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

Date 11-14 Sept. 2011

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Displaying Results 1 - 25 of 933
  • [Title page]

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  • [Welcome one]

    Page(s): ii - iii
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  • [Welcome two]

    Page(s): iv - v
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  • Committees

    Page(s): vi - vii
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  • Program

    Page(s): viii - cxxi
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  • Modern shape from shading and beyond

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (231 KB) |  | HTML iconHTML  

    Shape from shading (SFS) is one of the most fundamental problems in Computer Vision: reconstruction of the three-dimensional (3D) shape of photographed objects given a single input image. Image formation is then modelled based on assumptions on illumination and light reflectance. Solving the 3D shape from the model is useful for many applications. SFS has been extended, for example, to several input images of the same scene taken under different lighting conditions, known as photometric stereo. The aim of the session is to introduce state-of-the-art models and algorithms in the field, and to discuss future directions of research. View full abstract»

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  • Numerical schemes for advanced reflectance models for Shape from Shading

    Page(s): 5 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (704 KB) |  | HTML iconHTML  

    In recent years, Shape from Shading (SfS) research has been dominated by the rise of perspective models and advanced numerical schemes, which allowed for impressive results compared to previous methods in the field. Despite of these groundbreaking developments, researchers concentrated on Lambertian reflectance models. Recently, some researchers started to include more advanced reflectance models into the Lambertian state-of-the art model of Prados and Faugeras. Such methods include simple models for specular reflectance, which is of particular importance when reconstructing shiny objects. Other models replace Lambertian reflectance by more advanced types of diffuse reflectance like the Oren-Nayar model, which is of particular importance for the reconstruction of human skin. We review these reflectance models and discuss their compatibility with the state-of-the-art numerical solvers for SfS, both iterative and non-iterative. View full abstract»

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  • Shape from shading with specular highlights: Analysis of the Phong model

    Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1715 KB) |  | HTML iconHTML  

    Recently, non-Lambertian models for perspective shape from shading (PSFS) have received attention in the literature. The Phong-based PSFS model combining Lambertian and specular reflection has been shown to give good results for objects in real-world images. In this paper, we present at hand of this model the first analysis of a non-Lambertian PSFS model in the literature. We show mathematically and experimentally how crucial the specular part of the model is for the reconstruction. Moreover, we give a detailed analysis of the Hamiltonian defining the model. While the non-Lambertian reflectance is generally assumed to lead to numerical difficulties, this investigation shows that an efficient fast marching scheme can be applied successfully without losing depth information. Our work represents a first step towards the thorough understanding of non-Lambertian PSFS models. View full abstract»

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  • Shape-from-shading under complex natural illumination

    Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1194 KB) |  | HTML iconHTML  

    We present a shape-from-shading algorithm for Lambertian surfaces of uniform but unknown albedo, illuminated by unknown, arbitrarily complex environment lighting. Our approach is based on a first order spherical harmonic approximation to the reflectance map. This is estimated from the image using surface normals interpolated from boundary points. The shape-from-shading step minimises local brightness error and an edge sensitive smoothness constraint. This involves the solution of a linear least squares problem with a quadratic equality constraint, the global optimum of which can be found using the method of Lagrange multipliers. We demonstrate the performance of the algorithm on complex objects rendered under realworld illumination. View full abstract»

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  • Reconstruction of non-Lambertian surfaces by fusion of Shape from Shading and active range scanning

    Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4581 KB) |  | HTML iconHTML  

    In this paper, we present an algorithm for the fusion of surface normals estimated based on Shape from Shading with absolute depth data under exploitation of the mutual advantages, regarding non-Lambertian surfaces with non-uniform albedos. While photometric 3D reconstruction methods yield dense surface detail information which is reliable on small scales, active range scanning provides absolute depth data which are typically noisy on small scales but reliable on large scales. The proposed algorithm applies an iterative refinement to the reconstructed surface in order to suppress errors that result from measurement uncertainties in the surface normals and the absolute depth data by simultaneous minimization of a global error functional. The obtained surface is the best fit to the observed image intensities and depth data. We apply our framework to small-scale real-world objects and to regions of the lunar surface. View full abstract»

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  • Shape from specular reflection in calibrated environments and the integration of spatial normal fields

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4539 KB) |  | HTML iconHTML  

    Reflections of a scene in a mirror surface contain information on its shape. This information is accessible by measurement through an optical metrology technique called deflectometry. The result is a field of normal vectors to the unknown surface having the remarkable property that it equally changes in all spatial directions, unlike normal maps occurring, e.g., in Shape from Shading. Its integration into a zero-order reconstruction of the surface thus deserves special attention. We develop a novel algorithm for this purpose which is relatively straightforward to implement yet outperforms existing ones in terms of efficiency and robustness. Experimental results on synthetic and real data complement the theoretical discussion. View full abstract»

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  • Joint pose estimation and action recognition in image graphs

    Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB) |  | HTML iconHTML  

    Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset. View full abstract»

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  • Inferring 3D body pose using variational semi-parametric regression

    Page(s): 29 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (701 KB) |  | HTML iconHTML  

    To deal with multi-modality in human pose estimation, mixture models or local models are introduced. However, problems with over-fitting and generalization are caused by our necessarily limited data, and the regression parameters need to be determined without resorting to slow and processor-hungry techniques, such as cross validation. To compensate these problems, we have developed a semi-parametric regression model in latent space with variational inference. Our method performed competitively in comparison to other current methods. View full abstract»

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  • Automatic target recognition using discriminative graphical models

    Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6275 KB) |  | HTML iconHTML  

    Of recent interest in automatic target recognition (ATR) is the problem of combining the merits of multiple classifiers. This is commonly done by “fusing” the soft-outputs of several classifiers into making a single decision. We observe that the improvement in recognition rates afforded by these approaches is due to the complementary yet correlated information captured by different features/signal representations that these individual classifiers employ. We present the use of probabilistic graphical models in modeling and capturing feature dependencies that are crucial for target classification. In particular, we develop a two-stage target recognition framework that combines the merits of distinct and sparse signal representations with discriminatively learnt graphical models. The first stage designs multiple projections yielding M >; 1 sparse representations, while the second stage models each individual representation using graphs and combines these initially disjoint and simple graphical models into a thicker probabilistic graphical model. Experimental results show that our approach outperforms state-of-the art target classification techniques in terms of recognition rates. The use of graphical models is particularly meritorious when feature dimensionality is high and training is limited - a commonly observed constraint in synthetic aperture radar (SAR) imagery based target recognition. View full abstract»

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  • A Belief Propagation algorithm for bias field estimation and image segmentation

    Page(s): 37 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (342 KB) |  | HTML iconHTML  

    Intensity-based image segmentation is often plagued by the spatial intensity inhomogeneities (or non-uniformities) that are caused by the imperfection of the imaging devices and the varying operating conditions, also known as the bias field. We present a graphical model representation of the joint segmentation and bias field estimation problem and propose an iterative solver based on the Belief Propagation (BP) algorithm. The intractable joint inference problem of the original graphical model is decoupled into two MRF-MAP estimation problems and solved by a discrete-valued BP and a Gaussian BP, respectively and iteratively. We validate our method using both simulated and real data and show its connection to some of the classical filtering-based approaches. View full abstract»

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  • Planarity-enforcing higher-order graph cut

    Page(s): 41 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (719 KB) |  | HTML iconHTML  

    When image primitives cannot be robustly extracted, the estimation of a perspective transformation between overlapping images can be formulated as a markov random field (MRF) and minimized efficiently using graph cuts. For well contrasted images with low noise level, a first order MRF leads to an accurate and robust registration. With increasing noise however, the registration quality decreases rapidly. This contribution presents a novel algorithm that enforces planarity (as required for perspective transformations) as a soft constraint by adding higher-order cliques to the energy formulation. Results show that for low levels of Gaussian noise (standard deviation σn ϵ [0,4]), the algorithm performs comparably to the standard first order formulation. For increasing levels of noise (σn ϵ [5,12]), the found solution is roughly twice as accurate (deviation of ≈2 pixels on average compared to ≈4 pixels for σn = 10). View full abstract»

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  • Learning structural conjunction of image content by sparse graphical model

    Page(s): 45 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2576 KB) |  | HTML iconHTML  

    In this paper we present a novel method on learning structural conjunction of image content by sparse graphical model. We first use matrix-variate distributions to formulate two statistical structure models and establish the connection between them. The connection leads us to sparse Gaussian graphical models in which sparse regression technique such as lasso is used for concentration matrix estimation as well as structure learning. Our proposed theoretical framework and structure selection methods provide an approach for exploiting structural conjunction of data. We apply this approach to construction of underlying structural correlation between image content, and demonstrate the effectiveness by solving image jigsaw problem. View full abstract»

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  • Automatic quality enhancement and nerve fibre layer artefacts removal in retina fundus images by off axis imaging

    Page(s): 49 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (906 KB) |  | HTML iconHTML  

    Retinal fundus images acquired with non-mydriatic digital fundus cameras are a versatile tool for the diagnosis of various retinal diseases. Even with relative ease of use, the images produced sometimes suffer from reflectance artefacts mainly due to the nerve fibre layer (NFL) or camera lens related reflections. We propose a technique that employs multiple fundus images to obtain a single higher quality image without these reflectance artefacts, which also compensates for a sub- optimal illumination. The removal of bright artefacts, can have great benefits for the reduction of false positives in the detection of retinal lesions by automatic systems or manual inspection. The fundus images are acquired by changing the stare point of the patient but keeping the camera fixed. Between each shot, the apparent shape and position of all the retinal structures that do not exhibit isotropic reflectance (e.g. bright artefacts) change. This physical effect is exploited by our algorithm. View full abstract»

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  • Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification

    Page(s): 53 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1705 KB) |  | HTML iconHTML  

    This paper presents significant improvements of Gray Level Size Zone Matrix (GLSZM) which is a bivariate statistical representation of texture, based on the co-occurrences of size/intensity of each flat zone (connected pixels of the same gray level). The first improvement is a multi-scale extension of the matrix which merges various quantizations of gray levels. A second alternative is proposed to take into account radial distribution of zone intensities. The third variant is a generalization of the matrix structure which allows to analyze fibrous textures, by changing the pair intensity/size for the pair length/orientation of each region. The interest of these improved descriptors is illustrated by texture classification problems arising from quantitative cell biology. View full abstract»

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  • Comparison of energy minimization methods for 3-D brain tissue classification

    Page(s): 57 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (370 KB) |  | HTML iconHTML  

    This paper presents 3-D brain tissue classification schemes using three recent promising energy minimization methods for Markov random fields: graph cuts, loopy belief propagation and tree-reweighted message passing. The classification is performed us ng the well known finite Gaussian mixture Markov Random Field model. Results from the above methods are compared with widely used iterative conditional modes algorithm. The evaluation is per formed on a dataset containing simulated Tl-weighted MR brain volumes with varying noise and intensity non-uniformities. The comparisons are performed in terms of energies as well as based on ground truth segmentations, using various quantitative metrics. View full abstract»

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  • Clump splitting via bottleneck detection

    Page(s): 61 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (685 KB) |  | HTML iconHTML  

    Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for the splitting of clumps formed by multiple objects due to under-segmentation. The algorithm includes two steps: finding a pair of points for clump splitting, and joining the pair of selected points. In the first step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly convex shape. In the second step, the selected pair of splitting points is joined by finding the optimal splitting line between them, based on minimizing an image energy. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach has several advantages over existing splitting methods in identifying points for splitting as well as finding an accurate split line. View full abstract»

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  • Texture classification of scarred and non-scarred myocardium in cardiac MRI using learned dictionaries

    Page(s): 65 - 68
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (543 KB) |  | HTML iconHTML  

    The late gadolinium enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scar area in myocardium for thorough examination. The results in our previous work [1] arises the hypothesis that there are textural differences between the non-scarred myocardium and the scarred areas. This paper presents our work of testing the hypothesis further by applying dictionary learning techniques and sparse representation on CMR images (manually segmented by cardiologists) in order to find textural differences in the myocardium and to classify texture in the non-scarred myocardium and the scarred areas. After myocardial infarction, cardiac patients considered to have high risk of ventricular arrhythmia are implanted with Implantable Cardioverter-Defibrillator (ICD). Our ultimate goal is to accurately identify the patients with highest risk of arrhythmia, who are to be implanted with ICD by exploring the textural properties in the scarred region of late gadolinium enhanced CMR images. View full abstract»

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  • Automatic IVUS media-adventitia border extraction using double interface graph cut segmentation

    Page(s): 69 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2035 KB) |  | HTML iconHTML  

    We present a fully automatic segmentation method to extract media-adventitia border in IVUS images. Segmentation in IVUS has shown to be an intricate process due to relatively low contrast and various forms of interferences and artifacts caused by, for example, calcification and acoustic shadow. Graph cut based methods often require careful manual initialization and produces in consistent tracing of the border. We use a double interface automatic graph cut technique to prevent the extraction of media-adventitia border from being distracted by those image features. Novel cost functions are derived from using a combination of complementary texture features. Comparative studies on manual labeled data show promising performance of the proposed method. View full abstract»

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  • Non-linearization of free schrodinger equation and pseudo-morphological complex diffusion operators

    Page(s): 73 - 76
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    The paper deals with a generalization of the complex diffusion in order to introduce pseudo-morphological complex filters which mimic dilation/erosion operators. The non- linearization paradigm is based on the counter-harmonic mean. The physical model underlying complex diffusion is the free Schrodinger equation and consequently the proposed operators can be interpreted as the asymptotic "pseudo-morphological" solution of this fundamental equation. Theoretical results are illustrated with some image filtering examples. View full abstract»

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  • Adaptive filtering of raster map images using optimal context selection

    Page(s): 77 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (779 KB) |  | HTML iconHTML  

    Filtering of raster map images or more general class of palette-indexed images is considered as a discrete denoising problem with finite color output. Statistical features of local context are used to avoid damages of some specific but frequently occurring contexts caused by conventional filters. Several context-based approaches have been developed using either fixed context templates or context tree modeling. However, these algorithms fail to reveal the local geometrical structures when the underlying contexts are also contaminated. To address this problem, we propose a novel context-based voting method to identify the possible noisy pixels, which are excluded in the context selection and optimization. Experimental results show that the proposed context based filtering outperforms all other existing filters both for impulsive and Gaussian additive noise. View full abstract»

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