Microsoft Technology Licensing, LLC

Xiao-Ou Tang

Jian Qiao Sun

Kaiming He

Ying-Qing Xu

Fang Wen

Carsten Curt Eckard Rother

Rong Xiao

A computing device is described herein that is configured to select a pixel pair including a foreground pixel of an image and a background pixel of the image from a global set of pixels based at least on spatial distances from an unknown pixel and color distances from the unknown pixel. The computing device is further configured to determine an opacity measure for the unknown pixel based at least on the selected pixel pair.

Opacity measurement using a global pixel set

Embodiments that provide cartoon personalization are disclosed. In accordance with one embodiment, cartoon personalization includes selecting a face image having a pose orientation that substantially matches an original pose orientation of a character in a cartoon image. The method also includes replacing a face of the character in the cartoon image with the face image. The method further includes blending the face image with a remainder of the character in the cartoon image.

Cartoon personalization

Techniques and technologies for de-hazing hazy images are described. Some techniques provide for determining the effects of the haze and removing the same from an image to recover a de-hazed image. Thus, the de-hazed image does not contain the effects of the haze. Some disclosed technologies allow for similar results. This document also discloses systems and methods for de-hazing images. Some of the disclosed de-hazing systems include an image capture device for capturing the hazy image and a processor for removing the effects of the haze from the hazy image. These systems store the recovered, de-hazed images in a memory and/or display the de-hazed images on a display. Some of the disclosed methods include removing the effects of the haze from a hazy image and outputting the recovered, de-hazed image.

Single image haze removal using dark channel priors

An object detector that includes a number of weak classifiers can be trained using a subset (a 'working set') of training data instead of all of the training data. The working set can be updated so that, for example, it remains representative of the training data. A decision to update the working set may be made based on the false positive sample rate&#x2014;if that rate falls below a threshold value, an update of the working set can be triggered.

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Beijing, China

Xiao-Ou Tang