Microsoft Technology Licensing, LLC

Anne H Loomis

Romualdo T Impas

Jeff J Lin

Alan Paulin

Eyal Ofek

Jeffrey Darren Couckuyt

Matthew Cheung

Wayne Liu

Various embodiments present a combined visual entity that represents overlapping visual entities. The combined visual entity can include a primary visualization that represents one of the overlapping visual entities and annotations that represent others of the overlapping visual entities. For example, a map view can include multiple geographical entities that overlap. A primary visualization can be rendered that represents one of the multiple geographical entities. The primary visualization can be visually annotated (e.g., with symbols, letters, or other visual indicators) to indicate others of the multiple geographical entities. In some embodiments, a zoom operation can cause visual entities to be added and/or removed from the combined visual entity.

Representation of overlapping visual entities

A geographic navigation system for segmenting a received set of instructions (or directions) that guide a user over a geographic route from a starting location to an ending location, and then abbreviating a subset of the instructions related to the inexact or familiar regions into a more concise and relevant form, yet retain all the information for guiding the user on the trip. Route segmentation occurs before instruction abbreviation by segmenting the route into multiple logical components in which abbreviation can be applied. After the need for instruction abbreviation is recognized and the route is segmented, techniques and designs are employed to generate understandable strings that describe the abbreviated directions as well as controls that allow optimization of the user experience.

Abbreviated directions for route navigation

Techniques for enhancing the reliable and efficient decoding of images of computer-readable symbols, which represent information encoded using a one- or two-dimensional visual symbology, are discussed. In accordance with one technique, prior to attempting to decode a particular image, a likelihood of successfully decoding the visual symbology from the image is determined based on a success metric. The success metric is calculated based on the blurriness of the image, or the tilt of the camera that captured the image, or both. Levels of blurriness or tilt, and the likelihood of decoding success, are ascertained with respect to predetermined reference values associated with generally decodable images. In accordance with another technique, image-sharpening algorithms are applied at select locations within the image. Such locations generally correspond to centers of modules within the visual symbology of the computer-readable symbol, which are assigned values based on grayscale values of proximate pixels in the image.

Techniques for decoding images of barcodes

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Seattle, WA, United States of America

Anne H Loomis