectral bands, where the estimates of the interpolated spectral bands are aggregated for each pixel.

Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

Abstract: Provided are an image processing apparatus, a control method, and a control program for more accurately removing the seal component from the input image including the seal. An image processing apparatus includes an acquisition module to acquire an input image, a detection module to detect a seal area corresponding to a seal in the input image, a calculation module to calculate a seal color ratio for each pixel in the seal area, a background information generating module to generate background information, a seal removal image generating module to generate a seal removal image acquired by removing a seal component from the input image, by synthesizing each pixel in the seal area and the background information corresponding to each pixel, based on the seal color ratio, and an output module to output the seal removal image or information generated using the seal removal image.

Abstract: Method of processing a digital image relating to a histological tissue, to vary a color by forcing it towards a target average color of a digital reference image. The method includes a segmentation of the image regions that express a hue in a neighborhood of the hue of a comparison color and the calculation of an average coloration of the segmented area and if this average coloration differs under a predetermined threshold from the comparison color, then calculation and application of a corrective factor for each point of the image that expresses a hue around the hue of the average color, if instead the average color deviates beyond the predetermined one threshold from the comparison staining, then (Step 5) the average staining is set as the comparison staining and the segmentation is resumed from (Step 2).

Abstract: Examples are described of segmenting an image into image regions based on depicted categories of objects, and for refining the image regions semantically. For example, a system can determine that a first image region in an image depicts a first category of object. The system can generate a color distance map of the first image region that maps color distance values to each pixel in the first image region. A color distance value quantifies a difference between a color value of a pixel in the first image region and a color value of a sample pixel in a second image region in the image. The system can process the image based on a refined variant of the first image region that is refined based on the color distance map, for instance by removing pixels from the first image region whose color distances fall below a color distance threshold.

Abstract: Examples are described that relate to correcting line bias in images. One example provides a method comprising receiving, from an imaging device, a plurality of images each comprising a plurality of lines of pixels. The method further comprises, for each image of the plurality of images, for each line of pixels of the plurality of lines of pixels, based at least on one or more pixel values of one or more pixels in the line of pixels, determining a line bias correction for the line, and applying the line bias correction to each pixel in the line, the line bias correction comprising an offset applied to each pixel value in the line of pixels.

Abstract: An image processing apparatus according to the present invention including a first acquisition unit configured to acquire color information of a first object obtained by capturing an image of the first object illuminated with light emitted by a light source, a second acquisition unit configured to acquire color information of a second object based on a condition determined by a direction of capturing the image of the first object, a direction of illuminating the first object with light from the light source, and a direction of a normal of the first object, and an output unit configured to output information according to the color information of the first object and the color information of the second object.

Abstract: A cell monitoring plate comprises a flat surface on which multiple cell culturing vessels may be stacked. The flats surface has multiple optical imaging systems embedded therein to fully image a cell culture vessels stacked on the plate. Each one of the multiple optical imaging systems provides both illumination and imaging through a single aperture in the surface of the monitoring plate.

Abstract: There is provided a parcel recognition device that recognizes a parcel based on a color image including the parcel, the device including: a processor; and a memory, in which by cooperating with the memory, the processor subtracts a color of a projection image from a color of an image projection surface of the parcel on which the projection image is projected, in the color image, and sets a color obtained by the subtraction as a color of a recognition target, and recognizes the parcel.

Abstract: The accuracy of an index indicating growth conditions of a crop obtained from a shot image is improved, while reducing the flight time of an aircraft shooting the crop. Crop image acquisition unit acquires an image of a crop region shot by drone. Index calculation unit calculates an index indicating growth conditions of a crop shot in the image based on the acquired image of the crop region. Flight instruction unit instructs, if a portion (low index region) regarding which the calculated index is less than a predetermined index threshold is present in the crop region, drone to shoot the low index region while increasing the resolution of the image. Specifically, flight instruction unit makes an instruction to shoot the portion while performing a low-altitude flight at an altitude lower than that when the image regarding which the index of the low index region has been calculated has been shot.

Abstract: An image processing apparatus includes; an image sensor including pixels that generate first image data, and an image processing system. The image processing system includes; a neural network processor that performs a cluster-level bad pixel correction operation on the first image data based on first coordinate information associated with first-type bad pixel clusters to generate second image data, and a main processor that performs a post-processing operation on the second image data to generate third image data.

Abstract: An image processing apparatus includes a setting unit, a small region setting unit, a determination unit, a search unit, a noise reduction unit, and a synthesis unit. The setting unit sets a target patch in an input image. The small region setting unit sets a small region near the target patch. The determination unit determines a number of similar patches to be detected as a patch similar to the target patch based on a noise amount in the small region. The search unit searches the input image for the similar patch based on the number of similar patches. The noise reduction unit configured to perform noise reduction processing with respect to the target patch using the target patch and the similar patch. The synthesis unit generates an image by synthesizing a plurality of patch groups subjected to the noise reduction processing.

Abstract: A computer-implemented method for identifying an effect pigment, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring sample image data describing a digital image of a layer comprising a sample effect pigment b) determining, based on the sample image data, sparkle point data describing a sample distribution of sparkle points defined by the digital image, wherein the sample distribution is defined in an N-dimensional color space, wherein N is an integer value equal to or larger than 3; c) determining, based on the sparkle point data, sparkle point transformation data describing a transformation of the sample distribution into an (N?1)-dimensional color space; d) determining, based on the sparkle point transformation data, sparkle point distribution geometry data describing a geometry of the sample distribution; e) acquiring reference distribution geometry data describing a geometry of a reference distribution of sparkle points in the (N?1)-dimensional colo

Abstract: Disclosed is a system and associated methods for generating and rendering a polyhedral point cloud that represents a scene with multi-faceted primitives. Each multi-faceted primitive stores multiple sets of values that represent different non-positional characteristics that are associated with a particular point in the scene from different angles. For instance, the system generates a multi-faceted primitive for a particular point of the scene that is captured in first capture from a first position and a second capture from a different second position.

Abstract: Systems and methods for spatial-aware information extraction from electronic source documents are disclosed. Exemplary implementations may: obtain an electronic source document in electronic format, including human-readable information; obtain extracted information that has been extracted from the electronic source document; generate a character-based representation of the electronic source document that uses a grid of character positions; and present a user interface to a user to search and/or select information in the electronic source document based on the character-based representation.

Abstract: Methods and systems for training a neural network to distinguish between text documents and image documents are described. A corpus of text and image documents is obtained. A page of a text document is scanned by shifting a text window to a plurality of locations. In accordance with a determination that the text in the window at a respective location meets text line criteria, the text in the window is stored as a respective text snippet. A plurality of image windows are superimposed over at least one page of an image document. In accordance with a determination that the content of a respective image window meets image criteria, content of the image window is stored as a respective image snippet. The respective text snippet and the respective image snippet are provided to a classifier.

Abstract: A system and method are provided for obtaining a pupil response profile for a subject. The method include: obtaining scan data as frames of a pupil response over time prior to, during and after exposure to a flash of a light source; locating a candidate pupil to be measured from the scan data; image processing the scan data to obtain a set of pupil candidate measurements to generate a graph of pupil measurements against time; filtering the graph to produce a final set of pupil measurements forming a pupil response profile. The method may also include: measuring profile parameters from the pupil response profile; and using the profile parameters to determine aspects of the pupil response.