A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

This disclosure is directed to an image authenticity detection method and apparatus. The method includes: obtaining an image; removing low-frequency information from the image to obtain first image information of the image; denoising the first image information to obtain second image information; determining, based on a difference between the first image information and the second image information, a fixed pattern noise feature map corresponding to the image; analyzing distribution of fixed pattern noise in the fixed pattern noise feature map, the fixed pattern noise being inherent noise from a camera sensor and not interfered by image content; and detecting, based on the distribution, authenticity of the image to obtain an authenticity detection result of the image.

This disclosure is directed to an image authenticity detection method and apparatus. The method includes: obtaining an image; removing low-frequency information from the image to obtain first image information of the image; denoising the first image information to obtain second image information; determining, based on a difference between the first image information and the second image information, a fixed pattern noise feature map corresponding to the image; analyzing distribution of fixed pattern noise in the fixed pattern noise feature map, the fixed pattern noise being inherent noise from a camera sensor and not interfered by image content; and detecting, based on the distribution, authenticity of the image to obtain an authenticity detection result of the image.

A point cloud data processing apparatus (11) includes: a memory (21) configured to store point cloud data (7) and pieces of image data (5), with positions of pixels of at least any one piece of image data (5) among the pieces of image data (5) being associated with points that constitute the point cloud data (7); and a processor, the processor being configured to cause a display unit (9) to display the point cloud data such that three-dimensional rotation, three-dimensional movement, and rescaling are enabled, accept a designation of a specified point in the point cloud data (7) displayed on the display unit (9), select a region of a target object including a region corresponding to the specified point, on the piece of image data (5), and assign the same attribute information to points, in the point cloud data (7), corresponding to the region of the target object.

A point cloud data processing apparatus (11) includes: a memory (21) configured to store point cloud data (7) and pieces of image data (5), with positions of pixels of at least any one piece of image data (5) among the pieces of image data (5) being associated with points that constitute the point cloud data (7); and a processor, the processor being configured to cause a display unit (9) to display the point cloud data such that three-dimensional rotation, three-dimensional movement, and rescaling are enabled, accept a designation of a specified point in the point cloud data (7) displayed on the display unit (9), select a region of a target object including a region corresponding to the specified point, on the piece of image data (5), and assign the same attribute information to points, in the point cloud data (7), corresponding to the region of the target object.

A computer-implemented method for analyzing an image to detect an article of interest (AOI) comprises processing the image using a machine learning algorithm configured to detect the AOI and comprising a convolutional neural network (CNN); and displaying the image with location of the AOI being indicated if determined to be present. The CNN comprises an input module configured to receive the image and comprising at least one convolutional layer, batch normalization and a nonlinear activation function; an encoder thereafter and configured to extract features indicative of a present AOI to form a feature map; a decoder thereafter and configured to discard features from the feature map that are not associated with the present AOI and to revert the feature map to a size matching an initial image size; and a concatenation module configured to link outputs of the input module, the encoder and the decoder for subsequent segmentation.

A computer-implemented method for analyzing an image to detect an article of interest (AOI) comprises processing the image using a machine learning algorithm configured to detect the AOI and comprising a convolutional neural network (CNN); and displaying the image with location of the AOI being indicated if determined to be present. The CNN comprises an input module configured to receive the image and comprising at least one convolutional layer, batch normalization and a nonlinear activation function; an encoder thereafter and configured to extract features indicative of a present AOI to form a feature map; a decoder thereafter and configured to discard features from the feature map that are not associated with the present AOI and to revert the feature map to a size matching an initial image size; and a concatenation module configured to link outputs of the input module, the encoder and the decoder for subsequent segmentation.

A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.