Abstract: Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Abstract: Techniques are provided for infrared (IR) crosstalk correction for hybrid Red-Green-Blue-IR (RGB-IR) sensors. A methodology implementing the techniques according to an embodiment includes estimating illumination characteristics applied to a subject. The estimation is based on the subject image provided by a hybrid RGB-IR sensor, which comprises a plurality of pixels, each of the pixels associated with an R, G, B, or IR channel. The method further includes selecting a set of correction model parameters from a calibration database, the selection based on the estimated illumination characteristics, and generating a correction model based on the selected set of correction model parameters. The correction model provides correction weights for the RGB channels. The method further includes generating RGB correction values as a product of the correction weights and the IR channel and adjusting the RGB channels by the correction values to reduce IR crosstalk between the IR channel and the RGB channels.

Abstract: A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.

Abstract: A method of jitter detection and image restoration for high-resolution TDI CCD satellite images. The method includes: obtaining a parallax image after a sub-pixel matching method relying on a correlation coefficient and least squares algorithm; transferring a jitter offset from an image space to an object space by an integral transformation function (ITF); dealing with a continuous push-broom mode of cameras in a discrete way with a denser sampling strategy according to a continue dynamic shooting model (CDSM), obtaining a specialized CDSM and feeding it back to the ITF; applying the accurate ITF to the obtained parallax image and conducting the jitter curve fitting to achieve the jitter detection; estimating a correspondent partial PSF according to the CDSM and the obtained jitter curve; carrying out an adaptive restoration based on context through optimal window Wiener filtering, by means of multiple input and single output to completes the image restoration.

Abstract: An imaging device including a pixel matrix and a processor is provided. The pixel matrix includes a plurality of phase detection pixels and a plurality of regular pixels. The processor performs autofocusing according to pixel data of the phase detection pixels, and determines an operating resolution of the regular pixels according to autofocused pixel data of the phase detection pixels, wherein the phase detection pixels are always-on pixels and the regular pixels are selectively turned on after the autofocusing is accomplished.

Abstract: A device and method for automatic image enhancement in vehicles, in particular land vehicles, including a camera to record a primary-image, and an image-processing-module to determine a resulting-image from the primary-image.

Abstract: A surveying apparatus (100) comprising a controller (CPU), the controller (CPU) being configured to: receive an image stream representing a video sequence; determine a camera pose for a second image in the image stream relative a first image in the image stream; match the first image with the second image, based on the camera pose; and generate a three dimensional model based on the image match.

Abstract: The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.

Abstract: A method for implementing image enhancement includes: performing filtering processing on a to-be-processed image to obtain an image subjected to the filtering processing; determining similarity degrees between pixel points in the to-be-processed image and a target region of a target object in the to-be-processed image; and fusing the similarity degrees, the to-be-processed image and the image subjected to the filtering processing, so that the higher a similarity degree between a pixel point and the target object in the to-be-processed image, the stronger a filtering effect of the pixel point, and the lower a similarity degree between the pixel point and the target object in the to-be-processed image, the weaker a filtering effect of the pixel point.

Abstract: A display device panel includes a display area including pixels and a non-display area. Each pixel is connected to one of gate lines and one of data lines. The non-display area includes data pad sections. The non-display area further includes a depiction of a first information code and a depiction of a second information code. The depiction of the first information code is disposed between first two adjacent data pad sections and is apart from an outline of the non-display area by a first distance. The depiction of the second information code is disposed between second two adjacent data pad sections and is apart from the outline of the non-display area by a second distance different from the first distance.

Abstract: This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.

Abstract: A method for noise reduction in a three-dimensional computed tomography dataset, which is reconstructed from two-dimensional projection images recorded with an x-ray device using different recording geometries, is provided. In a post-processing section following the reconstruction of the computed tomography dataset, to obtain a first intermediate dataset, a first, edge-preserving filter is applied to the reconstructed computed tomography dataset. To obtain a second intermediate dataset, a second, morphological filter is applied to the reconstructed computed tomography dataset. A first weighting dataset weighting edges more strongly is established from a subtraction dataset of the first intermediate dataset and the second intermediate dataset. A noise-reduced result dataset is established as a weighted sum of the first intermediate dataset and the second intermediate dataset.

Abstract: The present invention proposes a method for rapidly dehazing an underground pipeline image based on dark channel prior (DCP). The method includes: preprocessing a hazy underground pipeline image to obtain a dark channel image corresponding to the hazy image; average-filtering the obtained dark channel image to estimate an image transmittance; compensating an offset value for an average filtering result to obtain a rough estimate of the transmittance; using a pixel value of the original image and an average-filtered image to estimate a global atmospheric light value; and using a physical restoration model to restore a dehazed image. The method of the present invention realizes the timeliness of the algorithm while ensuring the dehazing effect, and is suitable for scientific fields such as video monitoring of underground pipeline environment and identification of underground pipeline defects.

Abstract: The method of the invention expresses an image with colors by creating a color expression drawing that corresponds to the image to be visualized, wherein: in a first step, n subject(s) each select(s), from among a color sample group that includes color samples including A×B types of color samples, S types of color samples that correspond to the image, and assign(s) a contribution degree for each of the selected color samples; in a second step, an expression chart of each of the n subject(s) is created using the contribution degrees assigned and the color samples of the color sample group; in a third step, for each samples, the contribution degree(s) included in the expression chart(s) of the n subject(s) are summed, thereby obtaining the sums of the contribution degrees of the n subject(s) for the respective color samples, where A, B, S?2, and n?1.

Abstract: A pre-determination process is executed for setting a person determination detection frame in a moving body detection frame surrounding a moving body region detected from an image and determining whether the image in the person determination detection frame is a person and a final determination process of analyzing a distribution state in a moving body detection frame determined to include a person in the pre-determination process and finally determining whether the image in the person determination detection frame is a person based on an analysis result. In the final determination process, an occupancy rate in a specific region in the moving body detection frame determined to include a person in the pre-determination process is calculated, and the image in the person determination detection frame is determined to be a person based on the occupancy rate.

Abstract: The present application provides an image enhancement method and system, the method includes acquiring an input brightness value, and computing a log average value of the input brightness value; processing the input image based on a Retinex image enhancement algorithm to obtain a first brightness gain value; computing a brightness gain control factor according to the log average value of the input brightness value; computing a second brightness gain value according to the brightness gain control factor and the first brightness gain value; and obtaining an output image by enhancing the input image according to the second brightness gain value. The present application uses a Retinex image enhancement algorithm to enhance the input image, and by using the brightness gain control factor, when the image has low brightness, the first brightness gain value is compressed, and when the image has high brightness, the first brightness gain value is compressed slightly.

Abstract: A method of sampling and decoding of a signal of interest x comprising, at a training stage: acquiring a set of training signals {xi}i=1M, providing a sampling operator P? and a decoder g?g(.), training operator P? on signals {xi}i=1M to obtain a learned sampling operator P{circumflex over (?)}; and, at a sampling stage: applying P{circumflex over (?)} in a transform domain ? to signal x, resulting in observation signal y; applying the decoder g?g(.) to y, to produce an estimate {circumflex over (x)} of signal x to decode and/or, decide about, the signal. Decoder g?g(.) is trained jointly with P? on signals {xi}i=1M, to obtain a learned decoder g{circumflex over (?)}g, by jointly determining, during a cost minimization step, sampling parameters ? and decoding parameters ?g according to a cost function, and wherein the step of applying the decoder g?g(.) uses decoding parameters ?g, such that estimate {circumflex over (x)} is produced by the learned decoder g{circumflex over (?)}g.

Abstract: An object detection apparatus is provided with a discriminator applier and a candidate area calculator. The discriminator applier applies a discriminator which detects an object to images acquired in past and calculates object detection information including at least location information of the object detected by the discriminator, in a learning phase. The candidate area calculator performs a machine-learning by use of the object detection information and calculates object candidate area information including at least information specifying a candidate area in which the object may appear in an image.

Abstract: Systems and methods for augmenting real-time semantic information to a spatial rendering of a predefined space and providing a real-time situational awareness feed.

Abstract: An image edge processing method is disclosed. The method includes steps of: extracting a brightness component from an input image; calculating an edge probability value mp of each pixel in the image according to the extracted brightness component; calculating an enhancement coefficient A for each pixel based on the edge probability value mp; performing a noise detection according to the brightness component, and determining if each pixel in the image is a noise point; when the pixel is not a noise point, performing a logarithmic processing to the pixel in order to obtain a data w; enhancing an edge of the image according to the ?, the w and the brightness component in order to obtain an enhanced brightness component data; and after performing a brightness component synthesis according to the enhanced brightness component data, outputting an enhanced image. An electronic device and computer readable storage medium are also disclosed.