Abstract: The present invention relates to a hand position estimation method and system for estimating a 3-dimensional hand position for quick hand movement, on the basis of domain transfer learning from a depth image to an infrared image, the hand position estimation method comprising the steps of: processing a depth image and an infrared image for hand movement; synthesizing a depth map with the infrared image by using a hand image generator (HIG), and estimating skeletal positions of hand joints from each of the depth map and an infrared map; and calculating a 3-dimensional hand position by using the skeletal positions and the center of a hand depth image.

Abstract: Existing Magnetic Resonance (MR) analysis approaches are either manual, which is time consuming and error prone, or automatic yet fail to identify correct landmarks. The disclosure herein generally relates method and system for MR image analysis. The system trains and uses a Generative adversarial network (GAN) for the MR image analysis. A generator network of the GAN generates a heatmap of a plurality of landmarks, by processing input data. Further, a discriminator network of the GAN, which has gradients updated based on a Modified Discriminator Loss (MDL) calculated based on a foreground pixel loss function and a standard discriminator loss, predicts each of a plurality of patches in a second input data as one of real and fake, based on presence of the one or more foreground pixel regions in the landmark heatmap in the ground-truth heatmap identified based on the foreground pixel loss function.

Abstract: A color measurement instrument for measuring colors of areas of interest of a target object, according to one aspect of the present invention, includes a spectral engine, a color camera, a display, and a processor. The spectral engine has a first field of view. The camera has a second field of view, the second field of view being larger than the first field of view and encompassing the first field of view. The processor is coupled to the spectral engine, camera and display. The processor to causes the spectral engine to make a color measurement of an area of interest on the target object within the first field of view and obtains from the camera a measurement image comprising at least a portion of the second field of view including the first field of view. The processor further causes the display of color values from a measurement made by the spectral engine along with at least a portion of the measurement image including the first field of view.

Abstract: Systems and techniques are provided for generating depth information. For example, a process can include obtaining a first feature volume including visual features corresponding to each respective frame included in a first set of frames. A first query generator network can generate reconstruction features associated with a reconstructed feature volume corresponding to the first feature volume. Based on the first feature volume, a second query generator network can generate motion features associated with predicted future motion corresponding to the first feature volume. An initial depth prediction can be generated for each respective frame based on cross-attention between features of a depth prediction decoder, the reconstruction features, and the motion features. A refined depth prediction can be generated for each respective based on cross-attention between the initial depth prediction, the reconstruction features, and the motion features.

Abstract: This application relates to the field of image processing, and provides an image processing method and an electronic device. The electronic device captures a first image—that is an image captured by a camera lens. The first image includes a first shot object and a second shot object. The electronic device determines a first scene corresponding to the first shot object and a second scene corresponding to the second shot object. The electronic device determines a first LUT based on the first scene, and determines a second LUT based on the second scene. The electronic device processes the first image based on the first LUT to obtain a second image, and processes the first image based on the second LUT to obtain a third image. The electronic device displays a fourth image. The fourth image includes a part of the second image and a part of the third image.

Abstract: A method for analyzing fashion attributes using large amounts of pieces of image data is provided. The method includes: collecting image data including at least one item; statistically analyzing on the image data based on an attribute classification AI model; and visualizing the results of the statistical analysis, wherein the attribute classification AI model is a model for detecting said at least one item included in the image data, and for recognizing, labeling and classifying fashion attributes of the at least one item.

Abstract: An image processing apparatus for forming an enhanced image is disclosed. The apparatus comprises one or more processors configured to: receive one or more input images; form, from each of the one or more input images, a respective feature representation, each feature representation representing features of the respective input image; and subject the one or more feature representations to a symmetric pooling operation to form an enhanced image from at least some of the features of the one or more feature representations identified by the symmetric pooling operation. The apparatus may generate images with increased photoreceptive dynamic range, increased bit depth and signal-to-noise ratio, with less quantization error and richer colour representation.

Abstract: An image processing device includes: a model storage unit storing a model for estimating, from image data, a first probability map indicating feature points of a workpiece in the image data, and a second probability map indicating areas between the feature points; an estimation unit estimating a first probability map and a second probability map on the basis of the image data using the model stored in the model storage unit; a feature point estimation unit estimating feature points of the same workpiece in the image data on the basis of the first probability map and the second probability map; a position and orientation estimation unit estimating the position and orientation of the workpiece in the image data on the basis of the result of the estimation by the feature point estimation unit; and an output unit outputting the result of the estimation by the position and orientation estimation unit.

Abstract: Implementations of the present specification provide an image data processing method, an image data recognition method, a training method for an image recognition model, an image data processing apparatus, an image data recognition apparatus, and a training apparatus for an image recognition model. During image data processing, data anonymization processing is performed on image data based on frequency domain transform to obtain anonymized image data of the image data. The obtained anonymized image data includes a subgraph data set. Each subgraph data in the subgraph data set corresponds to a different frequency. Then, image blending processing is performed on the obtained anonymized image data based on data augmentation, to obtain augmented anonymized image data.

Abstract: An image processing method is performed by a computer device, which includes: converting initial image data into super-resolution image data using a trained image processing network, a resolution of the super-resolution image data being greater than or equal to a target resolution; performing image quality enhancement processing on the super-resolution image data using the trained image processing network, to obtain first enhanced image data; when there is a face image in the first enhanced image data, performing face enhancement on the face image in the first enhanced image data using the trained image processing network to obtain second enhanced image data; and performing image sharpening processing on the second enhanced image data using the trained image processing network to obtain sharpened image data.

Abstract: Described are methods and systems for training a system for detecting anomalies in images of documents in a class of documents. A plurality of training document images of training documents in a class of documents are obtained. For each training document image, the training document image is segmented into a plurality of region of interest (ROI) images, each ROI image corresponding to a respective ROI of the training document. For each ROI image, a plurality of transformations are applied to the ROI image to generate respective transform-specific features for the ROI image and respective transform-specific anomaly scores from the transform-specific features. Based on the respective anomaly scores of the plurality of training document images, a transform-specific threshold is computed for each transformation to separate document images containing an anomaly from document images not containing an anomaly.

Abstract: An image processing apparatus includes at least one processor and at least one memory containing instructions that cause the at least one processor to be configured to function as an acquisition unit, a decision unit, and a generation unit. The acquisition unit is configured to acquire a plurality of images including at least one High Dynamic Range (HDR) image. The decision unit is configured to decide a peak luminance value of the composite image. The generation unit is configured to generate the composite image by executing additive composition processing using the plurality of images. The generation unit controls the additive composition processing so that a signal level of each pixel of the composite image falls within an output dynamic range whose maximum value is set to a signal level corresponding to the peak luminance value decided by the decision unit.

Abstract: Image processing systems and image processing techniques leveraging neural networks (e.g., convolutional neural networks (CNNs)) for image restoration tasks (e.g., for demosaicing tasks) are described. In certain aspects, Mixture of Experts (MoE) techniques may be employed, where multiple different expert networks are used to divide a problem space (e.g., image reconstruction tasks) into homogenous regions. For example, each MoE module may reconstruct a certain problem in an image, and a gating component may activate certain MoE modules to provide a reconstructed image. In some aspects, training and optimization techniques are described for each expert of the MoE architecture, to increase individual performance (e.g., a sub-task for each expert of an image processing system may be imposed in a residual manner, a gating function may be trained, etc.).

Abstract: The present invention provides an image processing apparatus operable to process first and second image data from an image capturing unit capable of amplifying, with different gains, a signal obtained by photoelectric conversion and outputting the first and second image data, wherein the apparatus comprises an acquiring unit configured to acquire an exposure difference between the first and second image data; a correcting unit configured to determine a correction amount for reducing a noise difference between the first and second image data based on the exposure difference acquired by the acquiring unit and correct at least one of the first and second image data based on the determined correction amount; and a generating unit configured to generate a composite image by compositing the first and second image data, at least one of which has been corrected by the correcting unit.

Abstract: Disclosed herein are an emotion recognition method and method based on context information. The emotion recognition method includes detecting information corresponding to an emotion recognition subject from an input image, extracting a recognition subject feature based on the information corresponding to the emotion recognition subject, extracting a context feature based on the input image, storing the recognition subject feature and the context feature in a short-term memory, and storing the context feature in a long-term memory.

Abstract: An image processing method and an image processing apparatus are provided. In the method, first encoding is performed on an input image, to output a first noisy image. De-noising is performed on the first noisy image, to output a first de-noised image. De-noising is performed on the input image according to the first de-noised image, to output a first image.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for determining a node of a decision tree. The method includes determining multiple features of multiple modals corresponding to input information. The method further includes generating a multi-modal feature representation by combining the multiple features of the multiple modals. The method further includes determining a target path in a decision tree that is associated with the multi-modal feature representation, the decision tree comprising multiple nodes. The method further includes determining, in the target path based on the multi-modal feature representation, a target node associated with the input information and used to indicate a question or an answer. This method enables the fusion of feature representations corresponding to input information of different modals to determine a multi-modal feature representation. In this way, it is possible to determine richer and more accurate user intentions.

Abstract: A method for preparing data for identifying analytes in a sample, in which in an experiment one or more analytes are colored with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting the multiple markers using a camera, which for each coloring round generates at least one image containing multiple pixels and color values assigned thereto, the image including colored signals and uncolored signals, wherein a colored signal is a pixel having a color value that originates from a marker, and an uncolored signal is a pixel having a color value that is not based on a marker, and storing the color information of the particular coloring rounds for evaluating the color information.

Abstract: Provided are an imaging method and apparatus, and a medium. The method includes: for a target field of view, performing photographing and quantification at a first resolution, to obtain a first image that has a first bit width; for the target field of view, performing photographing and differential processing at a second resolution, to obtain a second image that has a second bit width, wherein the differential processing includes: for a pixel point obtained by photographing at the second resolution, quantifying the difference between the pixel point and a neighboring pixel point of the pixel point, to obtain a quantified difference a value of a corresponding pixel point in the second image; and fusing the first image with the second image, to obtain a third image, wherein the first resolution is lower than the second resolution, and the first bit width is higher than the second bit width.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.