Abstract: The invention relates to a method for producing a panoramic tomographic image (1, 11, 60) of an object (2) to be recorded using a 2D panoramic X-ray device (3), in the course of which X-rays (4) produced by means of an X-ray source (5) irradiate the object (2) and are acquired by means of an X-ray detector (7), wherein, during a movement of the X-ray source (5) and the X-ray detector (7) around the object (2), a number of 2D X-ray projection images (30) are acquired from different acquisition directions (6).

Abstract: Provided is an apparatus for acquiring images including an image sensor including a sensor substrate including a plurality of photo-sensing cells sensing light, and a color separation lens array provided above the sensor substrate, the color separation lens array including a fine structure in each of a plurality of regions respectively facing the plurality of photo-sensing cells and separating incident light based on color, the fine structure forming a phase distribution to condense light having different wavelengths on adjacent photo-sensing cells, a signal processor configured to perform, based on a point spread function corresponding to each color pixel by the color separation lens array, deconvolution on sensing signals of the plurality of photo-sensing cells to process an image signal for each color obtained by the image sensor, and an image processor configured to form a color image from the image signal for each color processed by the signal processor.

Abstract: A radar device includes: a signal transmission unit for generating a MIMO signal including a plurality of pulse signals, and radiating the MIMO signal into space; a signal reception unit for receiving a reflection signal resulting from reflection, by a target, of the MIMO signal radiated from the signal transmission unit; a demodulation unit for demodulating the MIMO signal from the reflection signal received by the signal reception unit; a beam-forming unit for forming beams in a plurality of different directions, by multiplying the plurality of pulse signals included in the MIMO signal demodulated by the demodulation unit by a respective plurality of different weighting coefficients; a control unit for changing noise power included in each of the beams in the plurality of directions formed by the beam-forming unit, by shifting a phase of the MIMO signal generated by the signal transmission unit and adjusting the plurality of weighting coefficients on the basis of an amount of phase shift of the phase; and a

Abstract: A system simulates hyperspectral imaging data or multispectral imaging data for a simulated sensor. Blackbody radiance of real-world thermal imagery data is computed using a Planck function, which generates a simulated spectral hypercube. Spectral emissivity data for background materials are multiplied by a per-pixel weighting function, which generates weighted spectral emissivity data. The simulated spectral hypercube is multiplied by the weighted spectral emissivity data, which generates background data in the simulated spectral hypercube. Simulated targets are inserted the background data using the Planck function. The simulated spectral hypercube is modified, and then it is used to estimate a mission measure of effectiveness of the simulated sensor.

Abstract: A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

Abstract: Apparatus and methods are described including receiving, via a computer processor, at least one image of a portion of a subject's body. One or more features that are present within the image of the portion of the subject's body, and that were artificially added to the image subsequent to acquisition of the image, are identified. In response thereto, an output is generated on an output device.

Abstract: A computer system can automatically analyze a video of a physical activity and provide corresponding feedback. For example, the system can receive a video file including image frames showing an entity performing a physical activity that involves a sequence of movement phases. The system can generate coordinate sets by performing image analysis on the image frames. The system can provide the coordinate sets as input to a trained model, the trained model being configured to assign scores and movement phases to the image frames based on the coordinate sets. The system can then select a particular movement phase for which to provide feedback, based on the scores and movement phases assigned to the image frames. The system can generate the feedback for the entity about their performance of the particular movement phase, which may improve the entity's future performance of that particular movement phase.

Abstract: A method for registering a mining computing entity (MCE) with a trusted execution environment entity (TEEE) in a blockchain of a distributed blockchain consensus network (DBCN), based on a proof-of-stake protocol, includes providing public signing and corresponding secret signing information and trusted time information by the TEEE of the MCE, providing public and secret account information for a virtual wallet of the blockchain by the MCE, and generating integrity information by the TEEE. The method further includes generating attestation information by signing the integrity information, hashed public signing information and public account information, computing proving information, by an attestation providing entity (APE), by attesting the attestation information, and sending a transaction to the blockchain, signed with the secret account information, the transaction including the public signing information and the proving information.

Abstract: Disclosed embodiments relate to systems for performing instructions to quickly convert and use matrices (tiles) as one-dimensional vectors. In one example, a processor includes fetch circuitry to fetch an instruction having fields to specify an opcode, locations of a two-dimensional (2D) matrix and a one-dimensional (1D) vector, and a group of elements comprising one of a row, part of a row, multiple rows, a column, part of a column, multiple columns, and a rectangular sub-tile of the specified 2D matrix, and wherein the opcode is to indicate a move of the specified group between the 2D matrix and the 1D vector, decode circuitry to decode the fetched instruction; and execution circuitry, responsive to the decoded instruction, when the opcode specifies a move from 1D, to move contents of the specified 1D vector to the specified group of elements.

Abstract: A storage device and method of controlling a storage device are disclosed. The storage device includes a host, a logic die, and a high bandwidth memory stack including a memory die. A computation lookup table is stored on a memory array of the memory die. The host sends a command to perform an operation utilizing a kernel and a plurality of input feature maps, includes finding the product of a weight of the kernel and values of multiple input feature maps. The computation lookup table includes a row corresponding to a weight of the kernel, and a column corresponding to a value of the input feature maps. A result value stored at a position corresponding to a row and a column is the product of the weight corresponding to the row and the value corresponding to the column.

Abstract: A method includes filtering a point cloud transformation of a 3D object to generate a 3D lattice and processing the 3D lattice through a series of bilateral convolution networks (BCL), each BCL in the series having a lower lattice feature scale than a preceding BCL in the series. The output of each BCL in the series is concatenated to generate an intermediate 3D lattice. Further filtering of the intermediate 3D lattice generates a first prediction of features of the 3D object.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to divide each of one or more images acquired by a camera into a plurality of zones, determine respective camera noise values for respective zones based on the one or more images, determine one or more zone expected values for the one or more images by summing camera noise values multiplied by scalar coefficients for each zone and normalizing the sum by dividing by a number of zones in the plurality of zones, and determine a source of the camera as being one of the same camera or an unknown camera based on comparing the one or more zone expected values to previously acquired expected zone values.

Abstract: One example provides a system for reading birefringent data. The system comprises one or more light sources, a first polarization state generator positioned to generate first polarized light from light of a first wavelength band output by the one or more light sources, a second polarization state generator positioned to generate second polarized light from light of a second wavelength band output by the one or light sources, an image sensor configured to acquire an image of the sample region via the first polarized light and the second polarized light, a polarization state analyzer disposed between the sample region and the image sensor, a first bandpass filter configured to pass light of the first wavelength band onto the image sensor, and a second bandpass filter configured to pass light of the second wavelength band onto the image sensor.

Abstract: An image sensor has an image sensor array and circuit design employing a method of black level compensation to address image shading related to global exposure image capture and rolling row by row readout schemes. An image sensor including the invented black level compensation pixel array and method may be incorporated within a digital camera.

Abstract: An online identity verification application may be provided. According to an exemplary embodiment, an online identity verification application may utilize photographic, biometric, and documentation identification protocols. The verification application may use a multi-tier verification process based on identification protocols to verify the owner of a verification account and subsequently verify any linked accounts.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to divide each of one or more images acquired by a camera into a plurality of zones, determine respective camera noise values for respective zones based on the one or more images, determine one or more zone expected values for the one or more images by summing camera noise values multiplied by scalar coefficients for each zone and normalizing the sum by dividing by a number of zones in the plurality of zones, and determine a source of the camera as being one of the same camera or an unknown camera based on comparing the one or more zone expected values to previously acquired expected zone values.

Abstract: An imaging device includes one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the imaging device to perform functions including: capturing a first image and thereafter a second image; making a determination of whether or not a difference between the first image and the second image is greater than a threshold value; generating a third image by processing the second image using an image processing algorithm that corresponds to the determination; and displaying the third image.

Abstract: A computer-implemented method may include (1) maintaining (a) a filter matrix in a filter cache included in a local memory device (LMD) included in a hardware accelerator, and (b) a plurality of activation matrices corresponding to different rows of an activation volume in an activation cache included in the LMD, (2) for each activation matrix, directing a matrix multiplication unit (MMU) included in the hardware accelerator to execute a matrix multiplication operation (MMU) using the filter matrix and the activation matrix, (3) loading an additional filter matrix into the filter cache, and (4) directing the MMU to execute a plurality of additional MMOs, each additional MMO using one filter matrix included in the filter cache and one activation matrix included in the activation cache, such that the MMU reuses the filter matrix for at least one additional MMO and uses the additional filter matrix for a different additional MMO.

Abstract: Examples are disclosed that relate to motion compensation on a single photon avalanche detector (SPAD) array camera. One example provides a method enacted on an imaging device comprising a SPAD array camera and a motion sensor, the SPAD array camera comprising a plurality of pixels. The method comprises acquiring a plurality of subframes of image data. Each subframe of image data comprises a binary value for each pixel. Based upon motion data from the motion sensor, the method further comprises determining a change in pose of the imaging device between adjacent subframes, applying a positional offset to a current subframe based upon the motion data to align a location of a stationary imaged feature in the current subframe with a location of the stationary imaged feature in a prior subframe to create aligned subframes, summing the aligned subframes to form an image, and outputting the image.

Abstract: A pattern recognition system including an image gathering unit that gathers at least one digital representation of a field, an image analysis unit that pre-processes the at least one digital representation of a field, an annotation unit that provides a visualization of at least one channel for each of the at least one digital representation of the field, where the image analysis unit generates a plurality of image samples from each of the at least one digital representation of the field, and the image analysis unit splits each of the image samples into a plurality of categories.

Abstract: A reticle inspection system may include two or more inspection tools to generate two or more sets of inspection images for characterizing a reticle, where the two or more inspection tools include at least one reticle inspection tool providing inspection images of the reticle. The reticle inspection system may further include a controller to correlate data from the two or more sets of inspection images to positions on the reticle, detect one or more defects of interest on the reticle with the correlated data as inputs to a multi-input defect detection model, and output defect data associated with the defects of interest.

Abstract: Systems and methods include a method for deblending signal and noise data. A shot domain for actual sources, a receiver domain for virtual sources, and a receiver domain for actual sources are generated from blended shot data. A dictionary of signal atoms is generated. Each signal atom includes a small patch of seismic signal data gathered during a small time window using multiple neighboring traces. A dictionary of noise atoms is generated. Each noise atom includes a small patch of seismic noise data gathered during a small time window using multiple neighboring traces. A combined signal-and-noise dictionary is generated that contains the signal atoms and the noise atoms. A sparse reconstruction of receiver domain data is created from the combined signal-and-noise dictionary. The sparse reconstruction is split into deblended data and blending noise data based on atom usage to create deblended shot domain gathers for actual sources.

Abstract: The present disclosure relates to an image retouching system that automatically retouches digital images by accurately correcting face imperfections such as skin blemishes and redness. For instance, the image retouching system automatically retouches a digital image through separating digital images into multiple frequency layers, utilizing a separate corresponding neural network to apply frequency-specific corrections at various frequency layers, and combining the retouched frequency layers into a retouched digital image. As described herein, the image retouching system efficiently utilizes different neural networks to target and correct skin features specific to each frequency layer.

Abstract: An image processing apparatus includes an image acquisition unit configured to acquire a plurality of temporally different images each of which has degraded by a turbulence, a parameter acquisition unit configured to acquire a learned network parameter, and a measurement unit configured to measure a turbulence strength from the plurality of images using the network parameter and a neural network.

Abstract: Disclosed are a method and an apparatus for adapting parameters of a neural network. The method includes selecting one or more dimensions for a weight parameter of each of at least one layer of the neural network, determining a dimension value and a corresponding target value in each dimension of the weight parameter, and padding the weight parameter in a case where the dimension value in at least one dimension of the weight parameter is less than the corresponding target value, the dimension value in each dimension of the weight parameter after the padding being equal to the corresponding target value.

Abstract: Implementations described and claimed herein provide systems and methods for sensor calibration. In one implementation, an image and a depth map of a field around a mobile device are obtained. The image is captured using a first sensor, and the depth map is captured using a second sensor. A position and a reflected radiation intensity of a calibration surface are detected in the field using the depth map. A first portion and a second portion of the calibration surface representing a background color and a foreground color are isolated based on the reflected radiation intensity. The calibration surface is detected in the image using the position of the calibration surface in the depth map and a calibration transform between the first sensor and the second sensor. The calibration transform is optimized based on an overlap of the foreground color and/or the background color between the depth map and the image.

Abstract: The present application provides an imaging method and system, and a non-transitory computer-readable storage medium. The imaging method comprises preprocessing projection data to obtain a predicted image of a truncated portion; performing forward projection on the predicted image to obtain predicted projection data of the truncated portion; and performing image reconstruction using the projection data obtained by forward projection and projection data of an untruncated portion.

Abstract: A method to correct a digital image to reverse the effect of signal diffusion among pixels of the digital image. For a target pixel j of the digital image, a set of signal values and a set of signal amplitudes are received, each corresponding to a set of kernel pixels i surrounding and including the target pixel j. For each kernel pixel i, a weighting coefficient is computed based on the signal amplitude of that kernel pixel i and on the signal amplitude of the target pixel j. A linear combination of signal values corresponding to the set of kernel pixels i is computed, wherein the signal value for each pixel i is weighted by the weighting coefficient corresponding to that pixel i. The linear combination is stored in volatile memory of an electronic device as a corrected signal value for the target pixel j.

Abstract: The present application discloses an image transmission device and method. The image transmission device includes a receiver configured to receive pixel data in image data from a camera in sequence and buffer the pixel data into a memory, and determine, upon reception of a line of pixel data, a line number of the line of pixel data in the image data and a frame number of the image data; and a processor configured to obtain the line of pixel data, the line number of the line of pixel data and the frame number of the image data from the receiver, package the obtained line of pixel data, line number of the line of pixel data and frame number of the image data into a data packet, and transmit the data packet to a server.

Abstract: A method of optimizing at least one IQMC parameter value for an IQMC includes: generating a set of tested IQMC candidate parameter values by performing an iterative method including selecting a first IQMC candidate parameter value for the at least one parameter of the IQMC; determining, using the first IQMC candidate parameter value, a performance metric value that comprises at least one of (i) an image rejection ratio (IRR) value, (ii) a signal-to-interference-plus-noise ratio (SINR) value, or (iii) a signal-to-image ratio (SImR) value; and determining a second IQMC candidate parameter value that is an update to the first IQMC candidate parameter value. The method of optimizing at least one IQMC parameter value for an IQMC further includes determining an IQMC candidate parameter value of the set of tested IQMC candidate parameter values that optimizes the performance metric.

Abstract: An example device for filtering a decoded block of video data includes one or more processing units configured to construct a plurality of filters for classes of blocks of a current picture of video data. To construct the plurality of filters for each of the classes, the processing units are configured to determine a value of a flag that indicates whether a fixed filter is used to predict a set of filter coefficients of the class, and in response to the fixed filter being used to predict the set of filter coefficients, determine an index value into a set of fixed filters and predict the set of filter coefficients of the class using a fixed filter of the set of fixed filters identified by the index value.

Abstract: The present application provides a method and device for obtaining a predicted image of a truncated portion, an imaging method and system, and a non-transitory computer-readable storage medium. The method for obtaining a predicted image of a truncated portion comprises preprocessing projection data to obtain, by reconstruction, an initial image of the truncated portion; and calibrating the initial image based on a trained learning network to obtain the predicted image of the truncated portion.

Abstract: Disclosed is a display apparatus displays a first image selected based on a user input, obtains, from a server, a second image having a transfer style, the transfer style preferred by a user from among a plurality of transfer styles, and displays the second image.

Abstract: The present invention discloses an image adaptive noise reduction method and a device thereof. The method includes: dividing an original image into a plurality of sub-blocks; performing a space conversion for all the sub-blocks; performing a significance analysis to obtain a significant characteristic map; performing a threshold segmentation on all the significant characteristic maps by a significant standard value to obtain a significant characteristic region and a non-significant characteristic region; performing adaptive filtering on the significant characteristic region and maxing an original image in the non-significant characteristic region to obtain a mixed image; and performing an image space inverse conversion for the mixed image, and outputting a final image. The present invention uses the method of dividing the image, based on the significant characteristics of the image, reducing noise reduction in the non-significant characteristic region, saving algorithm running time and hardware resources.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a first photograph. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the first photograph. The probability of blurriness is based on an analysis of a portion of the first photograph, the portion excluding a region of the first photograph associated with an auto-focus operation. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph to replace the first photograph when the probability of blurriness exceeds a blurriness threshold.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: A non-local means method is insufficient in its noise reduction effect or edge retainability due to a perfect match between blocks in a case where a reference pixel matches a target pixel. Therefore, information on a target region and plural reference regions is obtained for the target pixel. Whether the target region matches any one of the reference regions is determined from the obtained information. Switching between weight derivation methods based on similarity between the target region and the reference region is performed according to a determined result.

Abstract: User interactions with information handling systems can be improved by providing real-time feedback to the user during use of an application on the information handling system. A user's focused attention can be determined and the user interface elements in an application window be customized to the user. The user's focus attention thus changes the display of user interface elements on the screen. Using such a technique, user interface elements of lower importance can be de-emphasized or removed from the application window, which improves the user's access to user interface elements of greater importance.

Abstract: Methods and apparatus for providing adaptive lens shading correction in at least a portion of a shaded image frame perform luminance lens shading correction for a portion of the shaded image frame and detect a color flat area in the shaded image. The methods and apparatus generate a frequency-based color shading profile that includes color shading parameters including an amplitude parameter and phase parameter corresponding to hue distributions from experimental shading data and generate an unshaded image by performing color shading correction on the detected color flat areas of the shaded image frame using the color shading parameters.

Abstract: Isolating and amplifying a conversation between selected participants is provided. A plurality of spectral masks is received. Each spectral mask in the plurality corresponds to a respective participant in a selected group of participants included in a conversation. A composite spectral mask is generated by additive superposition of the plurality of spectral masks. The composite spectral mask is applied to sound captured by a microphone to filter out sounds that do not match the composite spectral mask and amplifying remaining sounds that match the composite spectral mask.

Abstract: An image processing apparatus includes a correction unit configured to correct first image data acquired via an image pickup optical system and to generate second image data using a filter generated based on a characteristic of the image pickup optical system, a decomposition unit configured to decompose each of the first image data and the second image data into a first frequency component and a second frequency component, a combination unit configured to combine the first frequency component of the first image data and the first frequency component of the second image data with each other, and a generation unit configured to generate third image data based on a frequency component including the first frequency component combined by the combination unit and the second frequency component of the second image data.

Abstract: In one implementation, a method includes: obtaining an input image, wherein the input image is captured by an image sensor having a rotational orientation with respect to a direction of gravity; obtaining a gravity direction estimation associated with the rotational orientation of the sensor; generating, from the input image, a rotationally preprocessed input image by applying one or more transformations to the input image based on the gravity direction estimation; providing the rotationally preprocessed input image to the machine learning sub-system; and identifying, using the machine learning sub-system, a visual feature within the rotationally preprocessed input image.

Abstract: The present invention discloses a noise processing method and apparatus. The method includes: selecting a pixel in an area array, which is formed by arranging a plurality of pixels in a matrix; obtaining pre-stored depth information corresponding to the pixel; performing pixel dispersion calculation according to the depth information corresponding to the pixel, to obtain at least one dispersion of the pixel, the dispersion being used to indicate a degree of dispersion between the pixel and another pixel in the area array; and identifying the pixel as a noise and removing the pixel from the area array when the at least one dispersion of the pixel is greater than a preset threshold.

Abstract: Various techniques are provided for reducing noise in captured image frames. In one example, a method includes determining row values for image frames comprising scene information and noise information. The method also includes performing first spectral transforms in a first domain on corresponding subsets of the row values to determine first spectral coefficients. The method also includes performing second spectral transforms in a second domain on corresponding subsets of the first spectral coefficients to determine second spectral coefficients. The method also includes selectively adjusting the second spectral coefficients. The method also includes determining row correction terms based on the adjusted second spectral coefficients to reduce the noise information of the image frames. Additional methods and systems are also provided.

Abstract: Described herein are techniques for improving the effectiveness of depth culling. In a first technique, a binner is used to sort primitives into depth bins. Each depth bin covers a range of depths. The binner transmits the depth bins to the screen space pipeline for processing in near-to-far order. Processing the near bins first results in the depth buffer being updated, allowing fragments for the primitives in the farther bins to be culled more aggressively than if the depth binning did not occur. In a second technique, a buffer is used to initiate two-pass processing through the screen space pipeline. In the first pass, primitives are sent down to update the depth block and are then culled. The fragments are processed normally in the second pass, with the benefit of the updated depth values.

Abstract: Methods are provided for training and validating deep learning models for visual search and related tasks as they pertain to fashion items such as garments. The methods presented address the special needs of visual search as it pertains to fashion and related industries by generating large numbers of synthetic images or videos for training deep learning models, and also for validating those models. Given a 3D model of a target garment, the methods select appropriate 3D models for humans and scenes, and also selects various values for the customizable parameters of each of the 3D models, and then renders an image or video. A dataset comprising such synthetic images can be blended with real-world tagged images to create composite datasets for training.

Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

Abstract: Techniques for reconstructing three-dimensional face images are described herein. The disclosed techniques include acquiring a real two-dimensional face key point and a predicted two-dimensional face key point; solving a first loss function consisting of the real two-dimensional face key point, the predicted two-dimensional face key point and a preset additional regular constraint term to iteratively optimize an expression coefficient, where the additional regular constraint term is used for constraining the expression coefficient such that the expression coefficient represents a real state of a face; and reconstructing a three-dimensional face image based on the expression coefficient.

Abstract: A level of detail-transformation adaptive enhancement method for image contrast includes: dividing a remote sensing image into a plurality images of different levels of detail, the lowest level of detail defined as L and the highest level of detail defined as H, and gradually transforming an image Imagei of an arbitrary level of detail i between the image ImageH of the highest level of detail H and the image ImageL of the lowest level of detail L from ImageL to ImageH through the following equation: Imagei=Ri×ImageH+(1?Ri)×ImageL. The image ImageH of the highest level of detail H is an image ImageACE produced with adaptive contrast enhancement processing, or an image produced with a contrast enhancement method such as Gaussian or histogram equalization; the image ImageL of the lowest level of detail L is an image ImageLCE produced by common linear contrast enhancement.

Abstract: To improve image quality of an image after correction, An image processing device 100 includes: a correction unit 110 for generating a plurality of correction images from an image by using a plurality of correction methods; an evaluation unit 120 for evaluating the image or each of the plurality of correction images for each of areas constituting the image or the correction image; and a synthesis unit 130 for synthesizing the plurality of correction images according to an evaluation result by the evaluation unit 120.