Abstract: A vehicle having one or more cameras, configured to record one or more images of a person approaching the vehicle. The camera(s) can be configured to send biometric data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the person based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to determine whether to notify a driver of the vehicle of the risk score based on the risk score exceeding a risk threshold. The vehicle can also include a user interface, configured to output the risk score to notify the driver when the computing system determines the risk score exceeds the risk threshold.

Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system includes receiving, at a sparse depth network, one or more sparse representations of an environment. The method also includes generating a depth estimate of the environment depicted in an image captured by an image capturing sensor. The method further includes generating, via the sparse depth network, one or more sparse depth estimates based on receiving the one or more sparse representations. The method also includes fusing the depth estimate and the one or more sparse depth estimates to generate a dense depth estimate. The method further includes generating the dense LiDAR representation based on the dense depth estimate and controlling an action of the vehicle based on identifying a three-dimensional object in the dense LiDAR representation.

Abstract: A method is provided. The method includes receiving an input image, extracting at least one feature from the input image, determining at least one local tone curve for a portion of the input image based on the extracted at least one feature, the portion of the input image being less than an overall area of the input image, and generating a toned image based on the at least one local tone curve.

Abstract: A method for processing an image includes acquiring an input image, performing down-sampling and feature extraction on the input image by an encoder network to obtain multiple feature maps, and performing up-sampling and feature extraction on the multiple feature maps by a decoder network to obtain a target segmentation image. Processing levels between the encoder network and the decoder network for outputting feature maps with the same resolution are connected with each other. The encoder network and the decoder network each includes one or more dense calculation blocks, and at least one convolution module in any dense computation block includes at least one group of asymmetric convolution kernels.

Abstract: A visual search system facilitates retrieval of provenance information using a machine learning model to generate content fingerprints that are invariant to benign transformations while being sensitive to manipulations. The machine learning model is trained on a training image dataset that includes original images, benign transformed variants of the original images, and manipulated variants of the original images. A loss function is used to train the machine learning model to minimize distances in an embedding space between benign transformed variants and their corresponding original images and increase distances between the manipulated variants and their corresponding original images.

Abstract: Methods, apparatus, and system to acquire aircraft flight data through visual analysis of flight instruments with a data capture neural network, to compare aircraft flight data to a standard of a flight maneuver with a data analysis neural network with minimal or no human input, to output a visualization of aircraft flight data and or analysis of aircraft flight data, and to acquire aircraft flight data, programmatically analyze aircraft flight data, and provide aircraft training to a prospective aircraft pilot.

Abstract: Disclosed are a method and system for optical calibration of a 3D printer. The method includes: projecting, by an optical apparatus, a projection image to a projection platform, placing a calibration plate on the projection platform, and capturing the projection platform; identifying the coordinates of calibration points and the coordinates of actual projection points according to the captured image to obtain a matrix of calibration points and a matrix of actual projection points; rotating and translating the matrix of the calibration points and/or the matrix of the actual projection points, and calculating a distance value T0 between the calibration points and the actual projection points in an image coordinate system; converting the T0 in the image coordinate system into an offset C1 in a pixel coordinate system, and inversely distorting an initial ideal projection image according to the offset C1 to offset optical distortion.

Abstract: An image processing method includes acquiring an image obtained by imaging of an object, ambient light data during the imaging, and reflection characteristic data and transmission characteristic data which depend on a concentration of a material contained in the object, and separating a reflected light component and a transmitting light component in the image using the ambient light data, the reflection characteristic data, and the transmission characteristic data.

Abstract: A method of displaying stress distribution on a sample surface includes: step S4 of capturing images of the sample surface before loading, during the loading, and after unloading; step S5 of measuring a first strain amount for each pixel position based on correlation between the image before the loading and the image after the unloading; step S6 of measuring a second strain amount for each pixel position based on correlation between the image before the loading and the image during the loading; step S7 of calculating stress for each pixel position based on the difference between the first strain amount and the second strain amount; and step S8 of displaying the distribution of the calculated stress at each pixel position.

Abstract: A product positioning method includes collecting a product image of a product, dividing the product image into a plurality of rectangular regions, performing integral image calculation on each rectangular region to obtain a plurality of integral images, forming n integral image regions each having four said integral images with two adjacent sides of each of the four said integral images connecting with one side of other two images of the four said integral images, numbering the four said integral images clockwise or counterclockwise, performing differential calculation on each integral image region to obtain a differential value according to the four said integral images, obtaining coordinates of a vertex of the product according to the differential values, and performing position correction on the product image according to obtained coordinates of the vertex and coordinates of a target vertex.

Abstract: A method and a device associate an object detection in a first frame with an object detection in a second frame using a convolutional neural (CNN) network trained to determine feature vectors such that object detections relating to separate objects are arranged in separate clusters. The CNN determines a reference set of feature vectors associated with the object detection in the first frame, and candidate sets of feature vectors associated with a respective one of identified areas corresponding to object detections in the second frame. A set of closest feature vectors is determined, and then measure of closeness to the reference set of feature vectors is determined for each candidate. A respective weight is determined for each object detection in the second frame. The object detection in the first frame is associated with one of the object detections in the second frame based on the assigned weights.

Abstract: In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for automatically transforming a digital image into a simulated pathology image are provided. In some embodiments, the method comprises: receiving a content image from an endomicroscopy device; receiving, from a hidden layer of a convolutional neural network (CNN) trained to recognize a multitude of classes of common objects, features indicative of content of the content image; receiving, providing a style reference image to the CNN; receiving, from another hidden layer of the CNN, features indicative of a style of the style reference image; receiving, from the hidden layers of the CNN, features indicative of content and style of a target image; generating a loss value based on the features of the content image, the style reference image, and the target image; minimizing the loss value; and displaying the target image with the minimized loss.

Abstract: A vehicle position determination device mountable to a vehicle, the vehicle position determination device including an acquisition unit that acquires a surrounding image that is road information for identifying a position of the vehicle and is represented by a small displacement with respect to the vehicle, and a control unit that compares the road information with road characteristic information indicating an absolute position of a predetermined point and determines a vehicle position according to a result of the comparison; the road information includes at least one of road shape information indicating a shape of a road surface in a direction of travel of the vehicle and road pattern information indicating a pattern on a road surface.

Abstract: A method and system for performing a deep learning based product search obtain an input image including a target product to be searched; transform a model pose included in the input image; obtain a standard input image having the transformed pose of the model; obtain a main product image having an area including the target product by performing deep learning based on the standard input image; extract a feature vector from the main product image; perform a product search for a product similar to the target product based on the feature vector; and output a result of the product search.

Abstract: The present invention provides a processing apparatus (20) including a first generation unit (22) that generates, from a plurality of time-series images, three-dimensional feature information indicating a time change of a feature in each position in each of the plurality of images, a second generation unit (23) that generates person position information indicating a position in which a person is present in each of the plurality of images, and an estimation unit (24) that estimates person behavior indicated by the plurality of images, based on the time change of the feature indicated by the three-dimensional feature information in the position in which the person is present being indicated by the person position information.

Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

Abstract: A system for determining calibrated camera extrinsic parameters for an autonomous vehicle includes a camera mounted to the autonomous vehicle collecting image data including a plurality of image frames. The system also includes one or more automated driving controllers in electronic communication with the camera that executes instructions to determine a vehicle pose estimate based on position and movement of the autonomous vehicle by a localization algorithm. The one or more automated driving controllers determine the calibrated camera extrinsic parameters based on three dimensional coordinates for specific feature points of interests corresponding to two sequential image frames, the specific feature points of interests corresponding to the two sequential image frames, and the camera pose corresponding to the two sequential image frames.

Abstract: A method is provided. The method includes receiving an input image, extracting at least one feature from the input image, determining at least one local tone curve for a portion of the input image based on the extracted at least one feature, the portion of the input image being less than an overall area of the input image, and generating a toned image based on the at least one local tone curve.

Abstract: An image processing device for improving accuracy of discrimination of hidden portions of a subject captured in an image is disclosed. In this regard the image processing device receives image data of an image in which a person is captured. Based on the received image data, the image processing device estimates a hidden body part of the person that is not captured in the image due to obstruction by another body part of the person. Such estimation is performed by detecting first feature points included in a left limb of the person and second feature points included in a right limb of the person, and estimating at least one of the first feature points and the second feature points are to be included in a hidden body part that is obstructed by another body part of the person or a non-hidden body part.

Abstract: A method for operating a powder bed-based manufacturing device for additive manufacturing of a component from a powder material includes taking at least one optical recording of a working powder layer of the powder material on a construction location of the manufacturing device, locally evaluating the at least one optical recording, and examining the working powder layer for local coating errors using the evaluation of the at least one optical recording.