Abstract: A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.

Abstract: Methods and systems are provided for implementing preprocessing operations and augmentation operations upon image datasets transformed to frequency domain representations, including decoding images of an image dataset to generate a frequency domain representation of the image dataset; performing a resizing operation based on resizing factors on the image dataset in a frequency domain representation; performing a reshaping operation based on reshaping factors on the image dataset in a frequency domain representation; and performing a cropping operation on the image dataset in a frequency domain representation. The methods and systems may further include performing an augmentation operation on the image dataset in a frequency domain representation. Methods and systems of the present disclosure may free learning models from computational overhead caused by transforming image datasets into frequency domain representations.

Abstract: A system for acquiring images of products in a retail store is disclosed. The system may include a first housing configured for location on a retail shelving unit, and an image capture device included in the first housing and configured relative to the first housing such that an optical axis of the image capture device is directed toward an opposing retail shelving unit when the first housing is fixedly mounted on the retail shelving unit. The system may further include a second housing configured for location on the retail shelving unit separate from the first housing, the second housing containing a processor configured to control the image capture device and a network interface for communicating with a remote server. The system may further include a data conduit configured to enable transfer of control signals from the processor to the image capture device.

Abstract: A system comprises an encoder configured to compress attribute and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, an encoder generates time-consistent patches for multiple version of the point cloud at multiple moments in time and uses the time-consistent patches to generate image based representations of the point cloud at the multiple moments in time.

Abstract: A patient interface for supplying a flow of breathable gas to the airways of a patient may comprise a heat and moisture exchanger (HME). The HME may be positioned in a flow path of the flow of breathable gas. The HME may absorb heat and moisture from gas exhaled by the patient and the incoming flow of breathable gas to be supplied to the patient's airways may be heated and moisturized by the heat and moisture held in the HME.

Abstract: In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Abstract: A spectrally encoded flow cytometry (SEFC) technique for imaging blood in the microcirculation. Since the dependency of one of the axes of the image on time prevents effective quantification of essential clinical parameters, the optical path in an SEFC system is split into two parallel imaging lines, followed by data analysis for recovering the flow speed from the multiplexed data. The data analysis may be auto-correlation of a pair of images obtained from a sequence of images of the imaged blood vessel.

Abstract: Various aspects of a system, a method, and a computer program product for determining a distance to the object on a road are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to receive visual data, location data and motion data of the vehicle corresponding to the first instance in time, and map data corresponding to the location data. The processor may be configured to calculate a distance of the vehicle from the object based on the visual data. The processor may be further configured to validate the location data, the motion data, and the calculated distance of the vehicle from the object, based on the map data. The processor may be further configured to generate output data corresponding to the object, based on the validated location data, the validated motion data, and the validated distance of the vehicle from the object.

Abstract: Image encoding within a pixelated privacy mask area is adapted in order to reduce flickering during movement of a camera. Motion vectors are set equal to the movement of the camera, and residuals are set to zero, thereby encoding pixel blocks within the privacy mask area as copies of corresponding pixel blocks in a reference image.

Abstract: A method performed by a computing device includes obtaining a set of image segment identigens for image segments of an image to produce sets of image segment identigens. A set of image segment identigens is a set of possible interpretations of a first image segment of the image segments. The method further includes identifying a subset of valid image segment identigens of each set of image segment identigens by applying identigen rules to the sets of image segment identigens to produce subsets of valid image segment identigens. Each valid image segment identigen of a subset of valid image segment identigens represents a most likely interpretation of a corresponding image segment. The method further includes generating an image entigen group utilizing the subsets of valid image segment identigens, where the image entigen group represents a most likely interpretation of the image.

Abstract: A method of manufacturing a patient interface for sealed delivery of a flow of air at a continuously positive pressure with respect to ambient air pressure to an entrance to the patient's airways includes collecting anthropometric data of a patient's face. Anticipated considerations are identified from the collected anthropometric data during use of the patient interface. The collected anthropometric data is processed to provide a transformed data set based on the anticipated considerations, the transformed data set corresponding to at least one customised patient interface component. At least one patient interface component is modeled based on the transformed data set.

Abstract: A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

Abstract: Described are methods and devices for applying a color gamut mapping process on a first image to generate a second image, where the content of the first and second images is similar but the respective color spaces of the first and second images are different. The color gamut mapping process may be controlled using a color gamut mapping mode obtained from a bitstream where the color gamut mapping mode belongs to a set comprising at least two preset modes and an explicit parameters mode. If the obtained color gamut mapping mode is the explicit parameters mode and the color gamut mapping process is not enabled for the explicit parameters mode, the color gamut mapping process may be controlled by a substitute color gamut mapping mode determined from additional data.

Abstract: Techniques related to implementing fully convolutional networks for semantic image segmentation are discussed. Such techniques may include combining feature maps from multiple stages of a multi-stage fully convolutional network to generate a hyper-feature corresponding to an input image, up-sampling the hyper-feature and summing it with a feature map of a previous stage to provide a final set of features, and classifying the final set of features to provide semantic image segmentation of the input image.

Abstract: To enable a good HDR image or video coding technology, being able to yield high dynamic range images as well as low dynamic range images, we invented a method of encoding a high dynamic range image (M_HDR), comprising the steps of: converting the high dynamic range image to an image of lower luminance dynamic range (LDR_o) by applying a) scaling the high dynamic range image to a predetermined scale of the luma axis such as [0,1], b) applying a sensitivity tone mapping which changes the brightnesses of pixel colors falling within at least a subrange comprising the darker colors in the high dynamic range image, c) applying a gamma function, and d) applying an arbitrary monotonically increasing function mapping the lumas resulting from performing the steps b and c to output lumas of the lower dynamic range image (LDR_o); and outputting in an image signal (S_im) a codification of the pixel colors of the lower luminance dynamic range image (LDR_o), and outputting in the image signal (S_im) values encoding the f

Abstract: A data labeling method, apparatus and system are provided. The method includes: sampling a data source according to an evaluation task for the data source to obtain sampled data; generating a labeling task from the sampled data; sending the labeling task to a labeling device; and receiving a labeled result of the labeling task from the labeling device. As such, an automatic evaluation of data can be implemented by using the evaluation task, and evaluation efficiency is improved.

Abstract: An apparatus includes an interface and a processor. The interface may be configured to receive images from a capture device. The processor may be configured to (i) extract features from the images, (ii) generate fused disparity maps in response to the features extracted, (iii) generate regenerated images by performing warping on a first subset of the images based on (a) the fused disparity maps and (b) first parameters, (iv) perform a comparison of a sample image to one of the first subset of the images, (v) perform a classification of the sample image based on second parameters, and (vi) update the first parameters and the second parameters in response to whether the classification is correct, wherein (a) the classification comprises indicating whether the sample image is one of a second subset of the images or one of the regenerated images and (b) the regenerated images are used as a training dataset.

Abstract: An approach for determining similar text documents. The approach can calculate a first set of vectors for a first cluster of text documents and a first comparison vector for a text document of interest. The approach can select a subset of text documents from the cluster of text documents based on comparing the vectors from the first set of vectors to the first comparison vector and picking a predetermined number of closest comparison text documents. The approach can calculate a second set of vectors for the subset of documents and second comparison vector for the document of interest. The approach can generate similarity ratings for the subset of documents based on pairwise comparisons of the second comparison vector and the second set of vectors. The approach can generate a ranked list of the second cluster of text documents based on the similarity ratings.

Abstract: The disclosure provides a method and an apparatus for detecting a mobile traffic light. The method includes: obtaining a set of positive sample images with the mobile traffic light and a set of negative sample images without the mobile traffic light; extracting current training data respectively from the set of positive sample images and the set of negative sample images based on a preset sampling ratio; detecting the mobile traffic light of each image in the current training data with a first preset detection model to determine a detection result of each image; determining current classification loss and detecting frame regression loss based on a difference between the detection result of each image and labeled data; and generating a detection model for detecting the mobile traffic light in case that the current classification loss and the current detecting frame regression loss meet a preset condition.

Abstract: This invention relates to a method of classification of images among different classes comprising: performing a dimensionality reduction step for said different classes on a training set of images whose classes are known, and then classifying one or more unknown images among said different classes with reduced dimensionality, said dimensionality reduction step being performed on said training set of images by machine learning including processing, for at least a first matrix and for at least a second matrix, a parameter representative of a product of two first and second matrices to assess to which given classes several first given images respectively belong: first matrix representing the concatenation, for said several first given images, of the values of the pixels of each said first given image, second matrix representing the concatenation, for said several first given images, of the values of differences between the pixels of each said first given image and the pixels of a second given image different fro