Showing papers by "Jun-Yan Zhu published in 2013"

Motion-Aware Gradient Domain Video Composition

Abstract: For images, gradient domain composition methods like Poisson blending offer practical solutions for uncertain object boundaries and differences in illumination conditions. However, adapting Poisson image blending to video presents new challenges due to the added temporal dimension. In video, the human eye is sensitive to small changes in blending boundaries across frames and slight differences in motions of the source patch and target video. We present a novel video blending approach that tackles these problems by merging the gradient of source and target videos and optimizing a consistent blending boundary based on a user-provided blending trimap for the source video. Our approach extends mean-value coordinates interpolation to support hybrid blending with a dynamic boundary while maintaining interactive performance. We also provide a user interface and source object positioning method that can efficiently deal with complex video sequences beyond the capabilities of alpha blending.

Multiple cluster instance learning for image classification

Abstract: The techniques and systems described herein create and train a multiple clustered instance learning (MCIL) model based on image features and patterns extracted from training images. The techniques and systems separate each of the training images into a plurality of instances (or patches), and then learn multiple instance-level classifiers based on the extracted image features. The instance-level classifiers are then integrated into the MCIL model so that the MCIL model, when applied to unclassified images, can perform image-level classification, patch-level clustering, and pixel-level segmentation.