Chi-Man Pun

University of Macau, Macao, Macau, Macao

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Publications (84)35.6 Total impact

  • Xiao-Chen Yuan, Chi-Man Pun
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    ABSTRACT: A robust and geometric invariant digital image watermarking scheme based on robust feature detector and local Zernike transform is proposed in this paper. The robust feature extraction method is proposed based on the Scale Invariant Feature Transform (SIFT) algorithm, to extract circular regions/patches for watermarking use. Then a local Zernike moments-based watermarking scheme is raised, where the watermarked regions/patches can be obtained directly by inverse Zernike Transform. Each extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the appointed binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.
    Multimedia Tools and Applications 09/2014; · 1.01 Impact Factor
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    ABSTRACT: In this paper, we focus on co-intrinsic decomposition, a new problem that performs intrinsic decomposition on a pair of images simultaneously, which share the same foreground with arbitrarily different illuminations and backgrounds. We specifically demand the common foreground across different images to share same reflectance values. For the purpose of efficiency and feasibility, we perform the co-intrinsic decomposition at superpixel-level and propose a uniform approach to automatically derive non-local reflectance relationships vi- a unsupervised L0 sparsity between superpixels from intra- and inter-images. We present a unicolor-light-based intrinsic model, from which we construct a non-local L0 sparse co-Retinex model that imposes feasible constraints on shad- ing, reflectance and environment light, respectively. The co- intrinsic decomposition is finally modeled as a quadratic minimization problem that leads to a fast closed form solution. Extensive experiments show plausible results of our approach in extracting common reflectance components from multiple images. We also validate the benefits of our results in boost- ing the accuracy of image co-saliency detection.
    IEEE International Conference on Multimedia and Expo; 07/2014
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    ABSTRACT: In this paper, we propose a hierarchical approach based on non-local $l_0$ sparsity for intrinsic image decomposition. Different from the previous studies which use heuristic ways to make the ill-posed problem well-defined, our approach can effectively construct sparse and non-local pairwise correlations in an unsupervised manner. Moreover, our approach is less dependent on chromaticity feature. Homogenous pairwise smoothness prior and scalability prior is added in our model to improve the decomposition accuracy. We formulate the decomposition problem as the minimization of a quadratic function, which can be solved in closed form with the standard conjugate gradient algorithm. Experimental results show that our approach can successfully extract the shading component and reflectance component from a single image, and outperform state-of-the-art techniques on the benchmark MIT dataset both in accuracy and perception. Besides, our approach can achieve comparable results with user-assisted approach on natural scenes.
    IEEE International Conference on Multimedia & Expo; 07/2014
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    ABSTRACT: As the increasing popularity of superpixel-based applications, measuring superpixel-level similarity becomes an important and commonly required problem. In this paper, we propose a general bag of squares (BoS) model for such particular purpose. Compared to existing methods, our approach provides a full scheme to both invariantly represent superpixels and accurately measure their pairwise similarities. In order to handle the split-and-merge variety of superpixels of same objects in different scenes, our model is based on superpixel pyramid. As a result, the BoS model of a superpixel is built upon a group of subregions consisting of the superpixel itself and its children subregions in the pyramid. For each subregion, we extract a proper number of maximum squares via distance transform, and then use a fast self-validated approach to clustering them into a small number of dominant squares, which together with a rotation and scale invariant square descriptor, jointly compose the BoS model for the particular superpixel. Finally, we measure the similarity between a pair of superpixels by the closeness of their BoS models. Experiments on interactive object segmentation and co-saliency detection show that the proposed BoS model can reliably capture the delicate differences among superpixels, thus always producing better segmentation results, especially for segmenting highly variant objects in clutter scenes.
    IEEE International Conference on Multimedia and Expo; 07/2014
  • Ning-Yu An, Chi-Man Pun
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    ABSTRACT: This paper presents a new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction. The 2-D discrete wavelet transform that can express the variance in frequency and direction of textures, and the contourlet transform that represents boundaries even more accurately are applied in our algorithm. The whole segmentation algorithm contains three stages. First, an adaptive color quantization scheme is utilized to obtain a coarse image representation. Then, the tiny regions are combined based on color information. Third, the proposed energy transform function is used as a criterion for image segmentation. The motivation of the proposed method is to obtain the complete and significant objects in the image. Ultimately, according to our experiments on the Berkeley segmentation database, our techniques have more reasonable and robust results than other two widely adopted image segmentation algorithms, and our method with contourlet transform has better performance than wavelet transform.
    Signal Image and Video Processing 07/2014; · 0.41 Impact Factor
  • Miao Cheng, Chi-Man Pun, Yuan Yan Tang
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    ABSTRACT: Nonnegative learning aims to learn the part-based representation of nonnegative data and receives much attention in recent years. Nonnegative matrix factorization has been popular to make nonnegative learning applicable, which can also be explained as an optimization problem with bound constraints. In order to exploit the informative components hidden in nonnegative patterns, a novel nonnegative learning method, termed nonnegative class-specific entropy component analysis, is developed in this work. Distinguish from the existing methods, the proposed method aims to conduct the general objective functions, and the conjugate gradient technique is applied to enhance the iterative optimization. In view of the development, a general nonnegative learning framework is presented to deal with the nonnegative optimization problem with general objective costs. Owing to the general objective costs and the nonnegative bound constraints, the diseased nonnegative learning problem usually occurs. To address this limitation, a modified line search criterion is proposed, which prevents the null trap with insured conditions while keeping the feasible step descendent. In addition, the numerical stopping rule is employed to achieve optimized efficiency, instead of the popular gradient-based one. Experiments on face recognition with varieties of conditions reveal that the proposed method possesses better performance over other methods.
    Formal Pattern Analysis & Applications 02/2014; · 0.81 Impact Factor
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    ABSTRACT: This paper introduces a novel parameter-control framework to produce many new one-dimensional (1D) chaotic maps. It has a simple structure and consists of two 1D chaotic maps, in which one is used as a seed map while the other acts as a control map that controls the parameter of the seed map. Examples and analysis results show that these newly generated chaotic maps have more complex structures and better chaos performance than their corresponding seed and control maps.
    01/2014;
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    ABSTRACT: Existing collage steganographic methods suffer from low payload of embedding messages. To improve the payload while providing a high level of security protection to messages, this paper introduces a new collage steganographic algorithm using cartoon design. It embeds messages into the least significant bits (LSBs) of color cartoon objects, applies different permutations to each object, and adds objects to a cartoon cover image to obtain the stego image. Computer simulations and comparisons demonstrate that the proposed algorithm shows significantly higher capacity of embedding messages compared with existing collage steganographic methods.
    01/2014;
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    Bo Liu, Chi-Man Pun, Xiao-Chen Yuan
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    ABSTRACT: Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.
    TheScientificWorldJournal. 01/2014; 2014:230425.
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    ABSTRACT: The intrinsic information of a graph can be fully encoded into its spectrum and corresponding eigenvectors of its adjacency matrix, which provides a solid foundation for the success of spectral graph matching methods. The spectral multiplicity, however, may significantly affect the matching accuracy. In this paper, we propose a spectral-multiplicity-tolerant graph matching approach. We start from modeling the spectral multiplicity in the matching error measurement. Next, we address the equal-size graph matching problem, and show how to establish the vertex-to-vertex correspondence by alternatively optimizing the multiplicity matrix C and the permutation matrix P. We also propose a reliable initialization method to make the iterative optimization process converge rapidly. Then, we extend the algorithm to unequal-size graph matching by optimally warping two graphs into the same size. A comprehensive performance evaluation has been conducted on a large synthetic dataset. We also demonstrate the effectiveness of our approach on shape retrieval. The experimental results show that compared with existing methods, the proposed approach is more robust to noise and structural corruption and has a comparable complexity.
    Pattern Recognition. 10/2013; 46(10):2819–2829.
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    ABSTRACT: A novel digital image watermarking scheme based on feature extraction and local Zernike transform is proposed in this paper. We proposed a local Zernike moments based watermarking scheme where the watermarked image/region can be obtained directly by inverse Zernike Transform. An edge-based feature detector is proposed for local region extraction, with which, the distinct circular patch of given size can be extracted for watermark embedding and extraction. The extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the selected binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.
    Signal Processing. 07/2013; 93(7):2087–2095.
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    ABSTRACT: One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually as-sume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing as-sumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors.
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    ABSTRACT: One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually as-sume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing as-sumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors. Index Terms—Convex hull, nonline-of-sight (NLOS), wireless localization.
  • [Show abstract] [Hide abstract]
    ABSTRACT: One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually as-sume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing as-sumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors. Index Terms—Convex hull, nonline-of-sight (NLOS), wireless localization.
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    [Show abstract] [Hide abstract]
    ABSTRACT: One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually as-sume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing as-sumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors. Index Terms—Convex hull, nonline-of-sight (NLOS), wireless localization.
    IEEE Transactions on Vehicular Technology 05/2013; 62(4):1484-1492. · 2.06 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually as-sume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing as-sumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors. Index Terms—Convex hull, nonline-of-sight (NLOS), wireless localization.