Chi-Man Pun

University of Macau, Macao, Macau, Macao

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Publications (92)43.4 Total impact

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    ABSTRACT: Because of the excellent properties of unpredictability, ergodicity and sensitivity to their parameters and initial values, chaotic maps are widely used in security applications. In this paper, we introduce a new two-dimensional Sine Logistic modulation map (2D-SLMM) which is derived from the Logistic and Sine maps. Compared with existing chaotic maps, it has the wider chaotic range, better ergodicity, hyperchaotic property and relatively low implementation cost. To investigate its applications, we propose a chaotic magic transform (CMT) to efficiently change the image pixel positions. Combining 2D-SLMM with CMT, we further introduce a new image encryption algorithm. Simulation results and security analysis demonstrate that the proposed algorithm is able to protect images with low time complexity and a high security level as well as to resist various attacks.
    Information Sciences. 11/2014;
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    ABSTRACT: Chaotic maps are widely used in different applications. Motivated by the cascade structure in electronic circuits, this paper introduces a general chaotic framework called the cascade chaotic system (CCS). Using two 1-D chaotic maps as seed maps, CCS is able to generate a huge number of new chaotic maps. Examples and evaluations show the CCS's robustness. Compared with corresponding seed maps, newly generated chaotic maps are more unpredictable and have better chaotic performance, more parameters, and complex chaotic properties. To investigate applications of CCS, we introduce a pseudo-random number generator (PRNG) and a data encryption system using a chaotic map generated by CCS. Simulation and analysis demonstrate that the proposed PRNG has high quality of randomness and that the data encryption system is able to protect different types of data with a high-security level.
    IEEE transactions on cybernetics. 10/2014;
  • 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; · 1.02 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; · 0.74 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.
  • Ka-Cheng Choi, Chi-Man Pun
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    ABSTRACT: In this paper, a high capacity digital audio reversible watermarking algorithm based on generalized integer transform is proposed. The proposed algorithm is built on top of a recently proposed reversible image watermarking scheme. The main challenges to customize an image watermarking scheme to work for audio domain are the differences between the structures of image and audio, the target of the scheme based on is a two-dimensional 8-bit grayscale image and the target now is a one-dimensional 16-bit quantization audio waveform. The dimension issue can be solved by small modifications, to partition the audio waveform into two portions, so that the image watermarking scheme based on can be applied to the audio. For the proposed algorithm, satisfactory amount of data can be hidden into the audio and the stego audio is perceptible for large payload but not annoying for listening.
    2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM); 12/2013
  • Chi-Man Pun, Xiao-Chen Yuan
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    ABSTRACT: A robust feature points detector for invariant audio watermarking is proposed in this paper. The audio segments centering at the detected feature points are extracted for both watermark embedding and extraction. These feature points are invariant to various attacks and will not be changed much for maintaining high auditory quality. Besides, high robustness and inaudibility can be achieved by embedding the watermark into the approximation coefficients of Stationary Wavelet Transform (SWT) domain, which is shift invariant. The spread spectrum communication technique is adopted to embed the watermark. Experimental results show that the proposed Robust Audio Segments Extractor (RASE) and the watermarking scheme are not only robust against common audio signal processing, such as low-pass filtering, MP3 compression, echo addition, volume change, and normalization; and distortions introduced in Stir-mark benchmark for Audio; but also robust against synchronization geometric distortions simultaneously, such as resample time-scale modification (TSM) with scaling factors up to ±50%, pitch invariant TSM by ±50%, and tempo invariant pitch shifting by ±50%. In general, the proposed scheme can well resist various attacks by the joint RASE and SWT approach, which performs much better comparing with the existing state-of-the art methods.
    IEEE Transactions on Audio Speech and Language Processing 11/2013; 21(11):2412-2424. · 1.68 Impact Factor
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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.
  • Hong-Min Zhu, Chi-Man Pun
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    ABSTRACT: We propose a human action recognition solution from the human's skeletal information. The angular representation of the skeleton shows its invariance to the scale of the actor and the orientation to the camera, while it maintains the correlation among different body parts. A modified Dynamic Time Warping (DTW) as a template matching solution is applied to do the action classification task. We collect our data with XBOX Kinect platform, a well-known Chinese traditional shadow boxing named Taiji is recognized based on types of actions which achieves the accuracy of 80%.
    2013 IEEE International Conference on Information and Automation (ICIA); 08/2013
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    Cong Lin, Chi-Man Pun
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    ABSTRACT: In this paper, we proposed an object tracking method for video stream based on conventional particle filter. Feature vectors are extracted from coefficient matrices of Discrete Cosine Transform (DCT). The feature, as experiment showed, is very robust to occlusion and rotation and it is not sensitive to scale changes. The proposed method is efficient enough to be used in a real-time application. The experiment is carried out on some common used datasets in literature. The result is satisfied and showed the estimated trace follows the target object very closely.
    07/2013;
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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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