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An Iterative Co-Saliency Framework for RGBD Images
Runmin Cong, Jianjun Lei, Huazhu Fu +4
As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existin…
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
Zhiyong Yang, Qianqian Xu, Xiaochun Cao +1
Visual attributes, which refer to human-labeled semantic annotations, have gained increasing popularity in a wide range of real world applications. Generally, the existing attribut…
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Ke Ma, Jinshan Zeng, Jiechao Xiong +4
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods…
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Runmin Cong, Jianjun Lei, Changqing Zhang +3
Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing salie…
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
Runmin Cong, Jianjun Lei, Huazhu Fu +3
Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision comm…
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
Wenqi Ren, Jinshan Pan, Xiaochun Cao +1
Video deblurring is a challenging problem as the blur is complex and usually caused by the combination of camera shakes, object motions, and depth variations. Optical flow can be u…
Halftone Image Watermarking by Content Aware Double-sided Embedding Error Diffusion
Yuanfang Guo, Oscar C. Au, Rui Wang +2
In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the EDHVW methods' expected performances and limitations. Then, we propose a new ge…