Chunhong Pan

Chinese Academy of Sciences, Peping, Beijing, China

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Publications (87)93.14 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: Face recognition has been popular in the pattern recognition field for decades, but it is still a difficult problem due to the various image distortions. Recently, sparse representation based classification (SRC) was proposed as a novel image classification approach, which is very effective with sufficient training samples for each class. However, the performance drops when the number of training samples is limited. In this paper, we show that effective local image features and appropriate nonlinear kernels are needed in deriving a better classification method based on sparse representation. Thus, we propose a novel kernel SRC framework and utilize effective local image features in this framework for robust face recognition. First, we present a kernel coordinate descent (KCD) algorithm for the LASSO problem in the kernel space, and we successfully integrate it in the SRC framework (called KCD-SRC) for face recognition. Second, we employ local image features and develop both pixel-level and region-level kernels for KCD-SRC based face recognition, making it discriminative and robust against illumination variations and occlusions. Extensive experiments are conducted on three public face databases (Extended YaleB, CMU-PIE and AR) under illumination variations, noise corruptions, continuous occlusions, and registration errors, demonstrating excellent performances of the KCD-SRC algorithm combining with the proposed kernels.
    Neurocomputing 01/2014; 133:141–152. · 1.63 Impact Factor
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    ABSTRACT: Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.
    ISPRS Journal of Photogrammetry and Remote Sensing 01/2014; 88:101–118. · 3.31 Impact Factor
  • Lingfeng Wang, Chunhong Pan
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    ABSTRACT: It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.
    Pattern Recognition. 01/2014; 47(5):1917–1925.
  • Lingfeng Wang, Chunhong Pan
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    ABSTRACT: Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches.
    Magnetic Resonance Imaging 11/2013; · 2.06 Impact Factor
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    ABSTRACT: Change detection of VHR (Very High Resolution) images is very difficult due to the impacts caused by the seasonal changes, the imaging condition, and so on. To address the above difficulty, a novel unsupervised change detection algorithm is proposed based on deep learning, where the complex correspondence between the images is established by Auto-encoder Model. By taking advantages of the powerful ability of deep learning in compensating the impacts implicitly, the multi-temporal images can be compared fairly. Experiments demonstrate the effectiveness of the proposed approach.
    Proc SPIE 10/2013;
  • Bin Fan, Chunlei Huo, Chunhong Pan, Qingqun Kong
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    ABSTRACT: Although feature-based methods have been successfully developed in the past decades for the registration of optical images, the registration of optical and synthetic aperture radar (SAR) images is still a challenging problem in remote sensing. In this letter, an improved version of the scale-invariant feature transform is first proposed to obtain initial matching features from optical and SAR images. Then, the initial matching features are refined by exploring their spatial relationship. The refined feature matches are finally used for estimating registration parameters. Experimental results have shown the effectiveness of the proposed method.
    IEEE Geoscience and Remote Sensing Letters 07/2013; 10(4):657-661. · 1.82 Impact Factor
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    ABSTRACT: Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. What is more, this task becomes more challenging in the case that the spectral bands are degraded with noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy based metric where the non-negative constraints on both endmembers and abundances are imposed to keep physical significance. In addition, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively re-weighted NMF with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and give more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.
    05/2013;
  • Lingfeng Wang, Huaiyu Wu, Chunhong Pan
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    ABSTRACT: Intensity inhomogeneity often causes considerable difficulties in image segmentation. To tackle this problem, we propose a new region-based level set method. The proposed method considers the local image information by describing it as a novel local signed difference (LSD) energy, which possesses both local separability and global consistency. The LSD energy term is integrated into an objective energy functional, which is minimized via a level set evolution process. Extensive experiments are performed to evaluate the proposed method, showing improvements in both accuracy and efficiency, as compared with the state-of-the-art approaches.
    Pattern Recognition Letters 04/2013; 34(6):637–645. · 1.27 Impact Factor
  • Jun Bai, Shiming Xiang, Chunhong Pan
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    ABSTRACT: The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method.
    IEEE Transactions on Geoscience and Remote Sensing 02/2013; 51(2):803-817. · 3.47 Impact Factor
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    ABSTRACT: Super-resolution from a single image plays an important role in many computer vision systems. However, it is still a challenging task, especially in preserving local edge structures. To construct high-resolution images while preserving the sharp edges, an effective edge-directed super-resolution method is presented in this paper. An adaptive self-interpolation algorithm is first proposed to estimate a sharp high-resolution gradient field directly from the input low-resolution image. The obtained high-resolution gradient is then regarded as a gradient constraint or an edge-preserving constraint to reconstruct the high-resolution image. Extensive results have shown both qualitatively and quantitatively that the proposed method can produce convincing super-resolution images containing complex and sharp features, as compared with the other state-of-the-art super-resolution algorithms.
    IEEE Transactions on Circuits and Systems for Video Technology 01/2013; 23(8):1289-1299. · 1.82 Impact Factor
  • Kun Ding, Chunlei Huo, Yuan Xu, Chunhong Pan
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    ABSTRACT: The difficulty of Very High Resolution (VHR) image change detection is mainly due to the low separability between the changed and unchanged class. The traditional approaches usually address the problem by solving the feature extraction and classification separately, which cannot ensure that the classification algorithm makes the best use of the features. Considering this, we propose a novel approach that combines the feature extraction and the classification task by utilizing the sparse representation algorithm with discriminative dictionary. Experiments on real data sets show that our method achieves effective results.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
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    ABSTRACT: Local image descriptors are one of the key components in many computer vision applications. Recently, binary descriptors have received increasing interest of the community for its efficiency and low memory cost. The similarity of binary descriptors is measured by Hamming distance which has equal emphasis on all elements of binary descriptors. This paper improves the performance of binary descriptors by learning a weighted Hamming distance for binary descriptors with larger weights assigned to more discriminative elements. What is more, the weighted Hamming distance can be computed as fast as the Hamming distance on the basis of a pre-computed look-up-table. Therefore, the proposed method improves the matching performance of binary descriptors without sacrificing matching speed. Experimental results on two popular binary descriptors (BRIEF [1] and FREAK [2]) validate the effectiveness of the proposed method.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • Lingfeng Wang, Huaiyu Wu, Chunhong Pan
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    ABSTRACT: It is still a very challenging task to recognize a face in a real world scenario, since the face may be corrupted by many unknown factors. Among them, illumination variation is an important one, which will be mainly discussed in this paper. First, the illumination variations caused by shadow or overexposure are regarded as a multiplicative scaling image over the original face image. The purpose of introducing scaling vector (or scaling image) is to enhance the pixels in shadow regions, while depress the pixels in overexposure regions. Then, based on the scaling vector, we propose a novel tone-aware sparse representation (TASR) model. Finally, a EM-like algorithm is proposed to solve the proposed TASR model. Extensive experiments on the benchmark face databases show that our method is more robust against illumination variations.
    Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on; 01/2013
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    ABSTRACT: Urban change detection of Very High Resolution (VHR) remote sensing images is challenging, due to the ill-posed nature of change detection problem, the inherent nature of VHR image, the complex morphology of urban scenes, etc. To address the above difficulties, a robust approach is proposed, which is based on discriminative local features, robust distance metric and novel multi-scale fusion strategy. By integrating these components synergistically, the proposed approach is superior to the traditional approaches in capturing semantic changes and removing the false changes. Comparative experiments demonstrate the effectiveness and advantages of the proposed approach.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
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    ABSTRACT: Object tracking plays an important role in many intelligent transportation systems. Unfortunately, it remains a challenging task due to factors such as occlusion and target-appearance variation. In this paper, we present a new tracking algorithm to tackle the difficulties caused by these two factors. First, considering the target-appearance variation, we introduce the local-background-weighted histogram (LBWH) to describe the target. In our LBWH, the local background is treated as the context of the target representation. Compared with traditional descriptors, the LBWH is more robust to the variability or the clutter of the potential background. Second, to deal with the occlusion case, a new forward-backward mean-shift (FBMS) algorithm is proposed by incorporating a forward-backward evaluation scheme, in which the tracking result is evaluated by the forward-backward error. Extensive experiments on various scenarios have demonstrated that our tracking algorithm outperforms the state-of-the-art approaches in tracking accuracy.
    IEEE Transactions on Intelligent Transportation Systems 01/2013; 14(3):1480-1489. · 3.06 Impact Factor
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    ABSTRACT: This paper presents a new deblurring method to remove the out-of-focus blur from similar image pairs. The method is motivated by an observation that a blurred structure appearing in one image can often have its corresponding clear one in the similar clear images. Our method first extracts the patch pairs from input images by SIFT matching. Then the constraints on the patch pairs are used to estimate the blur kernel via the RANSAC algorithm. Finally, the non-blind deconvolution is adopted to restore the blurred image. The main advantage is that we can improve the deblurring results with the help of additional similar clear images in many practical applications. Our method is validated on synthetic and real images by comparing with state-of-the-art methods.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
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    ABSTRACT: This paper presents a novel segmentation approach for extracting faces from videos. Under an active learning framework, the segmentation is conducted automatically without human interactions. A small portion of pixels are first labeled as face or non-face. Given these labeled samples, a semi-supervised spline regression model is then applied to obtain the face region. Based on the segmentation result, new pixels are selected and labeled. These two steps perform iterately until convergence. The main novelty is that color and depth data are combined to provide the labeling information. Our approach is validated via comparisons with state-of-the-art methods on real videos captured from the commodity Kinect camera.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
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    ABSTRACT: The Linear Discriminant Analysis (LDA) algorithm plays an important role in pattern recognition. A common practice is that LDA and many of its variants generally learn dense bases, which are not robust to local image distortions and partial occlusions. Recently, the LASSO penalty has been incorporated into LDA to learn sparse bases. However, since the learned sparse coefficients are globally distributed all over the basis image, the solution is still not robust to partial occlusions. In this paper, we propose a Local Sparse Discriminant Analysis (LoSDA) method, which aims at learning discriminant bases that consist of local object parts. In this way, it is more robust than dense or global basis based LDA algorithms for visual classification. The proposed model is formulated as a constrained least square regression problem with a group sparse regularization. Furthermore, we derive a weighted LoSDA (WLoSDA) approach to learn localized basis images, which also enables multi subspace learning and fusion. Finally, we develop an algorithm based on the Accelerated Proximal Gradient (APG) technique to solve the resulting weighted group sparse optimization problem. Experimental results on the FRGC v2.0 and the AR face databases show that the proposed LoSDA and WLoSDA algorithms both outperform the other state-of-the-art discriminant subspace learning algorithms under illumination variations and occlusions.
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on; 01/2013
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    ABSTRACT: Frame structure estimation from line segments is an important yet challenging problem in understanding indoor scenes. In practice, line segment extraction can be affected by occlusions, illumination variations, and weak object boundaries. To address this problem, an approach for frame structure recovery based on line segment refinement and voting is proposed. We refined line segments by the revising, connecting, and adding operations. We then propose an iterative voting mechanism for selecting refined line segments, where a cross ratio constraint is enforced to build crab-like models. Our algorithm outperforms state-of-the-art approaches, especially when considering complex indoor scenes.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
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    ABSTRACT: A scanned image of an opened book page often suffers from various scanning artifacts known as scanning shading and dark borders noises. These artifacts will degrade the qualities of the scanned images and cause many problems to the subsequent process of document image analysis. In this paper, we propose an effective method to rectify these scanning artifacts. Our method comes from two observations that the shading surface of most scanned book pages is quasi-concave and that the document contents are usually printed on a sheet of plain and bright paper. Based on these observations, a shading image can be accurately extracted via convex hulls based image reconstruction. The proposed method proves to be surprisingly effective for image shading correction and dark borders removal. It can restore a desired shading-free image and meanwhile yield an illumination surface of high quality. More importantly, the proposed method is non-parametric and thus does not involve any user interactions or parameters fine-tuning. This would make it much appealing to non-expert users in applications. Extensive experiments based on synthetic and real-scanned document images demonstrate the efficiency of the proposed method.
    IEEE Transactions on Software Engineering 11/2012; · 2.59 Impact Factor

Publication Stats

71 Citations
93.14 Total Impact Points

Institutions

  • 2003–2014
    • Chinese Academy of Sciences
      • Institute of Automation
      Peping, Beijing, China
  • 2004–2013
    • Northeast Institute of Geography and Agroecology
      • • Institute of Automation
      • • National Pattern Recognition Laboratory
      Beijing, Beijing Shi, China
  • 2011
    • Peking University
      Peping, Beijing, China