OpenRaiser/Intern-Atlas huggingface dataset:
Feature Preserving Image Smoothing Using a Continuous Mixture of Tensors
Authors: Özlem N. Subakan, Bing Jian, Baba C. Vemuri, C. Eduardo Vallejos
Venue: ICCV
Year: 2007

Many computer vision and image processing tasks require the preservation of local discontinuities, terminations and bifurcations. Denoising with feature preservation is a challenging task and in this paper, we present a novel technique for preserving complex oriented structures such as junctions and corners present in images. This is achieved in a two stage process namely, (1) All image data are pre-processed to extract local orientation information using a steerable Gabor filter bank. The orientation distribution at each lattice point is then represented by a continuous mixture of Gaussians. The continuous mixture representation can be cast as the Laplace transform of the mixing density over the space of positive definite (covariance) matrices. This mixing density is assumed to be a parameterized distribution, namely, a mixture of Wisharts whose Laplace transform is evaluated in a closed form expression called the Rigaut type function, a scalar-valued function of the parameters of the Wishart distribution. Computation of the weights in the mixture Wisharts is formulated as a sparse deconvolution problem. (2) The feature preserving denoising is then achieved via iterative convolution of the given image data with the Rigaut type function. We present experimental results on noisy data, real 2D images and 3D MRI data acquired from plant roots depicting bifurcating roots. Superior performance of our technique is depicted via comparison to the state-of-the-art anisotropic diffusion filter.
Correspondence Establishment in Statistical Modeling of Shapes with Arbitrary Topology
Authors: Ekaterina Syrkina, Miguel Ángel González Ballester, Gábor Székely
Venue: ICCV
Year: 2007

Correspondence establishment is a key step in statistical shape model building. There are several automated methods for solving this problem in 3D, but they usually can only handle objects with simple topology, like that of a sphere or a disc. We propose an extension to correspondence establishment over a population based on the optimization of the minimal description length function, allowing considering objects with arbitrary topology. Instead of using a fixed structure of kernel placement on a sphere for the systematic manipulation of point landmark positions, we rely on an adaptive, hierarchical organization of surface patches. This hierarchy can be built on surfaces of arbitrary topology and the resulting patches are used as a basis for a consistent, multi-scale modification of the surfaces' parameterization, based on point distribution models. The feasibility of the approach is demonstrated on synthetic models with different topologies.
Variable Dimensional Local Shape Descriptors for Object Recognition in Range Data
Authors: Babak Taati, Michel Bondy, Piotr Jasiobedzki, Michael A. Greenspan
Venue: ICCV
Year: 2007

We propose a new set of highly descriptive local shape descriptors (LSDs) for model-based object recognition and pose determination in input range data. Object recognition is performed in three phases: point matching, where point correspondences are established between range data and the complete model using local shape descriptors; pose recovery, where a computationally robust algorithm generates a rough alignment between the model and its instance in the scene, if such an instance is present; and pose refinement. While previously developed LSDs take a minimalist approach, in that they try to construct low dimensional and compact descriptors, we use high (up to 9) dimensional descriptors as the key to more accurate and robust point correspondence. Our strategy significantly simplifies the computational burden of the pose recovery phase by investing more time in the point matching phase. Experiments with Lidar and dense stereo range data illustrate the effectiveness of the approach by providing a higher percentage of correct matches in the candidate point matches list than a leading minimalist technique. Consequently, the number of RANSAC iterations required for recognition and pose determination is drastically smaller in our approach.
No Grouping Left Behind: From Edges to Curve Fragments
Authors: Amir Tamrakar, Benjamin B. Kimia
Venue: ICCV
Year: 2007

We present a framework for extracting image contours based on geometric and structural consistency among edge element locations and orientations. The paper presents two contributions. First, we observe that while the traditional edge orientation operators are based on first-order derivatives, orientation as tangent of a localized curve requires third-order derivatives. We derive a numerically stable third-order edge operator and show that it outperforms current techniques. Second, we consider all discrete n-tuples of edges in a local neighborhood (7times7) and retain those that are geometrically consistent with a third-order local curve model. This results in a number of ordered discrete combinations of edges, each represented by a bundle of curves. The resulting curve bundle map is a representation of all possible local groupings from which longer contour fragments are constructed. We validate our results and show that our framework outperforms traditional approaches to contour extraction.
Co-Tracking Using Semi-Supervised Support Vector Machines
Authors: Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
Venue: ICCV
Year: 2007

This paper treats tracking as a foreground/background classification problem and proposes an online semi- supervised learning framework. Initialized with a small number of labeled samples, semi-supervised learning treats each new sample as unlabeled data. Classification of new data and updating of the classifier are achieved simultaneously in a co-training framework. The object is represented using independent features and an online support vector machine (SVM) is built for each feature. The predictions from different features are fused by combining the confidence map from each classifier using a classifier weighting method which creates a final classifier that performs better than any classifier based on a single feature. The semi-supervised learning approach then uses the output of the combined confidence map to generate new samples and update the SVMs online. With this approach, the tracker gains increasing knowledge of the object and background and continually improves itself over time. Compared to other discriminative trackers, the online semi-supervised learning approach improves each individual classifier using the information from other features, thus leading to a more robust tracker. Experiments show that this framework performs better than state-of-the-art tracking algorithms on challenging sequences.
From Uncertainties to Statistical Model Building and Segmentation of the Left Ventricle
Authors: Maxime Taron, Nikos Paragios, Marie-Pierre Jolly
Venue: ICCV
Year: 2007

Reliable segmentation of the left ventricle is a long sought objective in medical imaging for automatic retrieval of anatomical and pathological measurements and detection of malfunctions. In this paper, we propose a novel model-constrained approach to address this task. The method is based on an implicit representation of the shape model used in a shape registration framework with a Thin Plate Spline transform to retrieve possible deformations. The main innovation of our approach resides in the use of uncertainties defined on the registered shape to augment the training set and improve the robustness of the statistical deformable model. We use ICA to reduce the dimensionality of the space of deformations and provide a good separation of the different deformable parts of the heart. Furthermore the estimation of uncertainties is also introduced in the segmentation process which is addressed in a variational framework where prior knowledge and visual support are considered. The method lead to very promising qualitative and quantitative experimental results in CT.
Depth-From-Recognition: Inferring Meta-data by Cognitive Feedback
Authors: Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars, Luc Van Gool
Venue: ICCV
Year: 2007

Thanks to recent progress in category-level object recognition, we have now come to a point where these techniques have gained sufficient maturity and accuracy to succesfully feed back their output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer meta-data such as depth cues, 3D points, or object decomposition in images of previously unseen object instances. Our approach builds on the implicit shape model of Leibe and Schiele, and extends it to transfer annotations from training images to test images. Experimental results validate the viability of our approach.
A Unifying Omnidirectional Camera Model and its Applications
Authors: Christian Toepfer, Tobias Ehlgen
Venue: ICCV
Year: 2007

Omnidirectional cameras are leaving the scientific labs to be used in market applications. Many such application areas like robotics and automotive need very accurate models of the image formation. When a car or robot motion is controlled by an omnidirectional camera system, the exact control of action depends on a reliable calibration. An unprecise calibration can cause costly or hazardous consequences for man and machine. Therefore, we developed an omnidirectional camera model that can deal with parabolic and hyperbolic mirrors in combination with distorting lenses. It has the adequate complexity to allow both, precision and robustness of the calibration process. We successfully applied this model to our driver assistance systems of future light and heavy trucks.
Robust Object Trackinng wvith Regional Affine Invariant Features
Authors: Son Dinh Tran, Larry S. Davis
Venue: ICCV
Year: 2007

We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.
Can Born Approximate the Unborn? A New Validity Criterion for the Born Approximation in Microscopic Imaging
Authors: Sigal Trattner, Micha Feigin, Hayit Greenspan, Nir A. Sochen
Venue: ICCV
Year: 2007

The Nomarski differential interference contrast (DIC) microscopy is of widespread use for observing live biological specimens. In fertility clinics the DIC microscope is used for evaluating human embryo cells. An image formation model for DIC imaging is needed for reconstruction and quantification of the visualized specimens. This calls for a complicated analysis of the interaction of light waves with biological matter. Most works express the solution via the first Born approximation, yet a theoretical bound is known that limits the validity of such approximation to very small objects. We show in this work that the theoretical bound is not directly relevant to microscopic imaging and is far too limiting. We derive a more realistic bound and show that it may justify in many cases the use of the Born approximation in biological cell microscopic imaging. It also provides limits on the validity of the Born expansion that several works violate.
A Symmetry-Based Generative Model for Shape
Authors: Nhon H. Trinh, Benjamin B. Kimia
Venue: ICCV
Year: 2007

We propose a novel generative language for shape that is based on the shock graph: given a shock graph topology, we explore constraints on the geometry and dynamics of the shock graph branches at each point required to generate a valid shape, i.e., with no self-intersection, cusps, or crossovers. We model the shape boundary as a piece-wise smooth circular arc spline, which is dense in the space of piecewise smooth curves. Using this model we derive an independent set of parameters which generate a variety of shapes and satisfy the reconstruction constraints. We show simple examples of using this generative model as an active deformable shape and for morphing between two shapes. The results illustrate that it is possible to generate any generic shape with relatively few parameters, further reduced if prior knowledge of shape is available.
Coupling CRFs and Deformable Models for 3D Medical Image Segmentation
Authors: Gabriel Tsechpenakis, Jianhua Wang 0002, Brandon Mayer, Dimitris N. Metaxas
Venue: ICCV
Year: 2007

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with optical coherence tomography.
Learn to Track Edges
Authors: Yanghai Tsin, Yakup Genc, Ying Zhu 0006, Visvanathan Ramesh
Venue: ICCV
Year: 2007

Reliability of a model-based edge tracker critically depends on its ability to establish correct correspondences between points on the model edges and edge pixels in an image. This is a non-trivial problem especially in the presence of large inter-frame motions and in cluttered environments. We propose an online learning approach to solving this problem. An edge pixel is represented by a descriptor composed of a small segment of intensity patterns. From training examples the algorithm utilizes the randomized forest model to learn a posteriori distribution of correspondence given the descriptor. In a new frame, the edge pixels are classified using maximum a posteriori (MAP) estimation. The proposed method is very powerful and it enables us to apply the proposed tracker to many previously impossible scenarios with unprecedented robustness.
Improved FFD B-Spline Image Registration
Authors: Nicholas J. Tustison, Brian B. Avants, James C. Gee
Venue: ICCV
Year: 2007

Due to their computational efficiency and other salient properties, B-splines form the basis not only in comprising the de facto standard for curve and surface representation but also for various nonrigid registration techniques frequently employed in medical image analysis. These registration techniques fall under the rubric of Free-Form Deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object represents the transformation of the registration. Representative, and often cited within the relevant community, of this class of techniques is the formulation of Rueckert et. al [7] who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained its essential characteristics since Rueckert's incarnation. We assert that such a straightforward gradient-learning is suboptimal in certain cases and to remedy this sub-optimality, we propose a fitting-based strategy for registration in the spirit of Thirion 's Demons [14] and directly manipulated free-form deformations [2], which takes advantage of our previously developed generalized B-spline fitting algorithm [17].
Locally Invariant Fractal Features for Statistical Texture Classification
Authors: Manik Varma, Rahul Garg 0001
Venue: ICCV
Year: 2007

We address the problem of developing discriminative, yet invariant, features for texture classification. Texture variations due to changes in scale are amongst the hardest to handle. One of the most successful methods of dealing with such variations is based on choosing interest points and selecting their characteristic scales [Lazebnik et al. PAMI 2005]. However, selecting a characteristic scale can be unstable for many textures. Furthermore, the reliance on an interest point detector and the inability to evaluate features densely can be serious limitations. Fractals present a mathematically well founded alternative to dealing with the problem of scale. However, they have not become popular as texture features due to their lack of discriminative power. This is primarily because: (a) fractal based classification methods have avoided statistical characterisations of textures (which is essential for accurate analysis) by using global features; and (b) fractal dimension features are unable to distinguish between key texture primitives such as edges, corners and uniform regions. In this paper, we overcome these drawbacks and develop local fractal features that are evaluated densely. The features are robust as they do not depend on choosing interest points or characteristic scales. Furthermore, it is shown that the local fractal dimension is invariant to local bi-Lipschitz transformations whereas its extension is able to correctly distinguish between fundamental texture primitives. Textures are characterised statistically by modelling the full joint PDF of these features. This allows us to develop a texture classification framework which is discriminative, robust and achieves state-of-the-art performance as compared to affine invariant and fractal based methods.
Multilinear Projection for Appearance-Based Recognition in the Tensor Framework
Authors: M. Alex O. Vasilescu, Demetri Terzopoulos
Venue: ICCV
Year: 2007

Numerical multilinear (tensor) algebra is a principled mathematical approach to disentangling and explicitly and parsimoniously representing the essential factors or modes of image formation, among them illumination, scene geometry, and imaging, thereby dramatically improving the performance of appearance-based recognition. Generalizing concepts from linear (matrix) algebra, we define the identity tensor and the pseudo-inverse tensor and we employ them to develop a multilinear projection algorithm, which is natural for performing recognition in the tensor algebraic framework. Our multilinear projection algorithm simultaneously projects an unlabeled test image into multiple constituent mode spaces spanned by learned, mode-specific basis sets in order to infer its mode labels. Multilinear projection is applied to unconstrained facial image recognition, where the mode labels are person identity, viewpoint, illumination, etc.
Boosting Invariance and Efficiency in Supervised Learning
Authors: Andrea Vedaldi, Paolo Favaro, Enrico Grisan
Venue: ICCV
Year: 2007

In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While one can incorporate invariance by adding virtual samples to the data (e.g., by jittering), we adopt a much more efficient strategy and work along the lines of vicinal risk minimization and tangent distance methods. As in vicinal risk minimization, we incorporate invariance to data by applying anisotropic smoothing along the directions of invariance. Moreover, as in tangent distance methods, we provide a simple local approximation to such directions, thus obtaining an efficient computational scheme. We also show that it is possible to automatically design optimal weak classifiers by using gradient descent. To increase efficiency at run time, such optimal weak classifiers are projected on a Haar basis. This results in designing strong classifiers that are more computationally efficient than in the case of exhaustive search. For illustration and validation purposes, we demonstrate the novel algorithm both on synthetic and on real data sets that are publicly available.
On Detecting Subtle Pathology via Tissue Clustering of Multi-parametric Data using Affinity Propagation
Authors: Ragini Verma, Peng Wang 0005
Venue: ICCV
Year: 2007

We propose a novel framework for tissue abnormality characterization in normal appearing brain tissue (NABT) that is progressively deteriorating, using affinity propagation applied to multi-parametric data created using a combination of magnetic resonance (MR) protocols. While traditional tissue segmentation and clustering can reveal clusters pertaining to healthy and diseased tissue easily, a complete characterization of the effect of pathology requires the study of heterogeneity of NABT. The problem is rendered challenging by the fact that there are no training samples available for such tissue and hence classification based techniques cannot be used and neither can traditional clustering techniques since the number of clusters are not known a priori. Our framework for the automated clustering of tissue types employs a combination of a) manifold learning, that determines the underlying non-linear structure and embeds it into a lower dimensional space and b) affinity propagation (AP), which is a novel clustering technique that combines model- and similarity- based clustering, to automatically obtain exemplar-based clustering. We also define a novel probabilistic clustering technique. The number of clusters associated with a tissue type is indicative of its heterogeneity. By computing the overlap of these clusters in each of the MR protocols, we obtain a measure of the degree of abnormality in a tissue type and the protocol most sensitive in providing that classification. This general framework is applied towards the characterization of NABT in patients with multiple sclerosis. Results demonstrate a greater heterogeneity in NABT surrounding the lesions along with a greater overlap between the NABT and lesion tissue.
The Best of Both Worlds: Combining 3D Deformable Models with Active Shape Models
Authors: Christian Vogler, Zhiguo Li, Atul Kanaujia, Siome Goldenstein, Dimitris N. Metaxas
Venue: ICCV
Year: 2007

Reliable 3D tracking is still a difficult task. Most parametrized 3D deformable models rely on the accurate extraction of image features for updating their parameters, and are prone to failures when the underlying feature distribution assumptions are invalid. Active Shape Models (ASMs), on the other hand, are based on learning, and thus require fewer reliable local image features than parametrized 3D models, but fail easily when they encounter a situation for which they were not trained. In this paper, we develop an integrated framework that combines the strengths of both 3D deformable models and ASMs. The 3D model governs the overall shape, orientation and location, and provides the basis for statistical inference on both the image features and the parameters. The ASMs, in contrast, provide the majority of reliable 2D image features over time, and aid in recovering from drift and total occlusions. The framework dynamically selects among different ASMs to compensate for large viewpoint changes due to head rotations. This integration allows the robust tracking effaces and the estimation of both their rigid and non- rigid motions. We demonstrate the strength of the framework in experiments that include automated 3D model fitting and facial expression tracking for a variety of applications, including sign language.