OpenRaiser/Intern-Atlas huggingface dataset:
What Data to Co-register for Computing Atlases
Authors: B. T. Thomas Yeo, Mert R. Sabuncu, Hartmut Mohlberg, Katrin Amunts, Karl Zilles, Polina Golland, Bruce Fischl
Venue: ICCV
Year: 2007

We argue that registration should be thought of as a means to an end, and not as a goal by itself. In particular, we consider the problem of predicting the locations of hidden labels of a test image using observable features, given a training set with both the hidden labels and observable features. For example, the hidden labels could be segmentation labels or activation regions in fMRI, while the observable features could be sulcal geometry or MR intensity. We analyze a probabilistic framework for computing an optimal atlas, and the subsequent registration of a new subject using only the observable features to optimize the hidden label alignment to the training set. We compare two approaches for co-registering training images for the atlas construction: the traditional approach of only using observable features and a novel approach of only using hidden labels. We argue that the alternative approach is superior particularly when the relationship between the hidden labels and observable features is complex and unknown. As an application, we consider the task of registering cortical folds to optimize Brodmann area localization. We show that the alignment of the Brodmann areas improves by up to 25% when using the alternative atlas compared with the traditional atlas. To the best of our knowledge, these are the most accurate Brodmann area localization results (achieved via cortical fold registration) reported to date.
Correspondence Transfer for the Registration of Multimodal Images
Authors: Zhao Yi, Stefano Soatto
Venue: ICCV
Year: 2007

Gene expression data provide information on the location where certain genes are active; in order for this to be useful, such a location must be registered to an anatomical atlas. Because gene expression maps are considerably different from each other - they display the expression of different genes - and from the anatomical atlas, this problem is currently addressed either manually by trained experts, or by neglecting all image information and only using the pre-segmented boundaries. In this manuscript we concentrate on data discrepancy measures that take into account image information when this is present in both the target and template images. We exploit such "bi-lateral" structures to drive the correspondence process in regions where the intensity information is inconsistent, analogously to a "motion in- painting" task. Although no ground truth can be established, and prior information clearly plays a key role, we show that our model achieves desirable results on subjective tests validated by expert subjects.
Stereo Matching with the Distinctive Similarity Measure
Authors: Kuk-Jin Yoon, In-So Kweon
Venue: ICCV
Year: 2007

The point ambiguity owing to the ambiguous local appearances of image points is the one of the main causes making the stereo problem difficult. Under the point ambiguity, local similarity measures are easy to be ambiguous and this results in false matches in ambiguous regions. In this paper, we present the new similarity measure to resolve the point ambiguity problem based on the idea that the distinctiveness, not the interest, is the appropriate criterion for the feature selection under the point ambiguity. The proposed similarity measure named the Distinctive Similarity Measure (DSM) is essentially based on the distinctiveness of image points and the dissimilarity between them, which are both closely related to the local appearances of image points; the distinctiveness of an image point is related to the probability of a mismatch while the dissimilarity is related to the probability of a good match. We verify the efficiency of the proposed DSM by using testbed image sets. Experimental results show that the proposed DSM is very effective and can be easily used for improving the performance of existing stereo methods under the point ambiguity.
Efficient Message Representations for Belief Propagation
Authors: Tian-Li Yu 0002, Ruei-Sung Lin, Boaz J. Super, Bei Tang
Venue: ICCV
Year: 2007

Belief propagation (BP) has been successfully used to approximate the solutions of various Markov random field (MRF) formulated energy minimization problems. However, large MRFs require a significant amount of memory to store the intermediate belief messages. We observe that these messages have redundant information due to the imposed smoothness prior. In this paper, we study the feasibility of applying compression techniques to the messages in the min-sum/max-product BP algorithm with 1D labels to improve the memory efficiency and reduce the read/write bandwidth. We articulate properties that an efficient message representation should satisfy. We investigate two common compression schemes, predictive coding and linear transform coding (PCA), and then propose a novel envelope point transform (EPT) method. Predictive coding is efficient and supports linear operations directly in the compressed domain, but it is only compatible with the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> smoothness function. PCA has the disadvantage that it does not guarantee the preservation of the minimal label. EPT is not limited to L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> smoothness cost and allows a flexible quality vs. compression ratio tradeoff compared with predictive coding. Experiments on dense stereo reconstruction have shown that the predictive scheme and EPT can achieve 8times or more compression without significant loss of depth accuracy.
Cortical Folding Development Study based on Over-Complete Spherical Wavelets
Authors: Peng Yu, B. T. Thomas Yeo, Patricia Ellen Grant, Bruce Fischl, Polina Golland
Venue: ICCV
Year: 2007

We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been shown to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant under rotations of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that over-complete spherical wavelets allow us to build more stable cortical folding development models, and detect a wider array of regions of folding development in a newborn dataset.
Blurred/Non-Blurred Image Alignment using Sparseness Prior
Authors: Lu Yuan, Jian Sun 0001, Long Quan, Heung-Yeung Shum
Venue: ICCV
Year: 2007

Aligning a pair of blurred and non-blurred images is a prerequisite for many image and video restoration and graphics applications. The traditional alignment methods such as direct and feature-based approaches cannot be used due to the presence of motion blur in one image of the pair. In this paper, we present an effective and accurate alignment approach for a blurred/non-blurred image pair. We exploit a statistical characteristic of the real blur kernel - the marginal distribution of kernel value is sparse. Using this sparseness prior, we can search the best alignment which produces the sparsest blur kernel. The search is carried out in scale space with a coarse-to-fine strategy for efficiency. Finally, we demonstrate the effectiveness of our algorithm for image deblurring, video restoration, and image matting.
Structure-Specific Statistical Mapping of White Matter Tracts using the Continuous Medial Representation
Authors: Paul A. Yushkevich, Hui Zhang 0005, Tony J. Simon, James C. Gee
Venue: ICCV
Year: 2007

This paper describes a new statistical analysis framework for diffusion-based white matter studies. The framework is based on a recent unbiased normalization algorithm for diffusion tensor images. Taking advantage of the fact that most human white matter tracts are thin sheet-like structures, this framework uses deformable medial models to represent six of the major tracts in a white matter atlas derived for a given set of images. The medial representation allows one to average tensor-based features along directions perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. Unlike earlier work in the area of tract-based spatial statistics (Smith et al, 2006), this framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q deletion syndrome.
Graph Based Discriminative Learning for Robust and Efficient Object Tracking
Authors: Xiaoqin Zhang 0002, Weiming Hu 0004, Stephen J. Maybank, Xi Li 0001
Venue: ICCV
Year: 2007

Object tracking is viewed as a two-class 'one-versus-rest' classification problem, in which the sample distribution of the target is approximately Gausian while the background samples are often multimodal. Based on these special properties, we propose a graph embedding based discriminative learning method, in which the topology structures of graphs are carefully designed to reflect the properties of the sample distributions. This method can simultaneously learn the subspace of the target and its local discriminative structure against the background. Moreover, a heuristic negative sample selection scheme is adopted to make the classification more effective. In tracking procedure, the graph based learning is embedded into a Bayesian inference framework cascaded with hierarchical motion estimation, which significantly improves the accuracy and efficiency of the localization. Furthermore, an incremental updating technique for the graphs is developed to capture the changes in both appearance and illumination. Experimental results demonstrate that, compared with two state-of-the-art methods, the proposed tracking algorithm is more efficient and effective, especially in dynamically changing and clutter scenes.
Moving Object Extraction with a Hand-held Camera
Authors: Guofeng Zhang 0001, Jiaya Jia, Wei Xiong, Tien-Tsin Wong, Pheng-Ann Heng, Hujun Bao
Venue: ICCV
Year: 2007

This paper presents a new method to detect and accurately extract the moving object from a video sequence taken by a hand-held camera. In order to extract the high quality moving foreground, previous approaches usually assume that the background is static or through only planar-perspective transformation. In our method, based on the robust motion estimation, we are capable of handling challenging videos where the background contains complex depth and the camera undergoes unknown motions. We propose the appearance and structure consistency constraint in 3D warping to robustly model the background, which greatly improves the foreground separation even on the object boundary. The estimated dense motion field and the bi- layer segmentation result are iteratively refined where continuous and discrete optimizations are alternatively used. Experimental results of high quality moving object extraction from challenging videos demonstrate the effectiveness of our method.
Detection and Tracking of Multiple Humans with Extensive Pose Articulation
Authors: Li Zhang, Bo Wu 0001, Ramakant Nevatia
Venue: ICCV
Year: 2007

We describe a method for detecting and tracking humans. Different from most of the previous work, we focus on humans with extensive pose articulations, under situations where there is typically only a single camera, multiple humans are present and the image resolution is low. In our method pose clusters are learned from an embedded silhouette manifold. A set of object detectors, each of which corresponds to one pose cluster, are trained based on a novel Object-Weighted Appearance Model. A probabilistic pose-based transition model is used to track multiple objects within a sliding window buffer, making use of the detection responses. The track segments in the sliding windows are connected sequentially into full trajectories. Experiments on a set of challenging surveillance videos are presented; these show good performance of our approach compared to standard pedestrian detectors, under difficult conditions.
Efficient Feature Extraction for Image Classification
Authors: Wei Zhang 0016, Xiangyang Xue 0001, Zichen Sun, Yue-Fei Guo, Mingmin Chi, Hong Lu 0001
Venue: ICCV
Year: 2007

In many image classification applications, input feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or to reduce the cost of computation. In this paper, we extract discriminant features for image classification by learning a low-dimensional embedding from finite labeled samples. In the new feature space, intra-class compactness and extra-class separability are achieved simultaneously. Target dimensionality of the embedding is selected by spectral analysis. Our method is designed suitable for data with both uni- and multi-modal class distributions. We also develop its two-dimensional variant which makes use of the matrix representation of images. Experimental results on three real image datasets demonstrate the efficacy of our method compared to the state of the art.
A Restoration Framework for Correcting Photometric and Geometric Distortions in Camera-based Document Images
Authors: Li Zhang 0005, Andy M. Yip, Chew Lim Tan
Venue: ICCV
Year: 2007

This paper presents a restoration framework for correcting both photometric and geometric distortions in camera-based images of non-planar shaped documents to facilitate human perception and machine recognition. The photometric distortions, usually perceived as shading artifacts, are corrected by separating the shading image from the reflectance image through digital inpainting and surface fitting techniques. Meanwhile, the extracted shading image is also used to recover the document's surface shape through a Shape-from-Shading (SFS) method with a generic formulation of the image irradiance under arbitrary illumination conditions. The recovered surface shape is then employed to correct the geometric distortions through a physically-based flattening process. Results on real document images demonstrate the performance of each sub-task and the functionality of the whole framework. OCR results on restored images also show great improvements over the original distorted images.
Real-time Accurate Object Detection using Multiple Resolutions
Authors: Wei Zhang 0002, Gregory J. Zelinsky, Dimitris Samaras
Venue: ICCV
Year: 2007

We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.
Differential EMD Tracking
Authors: Qi Zhao, Shane Brennan, Hai Tao
Venue: ICCV
Year: 2007

Illumination changes cause object appearance to change drastically and many existing tracking algorithms lack the capability to handle this problem. The Earth mover's distance (EMD) is a similarity measure that is more robust against illumination changes. However, EMD is computationally expensive and we therefore propose the differential EMD (DEMD) algorithm which computes the derivative of the EMD with respect to the object location so that the EMD does not need to be computed for every location in the tracking window. The fast differential formula is derived based on the sensitivity analysis of the simplex method as applied to the EMD formula. To further reduce the computation, signatures, i.e., variable-size descriptions of distributions, are employed as an object representation. The new algorithm models local background scenes as well as foreground objects to handle scale changes in a principled way. Extensive quantitative evaluation of the proposed algorithm has been carried out using benchmark sequences and the improvement over the standard mean shift tracker is demonstrated.
Contextual Distance for Data Perception
Authors: Deli Zhao, Zhouchen Lin, Xiaoou Tang
Venue: ICCV
Year: 2007

Structural perception of data plays a fundamental role in pattern analysis and machine learning. In this paper, we develop a new structural perception of data based on local contexts. We first identify the contextual set of a point by finding its nearest neighbors. Then the contextual distance between the point and one of its neighbors is defined by the difference between their contribution to the integrity of the geometric structure of the contextual set, which is depicted by a structural descriptor. The centroid and the coding length are introduced as the examples of descriptors of the contextual set. Furthermore, a directed graph (digraph) is built to model the asymmetry of perception. The edges of the digraph are weighted based on the contextual distances. Thus direction is brought to the undirected data. And the structural perception of data can be performed by mining the properties of the digraph. We also present the method for deriving the global digraph Laplacian from the alignment of the local digraph Laplacians. Experimental results on clustering and ranking of toy problems and real data show the superiority of asymmetric perception.
Laplacian PCA and Its Applications
Authors: Deli Zhao, Zhouchen Lin, Xiaoou Tang
Venue: ICCV
Year: 2007

Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPCA) algorithm which is the extension of PCA to a more general form by locally optimizing the weighted scatter. In addition to the simplicity of PCA, the benefits brought by LPCA are twofold: the strong robustness against noise and the weak metric-dependence on sample spaces. The LPCA algorithm is based on the global alignment of locally Gaussian or linear subspaces via an alignment technique borrowed from manifold learning. Based on the coding length of local samples, the weights can be determined to capture the local principal structure of data. We also give the exemplary application of LPCA to manifold learning. Manifold unfolding (non-linear dimensionality reduction) can be performed by the alignment of tangential maps which are linear transformations of tangent coordinates approximated by LPCA. The superiority of LPCA to PCA and kernel PCA is verified by the experiments on face recognition (FRGC version 2 face database) and manifold (Scherk surface) unfolding.
Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features
Authors: Yefeng Zheng 0001, Adrian Barbu, Bogdan Georgescu, Michael Scheuering, Dorin Comaniciu
Venue: ICCV
Year: 2007

Multi-chamber heart segmentation is a prerequisite for global quantification of the cardiac function. The complexity of cardiac anatomy, poor contrast, noise or motion artifacts makes this segmentation problem a challenging task. In this paper, we present an efficient, robust, and fully automatic segmentation method for 3D cardiac computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-dimensional similarity search problem for localizing the heart chambers. MSL reduces the number of testing hypotheses by about six orders of magnitude. We also propose to use steerable image features, which incorporate the orientation and scale information into the distribution of sampling points, thus avoiding the time-consuming volume data rotation operations. After determining the similarity transformation of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments on multi-chamber heart segmentation demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.
On Constrained Sparse Matrix Factorization
Authors: Wei-Shi Zheng 0001, Stan Z. Li, Jian-Huang Lai, Shengcai Liao
Venue: ICCV
Year: 2007

Various linear subspace methods can be formulated in the notion of matrix factorization in which a cost function is minimized subject to some constraints. Among them, constraints on sparseness have received much attention recently. Some popular constraints such as non-negativity, lasso penalty, and (plain) orthogonality etc have been so far applied to extract sparse features. However, little work has been done to give theoretical and experimental analyses on the differences of the impacts of different constraints within a framework. In this paper, we analyze the problem in a more general framework called Constrained Sparse Matrix Factorization (CSMF). In CSMF, a particular case called CSMF with non-negative components (CSMFnc) is further discussed. Unlike NMF, CSMFnc allows not only additive but also subtractive combinations of non-negative sparse components. It is useful to produce much sparser features than those produced by NMF and meanwhile have better reconstruction ability, achieving a trade-off between sparseness and low MSE value. Moreover, for optimization, an alternating algorithm is developed and a gentle update strategy is further proposed for handling the alternating process. Experimental analyses are performed on the Swimmer data set and CBCLface database. In particular, CSMF can successfully extract all the proper components without any ghost on Swimmer, gaining a significant improvement over the compared well-known algorithms.
Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation and Registration Framework
Authors: Yuanjie Zheng, Karl V. Steiner, Thomas L. Bauer, Jingyi Yu 0001, Dinggang Shen, Chandra Kambhamettu
Venue: ICCV
Year: 2007

In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the B- Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve the volume and the shape of the tumor. In particular, we set the control points within the tumor to form a control mesh and thus assume the tumor region follows the same rigid transformation as the control mesh. For segmentation, we apply a 2D graph-cut algorithm on the 3D lung and tumor datasets. By iteratively performing segmentation and registration, our method achieves highly accurate segmentation and registration on serial CT data. Finally, since our method eliminates the possible volume variations of the tumor during registration, we can further estimate accurately the tumor growth, an important evidence in lung cancer diagnosis. Initial experiments on five sets of patients ' serial CT data show that our method is robust and reliable.