Microsoft Technology Licensing, LLC

Piotr Dollar

Charles Lawrence Zitnick, Iii

Wolf Kienzle

David Nister

Matthew Sammis Ashman

Ivan Stojiljkovic

Magdalena Vukosavljevic

Mladen Radojevic

Dennis I Park

Matthew S Ashman

A method of object detection includes receiving a first image taken from a first perspective by a first camera and receiving a second image taken from a second perspective, different from the first perspective, by a second camera. Each pixel in the first image is offset relative to a corresponding pixel in the second image by a predetermined offset distance resulting in offset first and second images. A particular pixel of the offset first image depicts a same object locus as a corresponding pixel in the offset second image only if the object locus is at an expected object-detection distance from the first and second cameras. The method includes recognizing that a target object is imaged by the particular pixel of the offset first image and the corresponding pixel of the offset second image.

Stereoscopic object detection leveraging expected object distance

Edges are detected in a digital image including a plurality of pixels. For each of the plurality of pixels, a plurality of different edge assessments are made for that pixel. Each different edge assessment considers that pixel in a different position of a different pixel patch. The different edge assessments for each pixel are aggregated.

Digital image edge detection

A method of object detection includes receiving a first image taken by a first stereo camera, receiving a second image taken by a second stereo camera, and offsetting the first image relative to the second image by an offset distance selected such that each corresponding pixel of offset first and second images depict a same object locus if the object locus is at an assumed distance from the first and second stereo cameras. The method further includes locating a target object in the offset first and second images.

Stereoscopic object detection leveraging assumed distance

The techniques and systems described herein are directed to isolating part-centric motion in a visual scene and stabilizing (e.g., removing) motion in the visual scene that is associated with camera-centric motion and/or object-centric motion. By removing the motion that is associated with the camera-centric motion and/or the object-centric motion, the techniques are able to focus motion feature extraction mechanisms (e.g., temporal differencing) on the isolated part-centric motion. The extracted motion features may then be used to recognize and/or detect the particular type of object and/or estimate a pose or position of a particular type of object.

Motion stabilization and detection of articulated objects

Technologies pertaining to object detection are described herein. A cascaded classifier executes over subwindows of an image in a plurality of stages. A crosstalk cascade is employed to reject subwindows as being candidates for including an object that is desirably detected, where the crosstalk cascade is a combination of multiple cascades.

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Piotr Dollar