Abstract: A method for depth sensing from an image of a projected pattern is performed at an electronic device with one or more processors and memory. The method includes receiving an image of a projection of an illumination pattern; for a portion of the image, selecting a candidate image of a plurality of candidate images by comparing the portion of the image with a plurality of candidate images; and determining a depth for the portion of the image based on depth information associated with the selected candidate image. Related electronic devices and computer readable storage medium are also disclosed.

Abstract: Systems, methods, and computer readable media for performing task assignment, completion, and management within a crowdsourced surveillance platform. A remote server may identify targets for image capture and may assign capture tasks to users based on travel plans of the user. Users may be assigned task to capture image of target locations lying along a travel path. The remote server may aggregate data related to the captured images and use it to update a map and log changes to the target location over time.

Abstract: The present embodiments include a system and method for processing multi-dimensional images in real time through the use of third-party servers and cloud computing. The system includes a data acquisition processor, a data storage unit, an administrator processor, and a server. The server can be a cloud-based server. The method includes receiving multi-dimensional imaging data, compressing and blending the image data, transmitting the image data to a server, decompressing and deblending the data, generating multi-dimensional images, and transmitting the imaging data back to the administrator processor.

Abstract: System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from sensor data according to whether the sensor data includes sparse depth data. The method includes selectively injecting the depth features into a depth model. The method includes generating a depth map from at least a monocular image using the depth model that is guided by the depth features when injected. The method includes providing the depth map as depth estimates of objects represented in the monocular image.

Abstract: An individual identification system includes: a storing unit for storing an image capture parameter in association with data characterizing a surface of a reference object; an acquiring unit that, when data characterizing a surface of an object to be matched is input, calculates an approximation degree between the input data and each data stored in the storing unit, and acquires the image capture parameter applied to the object to be matched from the storing unit based on the calculated approximation degree; a condition setting unit that sets an image capture condition determined by the acquired image capture parameter; an image capturing unit that acquires an image of the surface of the object to be matched under the set image capture condition; an extracting unit that extracts a feature value from the acquired image; and a matching unit that matches the extracted feature value against a registered feature value.

Abstract: A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Abstract: A method of image processing converts images of different brightness to a common brightness range, the images representing at least partly the same scene. One of images including a local block with movement is selected for forming a composed image from the images. One or more corresponding blocks corresponding to the local block are determined in the images. Each of said one or more corresponding blocks is weighted with at least one of the following: similarity with respect to the local block, a distance from a location of the local block, saturation of the one or more corresponding blocks, and noise of the one or more corresponding blocks. The local block and at least one of the one or more corresponding blocks are combined, or the local block is replaced by at least one of the one or more corresponding blocks based on the weighting for forming the composed image from the images.

Abstract: A method for classifying measuring points of a point cloud ascertained by at least one sensor, in particular, a point cloud ascertained from a LIDAR sensor, a radar sensor and/or a camera sensor, via a control unit. Local surface vectors to adjacent measuring points are ascertained for each measuring point of the point cloud. For each local surface vector, respectively one angle is calculated between the local surface vectors with respect to a gravity vector. A maximal surface vector having a maximal angle with respect to the gravity vector and a standardized surface vector are ascertained for each measuring point of the point cloud based on the calculated angles. Each measuring point of the point cloud includes a standardized surface vector and/or includes a maximal surface vector having an angle with respect to the gravity vector above a limiting value being classified as a non-ground point.

Abstract: A method of monitoring an aircraft interior includes capturing an image using at least one camera mounted within the fuselage of the aircraft, the image being a captured image including individual image frames. The method further includes modifying the captured image and generating an optimized image using an image processing module, detecting an animate object within the optimized image and identifying features of the animate object using an object detection module, and displaying the optimized using a display module.

Abstract: Disclosed are an image semantic segmentation method, a logical integrated circuit, a system and an electronic device. The logical integrated circuit includes a convolution processing module and a deconvolution processing module. The convolution processing module performs convolution operation processing on an image to generate a piece of feature data of each and every feature image block of the image. The deconvolution processing module is configured to perform deconvolution operation processing on each piece of feature data to obtain an respective image block region; determine an approximation degree of the each piece of feature data and each and every preset semantic category of multiple preset semantic categories, and classify the each piece of feature data into a preset semantic category; and fill each image block region corresponding to the each piece of feature data with a filling color to achieve semantic segmentation of the image.

Abstract: The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.

Abstract: Aspects of the present disclosure involve a system and a method for performing operations comprising: identifying a plurality of features for an image received by a messaging application server; assigning a first of the plurality of features and a second of the plurality of features respectively to a first nearest visual codebook cluster and a second nearest visual codebook cluster; applying the first and second nearest visual codebook clusters to a visual search database to identify a plurality of candidate matching images; selecting a given matching image based on a geometric verification of the plurality of matching images and the received image; and accessing an augmented reality experience corresponding to the given matching image.

Abstract: The invention provides a method, apparatus, device and storage medium for predicting a protein binding site. The method comprises the steps of: receiving a protein sequence to be predicted, dividing the protein sequence by using a preset sliding window and sliding step to obtain a plurality of amino acid sub-sequences, building word vectors for the protein sequence according to the amino acid sub-sequences, extracting document features from word elements, building document feature vectors for the protein sequence according to the extracted document features, extracting protein chain biological features from the amino acid sub-sequences, building biological feature vectors for the protein sequence according to the extracted biological features, classifying the amino acid sub-sequences expressed with the document feature vectors and the biological feature vectors by using a preset amino acid residue classification model to obtain amino acid residue types for the protein sequence.

Abstract: An image obtaining method comprises: by a projecting device, separately projecting an image acquisition light and a reference light onto a target object, wherein the light intensity of the image acquisition light is higher than the light intensity of the reference light; by an image obtaining device, obtaining a first image and a second image, both the first image and the second image comprising the image of the target object, with the target object of the first image being illuminated by the image acquisition light, and the target object of the second image being illuminated by the reference light, wherein the first image has a first area including a part of the target object, and the second image has a second area including the part of the target object; and by a computing device, performing a difference evaluation procedure to obtain a required light intensity based on a required amount.

Abstract: A computer-implemented method for annotating images is disclosed. The computer-implemented method includes generating a saliency map corresponding to an input image, wherein the input image is an image that requires annotation, generating a behavior saliency map, wherein the behavior saliency map is a saliency map formed from an average of a plurality of objects contained within respective bounding boxes of a plurality of sample images, generating a historical saliency map, wherein the historical saliency map is a saliency map formed from an average of a plurality of tagged objects in the plurality of sample images, fusing the saliency map corresponding to the input image, the behavior saliency map, and the historical saliency map to form a fused saliency map, and generating, based on the fused saliency map, a bounding box around an object in the input image.

Abstract: A synchronization method of a first depth sensing apparatus includes: transmitting a first optical signal to measure a distance to an object; receiving the first optical signal reflected by the object; when recognition of the received first optical signal fails, stopping transmission of the first optical signal and generating first synchronization information for synchronization with at least one second depth sensing apparatus; receiving a third optical signal for synchronization with the first depth sensing apparatus, which is transmitted by the at least one second depth sensing apparatus, and decoding the received third optical signal to extract at least one piece of second synchronization information; determining a time point at which and a cycle in which to re-transmit the first optical signal, based on the first synchronization information and the at least one piece of second synchronization information; and re-transmitting the first optical signal at the determined time point and cycle.

Abstract: A system and method. The system may include a display, a lens having distortion, an image generator, and a processor. The lens may be configured to focus light received from an environment. The image generator may be configured to receive the light from the lens and output a stream of images as image data, wherein each of the stream of images is distorted. The processor may be configured to: receive the image data from the image generator; detect a point source object in the stream of images of the image data; enhance the point source object in the stream of images of the image data; undistort the stream of images of the image data having an enhanced point source object; and output a stream of undistorted images as undistorted image data to the display.

Abstract: An information processing device that is configured to: receive, from each of two or more vehicles, image information captured by an image capture device installed at a vehicle, and vehicle information including position information on the vehicle; in a case in which a dangerously-driven vehicle has been detected by vehicles, establish a priority level for image processing of image information captured by the vehicles that have detected the dangerously-driven vehicle, in accordance with a predetermined condition; and based on the image information, perform image processing to identify a characteristic of the dangerously-driven vehicle in accordance with the established priority level.

Abstract: Image acquisition and interpretation method used for articles of commerce, consisting of one or more gondolas or shelves (A) in which shelves (X) are installed, to which are added four repair marks (B, E) that, in combination with a mobile device with a photographic camera (D), will take and process images in an automated way thanks to areas designed for this purpose (C) where reference marks (F, G) are displayed, correcting the perspective deformation and cutting the image to the area delimited by the repair marks, to then send the processed images to a computer server that can index data such as quantity and details of articles, number of units of each, physical characteristics, prices, etc.

Abstract: The present invention relates to a method for size estimation by image recognition of a specific target using a given scale. First, a reference objected is recognized in an image and the corresponding scale is established. Then the specific target is searched and the size of the specific target is estimated according to the acquired scale.