Abstract: A system and method for adaptive color assignments to image labels during annotation of datasets includes preparing a dataset for image labeling by an annotator by: leveraging a global color analyzer to perform a global color distribution of a plurality of images to identify one or more overall colors present in the plurality of images, and a local color analyzer to perform a local color distribution for each image to identify one or more colors present in an area of interest of the image, and selecting a plurality of candidate colors to be used as image labels by the annotator, based on an output of the global color analyzer and an output of the local color analyzer.

Abstract: A method of motor vehicle detection and identification through audio propagation includes generating source autometric signatures; broadcasting autometric signatures; detecting the autometric signatures; visualizing the autometric signatures; determining known autometric signatures; and evaluating and sending autometric signatures to the motor vehicle.

Abstract: A method for evaluating the effectiveness of a skin treatment on a skin feature includes correcting the color of a first image before the skin treatment, correcting the color of a second image after the skin treatment, determining the sizes of the skin feature in the first and second images, and comparing the corrected colors and sizes of the skin feature in the first and second images. In some embodiments, the first and second images include the skin feature and an indicator adjacent to the skin feature, where the indicator has a standard color and a known size.

Abstract: An image recognition approach employs both computer generated and manual image reviews to generate image tags characterizing an image. The computer generated and manual image reviews can be performed sequentially or in parallel. The generated image tags may be provided to a requester in real-time, be used to select an advertisement, and/or be used as the basis of an internet search. In some embodiments generated image tags are used as a basis for an upgraded image review. A confidence of a computer generated image review may be used to determine whether or not to perform a manual image review. Images and their associated image tags are optionally added to an image sequence.

Abstract: A computer vision processor of a camera extracts attributes of persons or vehicles from hyperzooms generated from image frames. The hyperzooms represent traffic patterns. The extracting is performed using a feature extractor of an on-camera convolutional neural network (CNN) including an inverted residual structure. The attributes include at least colors of clothing of the persons or colors of the vehicles. Mobile semantic segmentation models of the CNN are generated using the hyperzooms and the attributes. Attribute analytics are generated by executing the mobile semantic segmentation models while obviating network usage by the camera. The attribute analytics are stored in a key-value database located on a memory card of the camera. A query is received from the server instance specifying one or more of the attributes. The attribute analytics are filtered using the one or more of the attributes to obtain a portion of the traffic patterns.

Abstract: A device for creating a shape characteristic value estimation model for estimating a shape characteristic value includes: a post-processing-image acquisition part for acquiring a post-processing image of a surface of a substrate subjected to film processing; a pre-processing-image acquisition part for acquiring a pre-processing image of the surface not subjected to the film processing; a color-change estimation model creation part for creating a color-change estimation model for estimating first color-related information of the surface of the substrate included in the post-processing image from second color-related information of the surface of the substrate included in the pre-processing image; and a correlation estimation model creation part for creating a correlation estimation model by obtaining a difference between the first information and a result estimated by the color-change estimation model, the correlation estimation model being used for estimating correlation between the difference and the shape c

Abstract: A coating analyzer is configured to receive electronic image data of a physical coating and to generate information regarding the pigments of the physical coating. The coating analyzer applies a computer vision model trained on baseline image data to the electronic image data. The coating analyzer assigns color values to the pigments forming the electronic image data and generates pigment groups based on the assigned color values. The pigment groups provide color palette data regarding the pigments forming the coating.

Abstract: A method of estimating the movement between two images (I1, I2) comprising the steps of determining an initialization displacement vector between the two images by projecting, in the image plane of the image capture device, the displacement between the two images calculated depending on collected information defining the movement of the image capture device between the two images and depending on the value of image capture parameter(s) used, chosen from an angular opening, a zoom factor and the resolution of the images implementing, based on the initialization displacement vector, an iterative algorithm delivering a displacement vector depending on an error function to be minimized between one of the two images and the transform of the other image by the displacement vector.

Abstract: The technology relates to determining whether a camera is occluded. For instance, an image may be captured using a camera having red, green, and blue pixels each including a photosensor. Output values for the photosensors of each of the red pixels, green pixels, and blue pixels may be determined for the images. The output values of the green pixels may be compared to one or more of the output values of the red pixels or the output values of the blue pixels. That the camera is occluded is determined based on the comparison.

Abstract: A method for enabling a user to paint an image uploaded to a device is provided. The method includes uploading an image to the device, performing a depth estimation on the image to generate a depth map, and performing an edge detection on the image to generate a first edge map. The method also includes performing the edge detection on the depth map to generate a second edge map, performing a skeletonization function on the first edge map to generate a third edge map, and performing the skeletonization function on the depth map to generate a fourth edge map. The method also includes generating a final edge map using the third edge map and the fourth edge map, generating a colorized image by applying color to the final edge map to paint at least one wall of a scene, and displaying the colorized image on a display of the device.

Abstract: Images may be altered based on ON-OFF visual pathway information processing. A process for generating altered images may include separating an original image into distinct images based on luminance values for each pixel of the pixels that form the original image. The distinct images may include a first and a second separated image, where each separated image includes distinct pixels. Additionally, the process may include determining a first luminance range for the first separated image based on the luminance value for each pixel forming the first separated image, and determining a second luminance range for the second separated image based on the luminance value for each pixel forming the second separated image. The process may also include adjusting visual parameters of the first and second separated images, and combining the adjusted, first and second separated images to generate the altered image.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. The machine-learned network of the regularizer is unrolled or made iterative. For each iteration, nested U-blocks form a hierarchy so that some of the down-sampling and up-sampling of some U-blocks begin and end with lower resolution data or features, reducing computational complexity.

Abstract: Techniques for machine learning based image attribute determination are disclosed. In some embodiments, one or more unknown attributes of a received image comprising a prescribed environment are determined using a machine learning based framework. The machine learning based framework is at least in part trained on image data sets comprising a model environment that substantially simulates the prescribed environment.

Abstract: The method for item identification preferably includes determining visual information for an item; calculating a first encoding using the visual information; calculating a second encoding using the first encoding; determining an item identifier for the item using the second encoding; optionally presenting information associated with the item to a user; and optionally registering a new item.

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Abstract: Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Abstract: A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Abstract: Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.

Abstract: Techniques for generating an arbitrary view or perspective of an ensemble scene are disclosed. In some embodiments, in response to a received request for a prescribed perspective of an ensemble scene comprising a plurality of assets, an output image of the ensemble scene for the requested prescribed perspective is generated based at least in part on combining at least a portion of an existing image of each of at least a subset of the plurality of assets.

Abstract: A semiconductor image classifier. Convolution functions are applied to modify the wafer images in order to extract key information about the image. The modified images are condensed then processed through a series of pairwise classifiers, each classifier configured to determine that the image is more like one of the pair than the other. Probabilities from each classifier are collected to form a prediction for each image.