Hierarchical Image Saliency Detection on Extended CSSD Shi, Jianping; Yan, Qiong; Xu, Li ...
IEEE transactions on pattern analysis and machine intelligence,
2016-April-1, 2016-Apr, 2016-4-1, 20160401, Volume:
38, Issue:
4
Journal Article
Peer reviewed
Complex structures commonly exist in natural images. When an image contains small-scale high-contrast patterns either in the background or foreground, saliency detection could be adversely affected, ...resulting erroneous and non-uniform saliency assignment. The issue forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. Different from varying patch sizes or downsizing images, we measure region-based scales. The final saliency values are inferred optimally combining all the saliency cues in different scales using hierarchical inference. Through our inference model, single-scale information is selected to obtain a saliency map. Our method improves detection quality on many images that cannot be handled well traditionally. We also construct an extended Complex Scene Saliency Dataset (ECSSD) to include complex but general natural images.
Consistent Depth Maps Recovery from a Video Sequence GUOFENG ZHANG; JIAYA JIA; WONG, Tien-Tsin ...
IEEE transactions on pattern analysis and machine intelligence,
06/2009, Volume:
31, Issue:
6
Journal Article
Peer reviewed
Open access
This paper presents a novel method for recovering consistent depth maps from a video sequence. We propose a bundle optimization framework to address the major difficulties in stereo reconstruction, ...such as dealing with image noise, occlusions, and outliers. Different from the typical multi-view stereo methods, our approach not only imposes the photo-consistency constraint, but also explicitly associates the geometric coherence with multiple frames in a statistical way. It thus can naturally maintain the temporal coherence of the recovered dense depth maps without over-smoothing. To make the inference tractable, we introduce an iterative optimization scheme by first initializing the disparity maps using a segmentation prior and then refining the disparities by means of bundle optimization. Instead of defining the visibility parameters, our method implicitly models the reconstruction noise as well as the probabilistic visibility. After bundle optimization, we introduce an efficient space-time fusion algorithm to further reduce the reconstruction noise. Our automatic depth recovery is evaluated using a variety of challenging video examples.
Motion Detail Preserving Optical Flow Estimation Li Xu; Jiaya Jia; Matsushita, Y.
IEEE transactions on pattern analysis and machine intelligence,
09/2012, Volume:
34, Issue:
9
Journal Article, Conference Proceeding
Peer reviewed
Open access
A common problem of optical flow estimation in the multiscale variational framework is that fine motion structures cannot always be correctly estimated, especially for regions with significant and ...abrupt displacement variation. A novel extended coarse-to-fine (EC2F) refinement framework is introduced in this paper to address this issue, which reduces the reliance of flow estimates on their initial values propagated from the coarse level and enables recovering many motion details in each scale. The contribution of this paper also includes adaptation of the objective function to handle outliers and development of a new optimization procedure. The effectiveness of our algorithm is demonstrated by Middlebury optical flow benchmarkmarking and by experiments on challenging examples that involve large-displacement motion.
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