Systems and methods generating a haptic output response are disclosed. Video content is displayed on a display. A location of a user touch on the display is detected while the video content is being displayed. A region of interest in the video content is determined based on the location of the user touch. And a haptic output response is generated to a user. A characteristic of the haptic output response is determined using one or more characteristics of the region of interest.

A technique for detecting a glitch in an image is provided. The technique includes providing an image to a plurality of individual classifiers to generate a plurality of individual classifier outputs and providing the plurality of individual classifier outputs to an ensemble classifier to generate a glitch classification.

A method for efficient image registration between two images in the presence of inlier-poor domains includes receiving a set of candidate correspondences between the two images. An approximate homography between the two images is generated based upon a first correspondence in the correspondences. The set of candidate correspondences is filtered to identify inlier correspondences based upon the approximate homography. A candidate homography is computed based upon the inlier correspondences. The candidate homography can be selected as a final homography between the two images based upon a support of the candidate homography against the set of candidate correspondences. An image registration is performed between the two images based upon the candidate homography being selected as the final homography.

A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

Applications under test (AUT) may be tested by automated testing systems utilizing machine vision to recognize visual elements presented by the AUT and apply inputs to graphical elements, just as a human would. By utilizing the smallest image patch available, processing demands of the testing system are minimized. However, the image patch used to identify a portion of an AUT must be identifiable to the automated system. By selecting image patches that comprise the smallest size, but can be identified in an AUT by an automated system using machine vision, even as the AUT display is resized, reproportioned, noisy, or otherwise altered from the testing platform that was utilized for training.

Saliency regions are identified in a global scene depicted by volumetric video. Saliency video streams that track the saliency regions are generated. Each saliency video stream tracks a respective saliency region. A saliency stream based representation of the volumetric video is generated to include the saliency video streams. The saliency stream based representation of the volumetric video is transmitted to a video streaming client.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

Innovative techniques to generate a haptic stream are proposed. The proposed techniques allow haptic stream to be captured and along with audio/video stream. In so doing, a full experience—audio, video, haptics experience—may be experienced during playback.

The present invention provides an automatic system for visual guidance and navigation using real-time visual anchor point detection, which includes an edge device, a cloud device, and a landmark database; the system of the present invention provides users with navigation directions via visual landmarks. A candidate visual landmark image is selected from the database; the system of the present invention can calculate the time of day, the current weather condition, the current season, etc. In addition, the system of the present invention can use the camera on the dashboard of the vehicle, the camera in the smartphone, or other cameras to collect real-time images; the system of the present invention can also provide feedback on the visibility or salience of landmarks to improve the visual landmark images obtained by subsequent users.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.