Motion Detail Preserving Optical Flow Estimation

The Chinese University of Hong Kong, Hong Kong.
IEEE Transactions on Software Engineering (Impact Factor: 5.78). 12/2011; 34(9). DOI: 10.1109/TPAMI.2011.236
Source: PubMed


A common problem of optical flow estimation in the multi-scale 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 adaption of the objective function to handle outliers and development of a new optimization procedure. The effectiveness of our algorithm is borne out by the Middlebury optical flow benchmark and by experiments on challenging examples that involve large-displacement motion.

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    • "Typical methods include similarity, affine, or projective transformations for rigid scenes [20]. For non-rigid and dynamic scenes, optic flow can be used to correct misaligned camera geometries [24] [29]. Our approach extends SIFT flow [14] by considering multi-lighting constraints. "
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    ABSTRACT: Detecting fine-grained subtle changes among a scene is critically important in practice. Previous change detection methods, focusing on detecting large-scale significant changes, cannot do this well. This paper proposes a feasible end-to-end approach to this challenging problem. We start from active camera relocation that quickly relocates camera to nearly the same pose and position of the last time observation. To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions. Given two times observations, we formulate fine-grained change detection as a joint optimization problem of three related factors, i.e., normal-aware lighting difference, camera geometry correction flow, and real scene change mask. We solve the three factors in a coarse-to-fine manner and achieve reliable change decision by rank minimization. We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions. Extensive experiments show the superior performance of our approach over state-of-the-art change detection methods and its ability to distinguish real scene changes from false ones caused by lighting variations.
    ICCV; 12/2015
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    • "Spectral techniques involving the Radon transform have also been used to estimate multiple superimposed translations in [18] and local affine models in [19]. Finally, feature matching was proposed in [20] and used as an initialisation in [21] [22]. For a complete review of the state-of-the-art see [23] [24] [25] [26] [27], and, more recently, [6] [28]. "
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    ABSTRACT: The optical flow is a velocity field that describes the motion of pixels within a sequence (or set) of images. Its estimation plays an important role in areas such as motion compensation, object tracking and image registration. In this paper, we present a novel framework to estimate the optical flow using local all-pass filters. Instead of using the optical flow equation, the framework is based on relating one image to another, on a local level, using an all-pass filter and then extracting the optical flow from the filter. Using this framework, we present a fast novel algorithm for estimating a smoothly varying optical flow, which we term the Local All-Pass (LAP) algorithm. We demonstrate that this algorithm is consistent and accurate, and that it outperforms three state-of-the-art algorithms when estimating constant and smoothly varying flows. We also show initial competitive results for real images.
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), Brisbane, Australia; 04/2015
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    • "Simon and Seitz [15] matched the sparse features and then propagated these correspondences to the whole image. Xu et al. [39] expanded the sparse feature correspondences to the candidate motion field, and combined them with the classical optical flow. However, these feature based matching algorithms may be less effective in regions with weak texture [41]. "
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    ABSTRACT: This paper presents a new method to compute the dense correspondences between two images by using the energy optimization and the structured patches. In terms of the property of the sparse feature and the principle that nearest sub-scenes and neighbors are much more similar, we design a new energy optimization to guide the dense matching process and find the reliable correspondences. The sparse features are also employed to design a new structure to describe the patches. Both transformation and deformation with the structured patches are considered and incorporated into an energy optimization framework. Thus, our algorithm can match the objects robustly in complicated scenes. Finally, a local refinement technique is proposed to solve the perturbation of the matched patches. Experimental results demonstrate that our method outperforms the state-of-the-art matching algorithms.
    IEEE Transactions on Multimedia 02/2015; 17(3):295-306. DOI:10.1109/TMM.2015.2395078 · 2.30 Impact Factor
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