Microsoft Patents Augmenting Images For Panoramic Display And Some Kinect Related Stuff

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Augmenting images for panoramic display

Methods and systems are provided methods and systems for augmenting image data (e.g., still image data or video image data) utilizing image context data to generate panoramic images. In accordance with embodiments hereof, a position and orientation of received image data is utilized to identify image context data (e.g., three-dimensional model data, two-dimensional image data, and/or 360.degree. image data from another source) rendered based upon the same position or a nearby position relative to the image data and the image data is augmented utilizing the identified context data to create a panoramic image. The panoramic image may then be displayed (e.g., shown on a LCD/CRT screen or projected) to create a user experience that is more immersive than the original image data could create.

Method and system to segment depth images and to detect shapes in three-dimensionally acquired data

A method and system analyzes data acquired by image systems to more rapidly identify objects of interest in the data. In one embodiment, z-depth data are segmented such that neighboring image pixels having similar z-depths are given a common label. Blobs, or groups of pixels with a same label, may be defined to correspond to different objects. Blobs preferably are modeled as primitives to more rapidly identify objects in the acquired image. In some embodiments, a modified connected component analysis is carried out where image pixels are pre-grouped into regions of different depth values preferably using a depth value histogram. The histogram is divided into regions and image cluster centers are determined. A depth group value image containing blobs is obtained, with each pixel being assigned to one of the depth groups.

Body scan

A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

About Author

Pradeep, a Computer Science & Engineering graduate.