Abstract: Provided are an image encoding method, an image decoding method and an image processing system including image encoding/decoding apparatus. The image encoding method includes steps of: acquiring a first image and a plurality of second images; updating features of each second image of the plurality of second images to obtain corresponding update features; superposing the first image with the update features of each second image of the plurality of second images to generate superposed images; generating a plurality of prediction images according to the superposed images; determining difference features between each second image of the plurality of second images and a corresponding prediction image; outputting the superposed images and the difference features; wherein the updating and/or predicting adopts a convolutional neural network.

Abstract: A method of encapsulating an encoded bitstream representing one or more images, the encapsulated bitstream comprising a data part and a metadata part. The method includes providing image item information identifying a portion of the data part representing a sub-image or an image of a single image and/or a set of single images; providing image description information comprising parameters including display parameters and/or transformation operators relating to one or more images and outputting said bitstream together with said provided information as an encapsulated data file. Said image item information comprises one or more properties including at least part of the image description information dedicated to the considered sub-image or single image or set of single images, said image description information being defined in one or more boxes.

Abstract: Image processing generates reference gain information based on a first tone characteristic by using an input image, the reference gain information including a gain value corresponding to a luminance value for each pixel, the first tone characteristic including a luminance range in which a gain signal monotonically increases; generates a correction signal to correct the reference gain information based on a second tone characteristic in which the gain signal monotonically decreases or monotonically increases regardless of the luminance range by using the input image and a low-frequency image generated from the input image; and performs gain processing on the input image based on gain information obtained after the reference gain information is corrected by the correction signal.

Abstract: Methods and systems for determining boundaries of patterned features formed on a specimen from an unresolved image of the specimen are provided. One system includes computer subsystem(s) configured for comparing a difference image in which patterned feature(s) are unresolved to different simulated images. The different simulated images are generated by simulating difference images generated for the patterned feature(s) formed on the specimen with different perturbations, respectively. The computer subsystem(s) are configured for, based on the comparing, assigning an amplitude to each of the different perturbations. The computer subsystem(s) are further configured for determining one or more boundaries of the patterned feature(s) formed on the specimen by applying the different perturbations to one or more designed boundaries of the patterned feature(s) with the assigned amplitudes.

Abstract: A method and apparatus are disclosed. The method may include determining a current zoom value of a camera and determining a first modulation transfer function of a transparent dome of the camera. The method may include applying a first low-pass filter to an image captured by the camera based on the current zoom value and the first modulation transfer function of the transparent dome.

Abstract: A transparent sensor pattern suitable for sensing fingerprint information in a display area is disclosed. The sensor pattern includes a receiver electrode extending in a first direction and a transmitter electrode extending in a second direction different from the first direction. The receiver electrode includes a conductive trace made from an opaque metal material and the receiver electrode is substantially free of transparent conductive material. The transmitter electrode includes a conductive segment made from a transparent conductive material. A dielectric material is disposed between the receiver electrode and the transmitter electrode, and the dielectric material electrically insulates the receiver electrode from the transmitter electrode.

Abstract: According to an aspect, a system comprises at least one processor, a memory, and a non-transitory computer-readable storage medium storing instructions. The stored instructions are executable to cause the at least one processor to: receive a digital image that represents an object scanned by a detection device, determine a region of the digital image that is likely to contain an item, transform the region of the digital image to an embedding, classify, based on the embedding, the region as containing a known class of known item, and responsive to classifying the region as containing the known class of item: generate a graphical representation based on the known class of item.

Abstract: An apparatus is described herein. The apparatus comprises a controller, a logarithmic tone-reproduction operator, a processor, and a compressor. The controller is to calculate a regularization term and a contrast preservation term of an objective function. The logarithmic tone-reproduction operator is to produce a reference image. The processor is to obtain a compressed luminance from the objective function as applied to the reference image. The compressor is to compress a high dynamic range (HDR) image by applying a gain term to the HDR image based on the compressed luminance.

Abstract: A computer-implemented method for color correction includes determining a peak signal-to-noise ratio (PSNR) for a noise evaluation image, determining a corrected PSNR for a corrected noise evaluation image, determining a downsampled PSNR for a downsampled noise evaluation image obtained by downsampling the noise evaluation image, determining a downsampled corrected PSNR for a downsampled corrected noise evaluation image obtained by downsampling the corrected noise evaluation image, and determining a noise amplification metric based on the PSNR, the corrected PSNR, the downsampled PSNR, and the downsampled corrected PSNR.

Abstract: An image processing apparatus stores, as instance images, a plurality of face images obtained by photographing respective faces of a plurality of persons on a plurality of photographing conditions, while associating the persons with the photographing conditions for each part; obtains the photographing condition of the input face image; approximates the respective parts of the input face images, by a combination of the instance images of the plurality of persons stored in association with the parts and the obtained photographing conditions; decides, for each part of the input face image, a combination corresponding to the combination in the approximation from the instance images of the plurality of persons stored in association with the part and a predetermined photographing condition; and generates an image obtained by photographing the input face image on the predetermined photographing condition, by synthesizing the obtained combination on the whole input face image.

Abstract: A method of selecting at least one video frame of a video sequence comprising a plurality of video frames. The method determines a time for analysis based on length of the video sequence and processing capability of a running device. A first sampling pattern is based on the determined time for analysis. A first set of frames in the video sequence is sampled using a first sampling pattern. The first set is sampled infrequently throughout the video sequence in accordance with the first sampling pattern. A candidate frame is determined from the sampled frames based on image quality. A second set of the frames comprising one or more of the frames in a narrow range of the video sequence near the determined candidate frame, is determined in accordance with a second sampling pattern. At least one of the video frames is selected from the sampled frames based on image quality.

Abstract: A method for estimating and forecasting an energy production indicator for a solar system. The method comprises, in particular, the steps of: a) receiving images of the sky taken from the ground, each image comprising an optical marker; b) extracting values of pixels and locating of image pixels from the received images; c) determining distances between the located pixels and the optical marker of the image; d) generating a vector of parameters from a pixel classification, according to the extracted values and the determined distances; e) comparing parameters of the generated vector with predetermined parameters, the predetermined parameters being respectively associated with energy production indicators; f) estimating the energy production indicator of the solar system from energy production indicators of the predetermined parameters compared to the generated vector.

Abstract: Methods and systems for describing a camera radiometrically, in terms of camera response function and vignetting, and in terms of color, suitable for non-uniform illumination set-ups. It estimates the camera response function and the camera color mapping from a single image of a generic scene with two albedos. With a second same-pose image with a different intensity of the near-light the vignetting is also estimated. The camera response function calibration involves the segmentation of the two albedos, the definition of the system of equations based on the assumption that can be made about the image formation process, and the actual estimation. For the modelling the vignetting there are three steps: computing the albedo-normalized irradiance, finding points of equal vignetting, when needed, and estimation.

Abstract: In a cut and clinch device, movable section 122 is slidable with respect to main body section 120. Also, a first insertion hole is formed in main body section 210, and a second insertion hole 136 is formed in curved section 133 of movable section 122 in a state overlapping the first insertion hole. A lead is cut by the movable section being slid with the lead inserted into the first insertion hole and the second insertion hole. In such a cut and clinch device, a pair of recognition marks 210 are provided on a bent section opposite each other centered around the second insertion hole. Thus, the recognition marks are imaged, and based on the image data, it is possible to appropriately recognize the opening position of the second insertion hole, to appropriately insert the lead into the second insertion hole, and to appropriately cut the lead.

Abstract: The disclosed apparatus, systems and methods relate to a framelet-based iterative algorithm for polychromatic CT which can reconstruct two components using a single scan. The algorithm can have various steps including a scaled-gradient descent step of constant or variant step sizes; a non-negativity step; a soft thresholding step; and a color reconstruction step.

Abstract: Methods and apparatus for the generation of interpolated frames of video data. In one embodiment, the interpolated frames of video data are generated by obtaining two or more frames of video data from a video sequence; determining frame errors for the obtained two or more frames from the video sequence, determining whether the frame errors exceed a threshold value; performing a multi-pass operation; performing a single-pass operation; performing frame blending; performing edge correction; and generating the interpolated frame of image data.

Abstract: A method for detecting jittering in videos generated by a shaken camera to remove the jittering on the videos using neural networks is provided for fault tolerance and fluctuation robustness in extreme situations. The method includes steps of: a computing device, generating each of t-th masks corresponding to each of objects in a t-th image; generating each of t-th object motion vectors of each of object pixels, included in the t-th image by applying at least one 2-nd neural network operation to each of the t-th masks, each of t-th cropped images, each of (t?1)-th masks, and each of (t?1)-th cropped images; and generating each of t-th jittering vectors corresponding to each of reference pixels among pixels in the t-th image by referring to each of the t-th object motion vectors. Thus, the method is used for video stabilization, object tracking with high precision, behavior estimation, motion decomposition, etc.

Abstract: A document processing apparatus includes an image processing unit, a target determination unit, a text reading unit, and a keyword extraction unit. The image processing unit processes an image. The target determination unit determines whether an image that is a target of a process executed by the image processing unit is to be used as a keyword extraction target. The text reading unit reads a text from the image processed by the image processing unit. The keyword extraction unit extracts a keyword from the text read by the text reading unit from an image determined to be a keyword extraction target by the target determination unit.

Abstract: An illuminance calculation unit detects an illuminance of an image when the image is captured. Based on the detected illuminance, a gamma correction amount calculation unit corrects at least one of a minimum output luminance value and a maximum output luminance value in a gamma characteristic for correcting a gradation characteristic of a luminance of the image. A gamma setting unit corrects the gradation characteristic of the luminance of the image with the gamma characteristic corrected based on the illuminance.

Abstract: A method for clearing unwanted data from optically detected virtual representations of objects includes: a. Defining an extension line of the representation; b. Generating a projection plane at one point of the extension line, which is perpendicular to the generated projection plane; c. Projecting all known points in space of the representation from one region on the projection plane onto the projection plane, the corresponding point in space being stored for each projected point; d. Generating a two-dimensional curve on the projection plane; e. Determining maxima, minima and a center of the curve; f. Identifying projected points of the curve that—viewed from the center of the curve—lie outside of the minima or maxima; g. Removing the points in space that correspond to the projected points that were identified in step f.; and h. Optionally, repeating starting from step b. for one further point of the extension line.