One or more computing devices, systems, and/or methods for selective content insertion into areas of media objects are provided. For example, a media object (e.g., an image or video), is selected for composition with content, such as where a message, interactive content, a hyperlink, or other types of content is overlaid or embedded into the media object to create a composite media object. The content is added into an area of the media object that is selectively identified to reduce occlusion and/or improve visual cohesiveness between the content and the media object (e.g., added to an area with a similar or complimentary color, having an adequate size with spare amounts of visual features such as a soccer player, a ball, or other entity, etc.). In this way, the content may be add into the area of the media object to create a composite media object to provide to users.

A division line recognition apparatus is provided with a feature detector detecting bright features showing an area which is brighter than a road surface from captured images, and a reflection determination unit which determines straight lines as road surface reflection areas, when the bright feature points are detected in a same position between frames of images captured at a preset period and form a straight line shorter than a preset threshold length, among the detected bright feature points. A white line recognition unit which recognizes a white line from the bright feature points having the bright feature points showing a short line determined as the road surface reflection, removed therefrom.

An image retrieval apparatus includes a processor, and the processor performs a process including: determining an image in which a first characteristic object is included in a subject to be a first image, and determining an image that is captured after the first image and in which a second characteristic object is included in the subject to be a second image, from among a series of captured images; specifying images as an image group, the images being captured during a period after the first image is captured before the second image is captured from among the series of captured images; and extracting a representative image from the image group.

Various methods, apparatuses/systems, and media for automatically extracting information from unstructured data are provided. A receiver receives digitized data of a document having unstructured data format. A processor applies machine learning models for sectioning the digitized data. An OCR device applies an OCR processing to the sectioned digitized data. The processor matches the sectioned digitized data to patterns and rules; applies classification models to the matched digitized data to identify entities and events from the sectioned digitized data; automatically link each entity with corresponding event in a hierarchical format to generate a document having structured data format; and output the document having the structured data with metadata having the linked entity with corresponding event in the hierarchical format to downstream applications.

This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.

Systems and methods for performing spatial line grouping on digital ink stokes. The system includes an electronic processor configured to access a set of hypothetical lines in an electronic document and determine a set of hypothetical line pairings. The electronic processor is also configured to determine, via a gradient boosting tree model, a merge confidence score for each hypothetical line pairing and compare a first merge confidence score with a merge threshold. The first merge confidence score is associated with a first hypothetical line and a first neighboring hypothetical line. The electronic processor is also configured to, in response to the first merge confidence score satisfying the merge threshold, merge the first hypothetical line and the first neighboring hypothetical line to form a first line grouping. The electronic processor is also configured to perform a digital ink stroke analysis on the electronic document based on the first line grouping.

Devices, systems, and methods for machine learning model generation. A method can include generating image chips of an image. The image chips can each provide a view of a different extent of an object in the image. Based on an object definition that indicates respective features of the object and a location of the respective features along a length of the object, it can be determined whether any of the image chips include any of the respective features. Each image chip of the image chips can be labeled to include an indication of any of the features included in the image chip resulting in labelled image chips. The method can include training an ensemble classifier based on the labelled image chips resulting in a trained ensemble classifier.

In implementations of systems for generating accessible color themes, a computing device implements an accessibility system to receive an input color palette including original colors defined in a color space. The accessibility system generates color vision deficiency simulations that correspond to pairs of the original colors and computes perceptual color differences between the color vision deficiency simulations. Candidate colors are determined for corresponding original colors based at least partially on the perceptual color differences and a conflicting perceptual color difference. The accessibility system outputs an output color palette including replacement colors defined in the color space that are generated at least partially based on distances between the candidate colors and the corresponding original colors computed in a CIELAB color space.

Disclosed is a method and apparatus for detecting cloud features. The method comprises: obtaining image data (e.g. using a camera), the image data defining a plurality of pixels and, for each pixel, a respective luminance value; defining one or more intervals for the luminance values of the pixels; partitioning the image data into one or more image segments (502-508), each respective image segment (502-508) containing pixels having a luminance value in a respective interval; and classifying, as a cloud feature, each image segment (502-508) containing pixels having luminance value greater than or equal to a threshold luminance value (L.sub.T).

Disclosed, among other things, is a computer device and computer-implemented method of classifying cells within an image of a tissue sample comprising providing the image of the tissue sample as input; computing nuclear feature metrics from features of nuclei within the image; computing contextual information metrics based on nuclei of interest with the image; classifying the cells within the image using a combination of the nuclear feature metrics and contextual information metrics.