Browsing by Author "Yu, Jingyi"
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Item Multiple-Layer Visibility Propagation-Based Synthetic Aperture Imaging through Occlusion(MDPI AG, 2015-08-04) Yang, Tao; Li, Jing; Yu, Jingyi; Zhang, Yanning; Ma, Wenguang; Tong, Xiaomin; Yu, Rui; Ran, Lingyan; Tao Yang, Jing Li, Jingyi Yu, Yanning Zhang, Wenguang Ma, Xiaomin Tong, Rui Yu and Lingyan Ran; Yu, Jingyi; Ma, WenguangHeavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem. Existing synthetic aperture imaging methods, however, emulate focusing at a specific depth layer, but are incapable of producing an all-in-focus see-through image. Alternative in-painting algorithms can generate visually-plausible results, but cannot guarantee the correctness of the results. In this paper, we present a novel depth-free all-in-focus SAI technique based on light field visibility analysis. Specifically, we partition the scene into multiple visibility layers to directly deal with layer-wise occlusion and apply an optimization framework to propagate the visibility information between multiple layers. On each layer, visibility and optimal focus depth estimation is formulated as a multiple-label energy minimization problem. The layer-wise energy integrates all of the visibility masks from its previous layers, multi-view intensity consistency and depth smoothness constraint together. We compare our method with state-of-the-art solutions, and extensive experimental results demonstrate the effectiveness and superiority of our approach.Item Random sampling and model competition for guaranteed multiple consensus sets estimation(Sage Publications Inc., 2017-01-02) Li, Jing; Yang, Tao; Yu, Jingyi; Jing Li, Tao Yang and Jingyi Yu; Yu, JingyiRobust extraction of consensus sets from noisy data is a fundamental problem in robot vision. Existing multimodel estimation algorithms have shown success on large consensus sets estimations. One remaining challenge is to extract small consensus sets in cluttered multimodel data set. In this article, we present an effective multimodel extraction method to solve this challenge. Our technique is based on smallest consensus set random sampling, which we prove can guarantee to extract all consensus sets larger than the smallest set from input data. We then develop an efficient model competition scheme that iteratively removes redundant and incorrect model samplings. Extensive experiments on both synthetic data and real data with high percentage of outliers and multimodel intersections demonstrate the superiority of our method.