Sparse Coding for Alpha Matting

Johnson, Jubin; Varnousfaderani, Ehsan Shahrian; Cholakkal, Hisham; Rajan, Dinesh

doi:10.1109/tip.2016.2555705

Cited by 48 publications

(32 citation statements)

References 35 publications

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“…Furthermore, in [53], besides employing an energy function to smooth out the matte like [27] [28], a graph-based optimization is used to obtain the final matte. Particularly, it combines the initial sparse coded α (Eq.…”

Section: Joint Methods Based On Matting Laplaicanmentioning

confidence: 99%

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Chroma keying based on stereo images

Chu¹,

Zhao²

2017

2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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This thesis proposes a novel chroma keying method based on stereo images, which can be applied to post-process the alpha matte generated by any existing matting approach.Given a pair of stereo images, a new matting Laplacian matrix is constructed based on the affinities between matching pixels and their neighbors from two frames. Based on the new matting Laplacian matrix, a new cost function is also formulated to estimate alpha values of the reference image through the propagation between stereo images.To avoid over-smoothing during propagation, a united window with an adaptive size is used in the Laplacian matrix to overcome the dilemma between the inadequate propagation caused by small size windows and the over-smoothness triggered by large size windows.Moreover, an optional pre-processing is presented. When it comes to the foreground object with strong reflection, based on the histogram statistics, the initial alpha values of the particular unknown pixels can be refined before the proposed post-processing method. In addition, a ground truth generation method is developed for chroma keying.Visual quality comparisons between the original mattes produced by existing approaches and the results post-processed by the proposed method show that our method can effectively enhance the quality of alpha mattes. Also, objective quality comparisons demonstrate that our proposed algorithm can significantly reduce the errors of alpha mattes.ii Acknowledgements I would like to thank my supervisor, Professor Jiying Zhao, for his guidance and providing me the chance to conduct the research on chroma keying. I greatly appreciate his patience and confidence in my research work.

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Section: Joint Methods Based On Matting Laplaicanmentioning

confidence: 99%

“…Recently, [53] introduces a novel color-sampling based approach that capitalizes on a sparse coding of pixel features. Here the feature vector is a 6D vector…”

Section: Sparse Coding Mattingmentioning

confidence: 99%

Chroma keying based on stereo images

Chu¹,

Zhao²

2017

2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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“…SVR [ZZX12] trains SVM models through the collected samples and solves the unknown pixels in SVM regression. Sparse‐Coded [JVCR16] treats samples as the dictionary in sparse codes and generates final α values through a Lasso problem. The sampling strategies of the two methods are implemented here as the original text which are slightly different from Section .…”

Section: Other Settling Manners and Complementsmentioning

confidence: 99%

“…SVR [ZZX12] trains SVM models through the collected samples and solves the unknown pixels in SVM regression. Sparse-Coded [JVCR16] Note that SVR also employs an iterative solution and all the solved {F, B, α} (including those of mixed pixels) are fed to the subsequent iterations so that errors are heavily accumulated compared to Bayesian and BP. Besides, both methods employ block solutions to accelerate.…”

Section: Solution Without Matting Equationmentioning

confidence: 99%

“…Consequently, sampling‐based matting is currently showing better prospects and receiving increasing research attentions. In contrast, affinity‐based matting mainly plays a pre‐processing role based on sampling‐based one to smooth the final alpha mattes [WC07b, RRRAS08, RRG08, RRKG10, GO10, HWS*13, HRR*11, CM13, ZZX12, KEE15, JVCR16, FLZ16].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey on Sampling‐Based Image Matting

Yao

Zhao

Liu

2017

Computer Graphics Forum

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Sampling‐based image matting is currently playing a significant role and showing great further development potentials in image matting. However, the consequent survey articles and detailed classifications are still rare in the field of corresponding research. Furthermore, besides sampling strategies, most of the sampling‐based matting algorithms apply additional operations which actually conceal their real sampling performances. To inspire further improvements and new work, this paper makes a comprehensive survey on sampling‐based matting in the following five aspects: (i) Only the sampling step is initially preserved in the matting process to generate the final alpha results and make comparisons. (ii) Four basic categories including eight detailed classes for sampling‐based matting are presented, which are combined to generate the common sampling‐based matting algorithms. (iii) Each category including two classes is analysed and experimented independently on their advantages and disadvantages. (iv) Additional operations, including sampling weight, settling manner, complement and pre‐ and post‐processing, are sequentially analysed and added into sampling. Besides, the result and effect of each operation are also presented. (v) A pure sampling comparison framework is strongly recommended in future work.

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Foreground–background decoupling matting

Zheng

Zeng

et al. 2022

Int J of Intelligent Sys

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Image matting aims to extract specific objects, deployed in many applications. Generally, the automatic matting methods need an extra before overcome the intricate details and the diverse appearances. Recently, the matting community has paid more attentions to the investigation of trimapfree matting direction to address the dependency of priors. Most trimap-free approaches divide the matting task into global segmentation and detail matting subtasks. Unfortunately, these methods suffer from stagewise modeling, uncorrectable errors, or subtasks bottleneck problems. To address these issues, we propose a new set of matting subtasks, including foreground segmentation, background segmentation, and disambiguation. And we present a novel Foreground-Background Decoupling Matting (FBDM) network motivated by the new subtasks. Specifically, we first design a nested attention mechanism to decouple the backbone features. Then, we utilize two independent progressive semantic decoders by the decoupling features to complete the foreground and background segmentation subtasks. Finally, we utilize multiple of the proposed frequency division local disambiguation modules to achieve the disambiguation subtask. Besides, we establish a challenging potted plant (PPT) benchmark which contains 100 potted plants images in the real world for the matting

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Sparse Coding for Alpha Matting

Cited by 48 publications

References 35 publications

Chroma keying based on stereo images

Chroma keying based on stereo images

A Comprehensive Survey on Sampling‐Based Image Matting

Foreground–background decoupling matting

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