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    [Welcome two]


    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116735
    Publication Year: 2011 , Page(s): iv - v

    IEEE Conference Publications

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    Frame buffer compression for low-power video coding

    Zhan Ma ; Segall, A.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116665
    Publication Year: 2011 , Page(s): 757 - 760
    Cited by:  Papers (2)

    IEEE Conference Publications

    In this paper, we propose a novel hybrid frame buffer compression algorithm to reduce the memory bandwidth for lowpower video coding. In our work, we first decompose the full-resolution image into low resolution (LR) and high resolution (HR) components. We then calculate the HR residual by taking the difference between original HR pixel and an estimate derived from surrounding LR pixels. Finally, we use absolute moment block truncation coding to quantize and compress the LR pixel and HR residual data so as to reduce the memory bandwidth. We integrate our approach into the JCT-VC reference software for High Efficiency Video Coding (HEVC). Results show negligible impact on coding efficiency with significant memory bandwidth reduction. Specifically, we observe a bit rate increase of 0.38% and 1% with 21% and 31% memory bandwidth reduction, respectively. View full abstract»

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    A scale-space based hierarchical representation of discrete data

    Hidane, M. ; Lezoray, O. ; Elmoataz, A.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116144
    Publication Year: 2011 , Page(s): 285 - 288

    IEEE Conference Publications

    A new hierarchical representation of general discrete data sets living on graphs is proposed. The approach takes advantage of recent works on graph regularization. The different levels of the hierarchy are discovered as the regularization process is performed. The role of the merging criterion that is common to hierarchical representations is greatly reduced due to the regularization step. This yields a robust representation of data sets. Moreover, the approach is particularly well adapted to the processing of digital images, where nonlocal processing allows to better handle repetitive patterns usually present in natural images. View full abstract»

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    Efficient joint poisson-gauss restoration using multi-frame L2-relaxed-L0 analysis-based sparsity

    Gil-Rodrigo, E. ; Portilla, J. ; Miraut, D. ; Suarez-Mesa, R.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115697
    Publication Year: 2011 , Page(s): 1385 - 1388

    IEEE Conference Publications

    Recently we proposed an efficient technique based on analysis-based sparsity in tight frames to restore images affected by shift-invariant blur and additive white Gaussian noise. Here we apply the same alternate marginal optimization idea used in that work, but dealing with combined Poissonian-Gaussian noise, which we approximate as Gaussian, additive and signal-dependent. We operate (1) by adding to the original image and its associated sparse vector another auxiliary variable to be optimized in the loop: the blurred, but not yet noisy, image; and (2) by using a quadratic soft constraint. We also re-formulate the previous prior modelling in order to perform a standard maximum a posteriori estimation, and generalize the approach to allow for a multi-frame prior. The proposed technique is computationally efficient and yields state-of-the-art performance. View full abstract»

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    Robust segmentation of relevant regions in low depth of field images

    Graf, F. ; Kriegel, H.-P. ; Weiler, M.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116145
    Publication Year: 2011 , Page(s): 2861 - 2864
    Cited by:  Papers (4)

    IEEE Conference Publications

    Low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. When viewing a low depth of field image, the viewer implicitly segments the image into region of interest and non regions of interest which has major impact on the perception of the image. Thus, robust algorithms for the detection of the OOI in low DOF images provide valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low depth of field images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low depth of field images. View full abstract»

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    Image-based object detection under varying illumination in environments with specular surfaces

    Maier, W. ; Eschey, M. ; Steinbach, E.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115698
    Publication Year: 2011 , Page(s): 1389 - 1392

    IEEE Conference Publications

    Image-based environment representations capture the appearance of the surroundings of a mobile robot and are useful for the detection of novelty. However, image-based novelty detection can be impaired by illumination effects. In this paper we present an approach for the image-based detection of novel objects in a scene under varying lighting conditions and in the presence of objects with specular surfaces. The computation of an illumination-invariant image-based environment representation allows for the extraction of the shading of the environment from camera images. Using statistical models infered from the luminance and the saturation component of the shading images, specularities and shadows are detected and suppressed in the process of novelty detection. Experimental results show that the proposed method outperforms two recently presented reference approaches for illumination-invariant change detection in images. View full abstract»

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    Multicolor image segmentation using Ambrosio-Tortorelli approximation

    Asahi, T. ; Ortega, J.H. ; Lecaros, R.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116146
    Publication Year: 2011 , Page(s): 2865 - 2868

    IEEE Conference Publications

    Our research aims at image segmentation using the variational framework of Mumford and Shah, following an approximation proposed by Ambrosio and Tortorelli. This technique circumvents the use of parametric contours and implicit level-set techniques, where its solution may be regarded as a soft segmentation, with a number the levels or colors being 2N. On the other hand, the implementation was based on an finite difference discretization, where two - and four - color cases are described with their corresponding numerical results. View full abstract»

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    Batch-incremental principal component analysis with exact mean update

    Guifang Duan ; Yen-wei Chen
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115700
    Publication Year: 2011 , Page(s): 1397 - 1400

    IEEE Conference Publications

    Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting function is proposed for contribution balance of the current data and the new observed data to the new subspace. The performance of our method is illustrated in the experiments on face modeling and face recognition. View full abstract»

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    Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images

    Nascimento, J.C. ; Carneiro, G.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115875
    Publication Year: 2011 , Page(s): 2021 - 2024

    IEEE Conference Publications

    Statistical pattern recognition models are one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. The underlying statistical model usually relies on a complex model for the shape and appearance of the left ventricle whose parameters can be learned using a manually segmented data set. Unfortunately, this complex requires a large number of parameters that can be robustly learned only if the training set is sufficiently large. The difficulty in obtaining large training sets is currently a major roadblock for the further exploration of statistical models in medical image analysis. In this paper, we present a novel semi-supervised self-training model that reduces the need of large training sets for estimating the parameters of statistical models. This model is initially trained with a small set of manually segmented images, and for each new test sequence, the system re-estimates the model parameters incrementally without any further manual intervention. We show that state-of-the-art segmentation results can be achieved with training sets containing 50 annotated examples. View full abstract»

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    Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

    Melendez, J. ; Girones, X. ; Puig, D.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116147
    Publication Year: 2011 , Page(s): 2869 - 2872
    Cited by:  Papers (1)

    IEEE Conference Publications

    This paper presents a new, efficient technique for supervised texture segmentation based on a set of specifically designed filters and a multi-level pixel-based classifier. Filter design is carried out by means of a neural network, which is trained to maximize the filters' discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed by a classification scheme that utilizes multiple evaluation window sizes following a top-down approach, which iteratively refines the resulting segmentation. The proposed technique is compared to previous supervised texture segmenters by using both synthetic compositions and real outdoor textured images. View full abstract»

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    Hazy image modeling using color ellipsoids

    Gibson, K.B. ; Nguyen, T.Q.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115830
    Publication Year: 2011 , Page(s): 1861 - 1864

    IEEE Conference Publications

    Several methods exist that try to remove haze from a single image but they lack in the support for why a particular method was chosen. The idea of using color ellipsoids is presented in this paper for the purpose of developing a new framework for analyzing single image dehazing methods. Synthetic and real world images are used to support important properties of the color ellipsoids that give a queue to the amount of haze in an image. As an example of its usefulness, a single dehazing method is analyzed within the color ellipsoid framework. View full abstract»

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    Enhanced classification of focal hepatic lesions in ultrasound images using novel texture features

    Sihyoung Lee ; In A Jo ; Kyung Won Kim ; Jae Young Lee ; Yong Man Ro
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115876
    Publication Year: 2011 , Page(s): 2025 - 2028

    IEEE Conference Publications

    This paper discusses novel texture features that allow providing enhanced classification accuracy for focal hepatic lesions. The proposed texture features takes advantage of the rotation and scale invariant nature of Gabor wavelets, as well as the gray-level co-occurrence matrix (GLCM) for analyzing the spatial distribution of the pixel intensity in the lesion. To verify the effectiveness of the proposed texture features, experiments were performed with 150 ultrasound images containing 150 focal hepatic lesions, consisting of 50 cysts, 50 hemangiomas, and 50 malignancies. Experimental results show that the proposed texture features allow for an improved classification performance, compared to the use of other features. View full abstract»

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    Automatic bandwidth estimation strategy for high-quality non-parametric modeling based moving object detection

    Cuevas, C. ; Garcia, N.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115800
    Publication Year: 2011 , Page(s): 1757 - 1760

    IEEE Conference Publications

    Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier. View full abstract»

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    A new information fusion approach for image segmentation

    Wentao Xu ; Kanawong, R. ; Ye Duan ; Guixu Zhang
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116148
    Publication Year: 2011 , Page(s): 2873 - 2876
    Cited by:  Papers (1)

    IEEE Conference Publications

    In this paper we propose a new hybrid image segmentation algorithm that integrate the region-based method with the boundary-based method. More specifically we take an information fusion approach based on the Tensor Voting framework that seamlessly fuse the information from the region-based Mean Shift method with the boundary-based Canny Edge Detection algorithm. We have tested our algorithm on several images from the Caltech 101 database [18]. Experiments results show the new algorithm is very efficient and can achieve very good segmentation results. View full abstract»

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    A fast object tracking approach based on sparse representation

    Zhenjun Han ; Jianbin Jiao ; Qixiang Ye
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115831
    Publication Year: 2011 , Page(s): 1865 - 1868
    Cited by:  Papers (1)

    IEEE Conference Publications

    This paper proposes a new approach based on object sparse representation (OSR) for object tracking. The OSR method implemented by L1-norm minimization is robust to the partial occlusion and deterioration in object images. Firstly, we dynamically construct a set of samples in a predicted searching window in a new video frame, on which the sparse representation of the tracked object can be calculated by the OSR method. This procedure can automatically select the subset of the samples as a basis which most compactly expresses the object with small residuals and rejects all other possible but less compact representations. In terms of this sparse and compact representation, the instantaneous tracking result is achieved in the new video frame. Extensive comparative experiments demonstrate the effectiveness of the proposed approach especially in occlusion context. View full abstract»

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    Boosting segmentation results by contour relaxation

    Guevara, A. ; Conrad, C. ; Mester, R.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115703
    Publication Year: 2011 , Page(s): 1405 - 1408

    IEEE Conference Publications

    This paper presents a versatile algorithmic building block that allows to significantly improve intermediate and final results of numerous variations of segmentation. The segmentation `context' can be very different in terms of the used data modality (gray scale, color, texture features, depth data, motion, ...), in terms of single frame vs. sequence segmentation, and in terms of the used initialization (measurement space clustering vs. `blind' initialization vs. interactively `sketching' the segmentation). For all these mentioned variations, the contour relaxation approach presented here offers the capability of very efficiently obtaining a segmentation result that is both visually pleasing as well as locally optimal with respect to a statistically well justified target functional. View full abstract»

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    Realistic log-compressed law for ultrasound image recovery

    Vegas-Sanchez-Ferrero, G. ; Martin-Martinez, D. ; Casaseca-de-la-Higuera, P. ; Cordero-Grande, L. ; Aja-Fernandez, S. ; Martin-Fernandez, M. ; Palencia, C.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115877
    Publication Year: 2011 , Page(s): 2029 - 2032

    IEEE Conference Publications

    A realistic log-compressed law for ultrasound images based on a real device is proposed. The model takes into account the linear behavior of the logarithmic amplifier for small signal gain which transforms image values in a different way as the classical models do. Additionally, for recovery purposes, a method for the estimation of the compression parameters is also proposed when a realistic log-compressed law is considered. Results with synthetic images show that the proposed method achieved a consistent Rayleigh parameter estimate with a very low error. Experiments with real images show that the inversion method is consistent through the whole acquisition process when parameters of the logarithmic amplifier are assumed constant. View full abstract»

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    Motion artifact-free HDR imaging under dynamic environments

    Sung-Chan Park ; Hyun-Hwa Oh ; Jae-Hyun Kwon ; Wonhee Choe ; Seong-Deok Lee
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116479
    Publication Year: 2011 , Page(s): 353 - 356
    Cited by:  Papers (3)

    IEEE Conference Publications

    High dynamic range (HDR) imaging is one of the most important emerging fields of the next generation digital cameras. It is hard to handle a problem so-called ghosting artifact caused by camera shake and/or object motion in the method of fusing a set of differently exposed images. Some object motions around under or over saturation region still produce severe artifacts due to the reference image's dynamic range limitation. For the commercial product, it is the important problem to be solved completely. We analyze this problem and propose a new HDR deghosting scheme capable of dealing with various motions. In order to avoid the ghosting artifacts, we capture only two uncompressed Bayer raw images with different exposures, select the wider dynamic range image as a reference, and process them in the Bayer domain. The experimental results show that our proposed method provides motion artifact-free under dynamic environments with various moving objects. View full abstract»

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    Face recognition through regional weight estimation

    Yule, D. ; Liang Chen
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115801
    Publication Year: 2011 , Page(s): 1761 - 1764

    IEEE Conference Publications

    Face recognition has become a very important field of AI, with many competing techniques, both holistic and local. Recently, a new framework for embedding holistic face recognition algorithms into a regional voting approach, has been shown to be a very stable and accurate mechanism for face recognition. A new system is proposed, which extends the regional voting concept and adds weights to each region. Several techniques for estimating the weights are discussed. The system is shown to outperform several other leading face recognition algorithms. View full abstract»

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    Content-aware utility-fair video streaming in wireless broadcasting networks

    Wen Ji ; Zhu Li ; Yiqiang Chen
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115717
    Publication Year: 2011 , Page(s): 145 - 148

    IEEE Conference Publications

    In wireless multi-content video broadcasting system, a critical problem is fairness among contents with respect to heterogeneous characteristics. To address this problem, we propose an approach of content-aware utility-fair streaming control scheme, which aims at heterogeneous QoS video provision and ensures max-min utility-fair sharing among video streams. First, we introduce a hybrid temporal-spatial quality metric to model content-aware utility so as to serve mobile users with heterogeneous QoS requirements. Second, we use max-min utility-fair scheme to guide rate allocation and video content generation among multi-content video broadcasting. Simulation results demonstrate the proposed approach can achieve utility fair among multiple video contents, and provide better quality of service to all broadcasting users especially when available bandwidth is limited. View full abstract»

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    Tracking pedestrians using smoothed colour histograms in an interacting multiple model framework

    Zhengqiang Jiang ; Huynh, D.Q. ; Moran, W. ; Challa, S.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116102
    Publication Year: 2011 , Page(s): 2313 - 2316
    Cited by:  Papers (3)

    IEEE Conference Publications

    In this paper, we present a method for tracking pedestrians in video sequences captured by a fixed camera. Pedestrians are detected in every video frame using the human detector proposed by Dalal and Triggs. An interacting multiple model method is used to predict and update pedestrian trajectories from current frame to the next one. We employ a stationary model and a constant velocity model in our method to handle cases such as when a pedestrian suddenly stops or changes walking direction. We smooth the colour histogram that describes the appearance of each detected pedestrian using kernel density estimation. Our experimental results show that our tracking method outperforms one that uses the Kalman filter and colour histograms. View full abstract»

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    Model-based multiview stereo via level sets with statistical shape prior

    El-Melegy, M. ; Al-Ashwal, N. ; Farag, A.A.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6365368
    Publication Year: 2011

    IEEE Conference Publications

    In this paper, we propose a methodology to incorporate 3D shape prior information in multi-view stereo. This is important for applications that deal with specific category of objects. The methodology is based on a new formulation of a level-set based energy functional. Shape prior model is then embedded in the energy functional to allow the reconstruction of an object with shape variations consistent with the training model examples. Several experiments to evaluate the proposed methodology are presented. View full abstract»

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    Automatic segmentation for Arabic characters in handwriting documents

    Lawgali, A. ; Bouridane, A. ; Angelova, M. ; Ghassemlooy, Z.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6116476
    Publication Year: 2011 , Page(s): 3529 - 3532
    Cited by:  Papers (1)

    IEEE Conference Publications

    The cursive and ligature nature of the Arabic script make the segmentation of words into individual characters a difficult task. Despite attempts to apply methods for cursive Latin and other scripts to Arabic script, it is generally insufficient to segment the Arabic text. This paper proposes a new segmentation algorithm for the handwritten Arabic text and the main idea consists of segmenting the word into sub-words and then computing the baseline of each sub-word. Using the descenders of sub-words and the baseline, candidate points are then calculated using a vertical projection. The algorithm has been tested using 800 handwritten Arabic words taken from the IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained. View full abstract»

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    Temporal trajectory filtering for bi-directional predicted frames

    Esche, M. ; Krutz, A. ; Glantz, A. ; Sikora, T.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115765
    Publication Year: 2011 , Page(s): 1633 - 1636

    IEEE Conference Publications

    In this work the application of a temporal in-loop filtering approach for B-frames in video compression based on the Temporal Trajectory Filter (TTF) is investigated. The TTF constructs temporal pixel trajectories for individual image points in the P-frames of a video sequence, which can be utilized to improve the quality of the reconstructed frames used for prediction. It is shown, how this concept can be adapted to B-frames despite the fact that these already use temporal motion information to a great extent through the flexible choice of reference frames and prediction modes. The proposed filter has been integrated into the H.264/AVC encoder using the extended profile with hierarchical B-frames and was tested on a wide range of sequences. The filter produces bit rate reductions of up to -4% with an average of -1.6% over all tested sequences while also improving the subjective quality of the decoded video. View full abstract»

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    Efficiently selecting spatially distributed keypoints for visual tracking

    Gauglitz, S. ; Foschini, L. ; Turk, M. ; Hollerer, T.
    Image Processing (ICIP), 2011 18th IEEE International Conference on

    DOI: 10.1109/ICIP.2011.6115832
    Publication Year: 2011 , Page(s): 1869 - 1872
    Cited by:  Papers (4)

    IEEE Conference Publications

    We describe an algorithm dubbed Suppression via Disk Covering (SDC) to efficiently select a set of strong, spatially distributed key-points, and we show that selecting keypoint in this way significantly improves visual tracking. We also describe two efficient implementation schemes for the popular Adaptive Non-Maximal Suppression algorithm, and show empirically that SDC is significantly faster while providing the same improvements with respect to tracking robustness. In our particular application, using SDC to filter the output of an inexpensive (but, by itself, less reliable) keypoint detector (FAST) results in higher tracking robustness at significantly lower total cost than using a computationally more expensive detector. View full abstract»

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