Mr. Taehyeon Kim Profile

Taehyeon Kim

at Yonsei Univ

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Proceedings Article | 13 March 2021 Paper

Single image shadow removal via detection-free spatially adaptive denormalization

Hyunjeong Ryu, Taehyeon Kim, Yoonsik Choe

Proceedings Volume 11766, 117661D (2021) https://doi.org/10.1117/12.2590749

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Removing shadows in a single image has been a challenging problem because shadows can appear in various forms due to complex physical situations, influenced by many factors such as light sources and the material’s transparency. In order to remove shadows precisely, most previous works utilized shadow mask information, which indicates the shadow region in a given image using binary representation. However, shadow mask utilization inevitably induces multiple problems, including shadow removal performance dependency and additional shadow detection process requirements. To solve these problems, the proposed algorithm is based on an image-to-image translation algorithm, which does not require additional shadow mask information. In this deep neural network , the convergence of fast learning is induced by utilizing various normalization layers. However, in a case that is very sensitive to various spatial features of an input image, such as shadow removal, the normalization process causes a problem of losing a large amount of information existing in the input image data. So, we utilize spatially adaptive denormalization(SPADE) to prevent loss of spatial features of input image data. Therefore, not only does it fundamentally solve the problem that various feature information constituting the input image is lost in the normalization process, but also enables precise shadow region removal by combining the feature map of multi-resolutions with the feature map of the decoder. In evaluation, the proposed algorithm shows that it exceeds the existing approach by about 20~30% in both PSNR and RMSE based on the ISTD large data set.

Proceedings Article | 1 June 2020 Paper

Background subtraction via exact solution of Bayesian L1-norm tensor decomposition

Taehyeon Kim, Yoonsik Choe

Proceedings Volume 11515, 115151G (2020) https://doi.org/10.1117/12.2566236

KEYWORDS: Video, Principal component analysis, Statistical analysis, Optimization (mathematics), Electronics engineering, Cameras, Video processing, Statistical methods, Data modeling, Bessel functions

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In background subtraction, principal component analysis (PCA) based algorithm has shown remarkable ability to decompose foreground and background in video acquired by static camera. The algorithm via closed form solution of L1-norm Tucker-2 decomposition is one of the real-time background subtraction algorithms. The closed form solution can be obtained from linear combination of video frame vectors and coefficient vector which composed of only +1 and -1. However, since the optimal coefficient vector is unknown, the method cannot help to be a complicated combinatorial optimization problem, when the number of input frame is large. In this paper, to solve this problem, Bayesian optimization (BayesOpt) which is a black-box derivative-free global optimization based background subtraction method is proposed. This method finds the optimal coefficient combination without considering the linear combination of all possible coefficient-combinations, using Bayesian statistical model and Expected Improvement (EI) acquisition function. Here the Bayesian statistical modeling is the method that measures the uncertainty of unsampled coefficient combination points and the EI function is a surrogate function which indicates the next sampling coefficient combination points. The experimental results confirm the efficiency of the proposed method.

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