OpenRaiser/Intern-Atlas huggingface dataset:
Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
Authors: Manmohan Krishna Chandraker, Sameer Agarwal 0001, David J. Kriegman, Serge J. Belongie
Venue: ICCV
Year: 2007

We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm upgrades it to affine by estimating the position of the plane at infinity. The plane at infinity is computed by globally minimizing a least squares formulation of the modulus constraints. In the second stage, the algorithm upgrades this affine reconstruction to a metric one by globally minimizing the infinite homography relation to compute the dual image of the absolute conic (DIAC). The positive semidefiniteness of the DIAC is explicitly enforced as part of the optimization process, rather than as a post-processing step. For each stage, we construct and minimize tight convex relaxations of the highly non-convex objective functions in a branch and bound optimization framework. We exploit the problem structure to restrict the search space for the DIAC and the plane at infinity to a small, fixed number of branching dimensions, independent of the number of views. Experimental evidence of the accuracy, speed and scalability of our algorithm is presented on synthetic and real data. MATLAB code for the implementation is made available to the community.
Random Walk and Front Propagation on Watershed Adjacency Graphs for Multilabel Image Segmentation
Authors: Christophe Chefd&apos, Hotel, Alexis Sebbane
Venue: ICCV
Year: 2007

The watershed partition of an image often results in over-segmentation. This well-known phenomenon is due to variations of intensity that do not correspond to object boundaries and produce spurious local minima in the image gradient magnitude. Filtering minima or merging watershed regions is then necessary to obtain a higher-level description of the data. In this paper, we propose new solutions to this problem by applying two interactive multilabel partitioning techniques to the adjacency graph of the watershed regions. In our first approach, the partition is derived from the probability that a "random walker" starting at an arbitrary node, first reaches a node with a pre-assigned label. In the second approach, we compute a geodesic partition of the graph using competing wavefronts starting at prescribed nodes. Both methods are based on existing segmentation techniques previously implemented on image lattices. Using a watershed adjacency graph greatly reduces their memory footprint and computational cost. We demonstrate the practicality and versatility of this approach with several experiments on 2D and 3D datasets.
A Multi-Image Restoration Method for Image Reconstruction from Projections
Authors: Yunqiang Chen, Lin Cheng, Tong Fang, Rainer Raupach
Venue: ICCV
Year: 2007

Traditional Bayesian restoration methods depend heavily on the accuracy of underlying generative models. For the challenging streak noise generated in the procedure of reconstruction from projections, Bayesian methods do not generalize well because accurate signal/noise models are not readily available. In this paper, we reformulate the reconstruction problem into a multi-image based restoration task and demonstrate that multiple images and mutual independence analysis can be utilized to significantly improve the generalization capability of traditional Bayesian frameworks in challenging scenarios. An efficient mutual independence analysis term is designed based on the properties of independent random variables to enforce the independent noise constraint between multiple images in an energy optimization framework, which can effectively detect and correct restoration error due to inaccurate generative models. Quantitative comparisons on phantom image and experiments on clinical scans both show significant improvements in accuracy and robustness of the proposed method.
Optimizing Image Registration by Mutually Exclusive Scale Components
Authors: Terrence Chen, Thomas S. Huang
Venue: ICCV
Year: 2007

Local optimum has been one of the most difficult problems in image registration notwithstanding the extensive research effort that has been put into solving it. Local optimums occur when a portion of patterns in the floating image coincide with a portion of patterns in the reference image even though the two images are not entirely matched. Existing hierarchical or multi-scale methods suffer from this problem mainly because some redundant information that causes local optimums appears in multiple scales. We propose to avoid it by decomposing an image into several mutually exclusive scale components so that minimal redundant information is present. Our method is evaluated and compared with existing methods using high resolution satellite imagery where thousands of local optimum traps are hidden. We show that our method has significant improvement over existing solutions in both robustness and efficiency.
A Divide-and-Conquer Approach to 3D Object Reconstruction from Line Drawings
Authors: Yu Chen 0009, Jianzhuang Liu, Xiaoou Tang
Venue: ICCV
Year: 2007

3D object reconstruction from a single 2D line drawing is an important problem in both computer vision and graphics. Many methods have been put forward to solve this problem, but they usually fail when the geometric structure of a 3D object becomes complex. In this paper, a novel approach based on a divide-and-conquer strategy is proposed to handle 3D reconstruction of complex manifold objects from single 2D line drawings. The approach consists of three steps: 1) dividing a complex line drawing into multiple simpler line drawings based on the result efface identification; 2) reconstructing the 3D shapes from these simpler line drawings; and 3) merging the 3D shapes into one complete object represented by the original line drawing. A number of examples are given to show that our approach can handle 3D reconstruction of more complex objects than previous methods.
Contrast Enhancement from Multiple Panoramic Images
Authors: Irene Cheng 0001, Anup Basu
Venue: ICCV
Year: 2007

In this work we discuss an efficient strategy for combining multiple panoramic scans of the same scene to create a single higher contrast image. Prior research papers mainly consider that precise exposure times for multiple images are known and create a new high dynamic range representation for each pixel. We simply consider multiple scans of the same scene where the exposure times are not known, and no special filters or image detectors are used in the image acquisition process. In this unrestricted scenario, the problem is how to select different parts of a scene from the "best" image among a collection of images in order to optimize the overall clarity or contrast of a single final image. Experimental results are shown, and steps to improve results using modified contrast measures for color images are described. The results are enhanced using morphological filtering and image blending.
3-D Metric Reconstruction and Registration of Images of Near-planar Surfaces
Authors: Tae Eun Choe, Gérard G. Medioni
Venue: ICCV
Year: 2007

In this study, we address the problem of 3-D dense metric reconstruction and registration from multiple images, given that the observed surface is nearly planar. This is difficult, as classical methods work well only if the scene is truly planar (mosaicing) or the scene has certain significant depth variations (classical Structure-from- Motion (SfM)). One domain in which this problem occurs is image analysis of the retinal fundus. Our approach is to first assume planarity, and perform 2-D global registration. A first bundle adjustment is applied to find the camera positions in metric space. We then select two images and compute the epipolar geometry between them using plane+parallax approach. These images are matched to generate a dense disparity map using mutual information. A second bundle adjustment is applied to transform the disparity map into a dense metric depth map, fixing the 2 camera positions. A third bundle adjustment is performed to refine both camera positions and a 3-D structure. All images are back-projected to the 3-D structure for the final registration. The entire process is fully automatic. In addition, a clear definition of "near-planarity " is provided. 3-D reconstruction is shown visually. The method is general, and can be applied to other domains, as shown in the experiments.
Computing the Alpha-Channel with Probabilistic Segmentation for Image Colorization
Authors: Oscar Dalmau Cedeño, Mariano Rivera, Pedro Pablo Mayorga
Venue: ICCV
Year: 2007

We propose a gray scale image colorization method based on a Bayesian segmentation framework in which the classes are established from scribbles made by a user on the image. These scribbles can be considered as a multimap (multilabels map) that defines the boundary conditions of a probability measure field to be computed in each pixel. The components of such a probability measure field express the degree of belonging of each pixel to spatially smooth classes. In a first step we obtain the probability measure field by computing the global minima of a positive definite quadratic cost function with linear constraints. Then color is introduced in a second step through a pixelwise operation. The computed probabilities (memberships) are used for defining the weights of a simple linear combination of user provided colors associated to each class. An advantage of our method is that it allows us to re-colorize part or the whole image in an easy way, without need of recomputing the memberships (or /sp alpha/-channels).
Measuring Cortical Thickness Using An Image Domain Local Surface Model And Topology Preserving Segmentation
Authors: Sandhitsu R. Das, Brian B. Avants, Murray Grossman, James C. Gee
Venue: ICCV
Year: 2007

We present a measure of gray matter (GM) thickness based on local surface models in the image domain. Thickness is measured by integrating GM probability maps along the white matter (WM) surface normal direction. The method is simple to implement and allows statistical tests to be performed in the gray matter volume. A novel topology preserving segmentation method is introduced that is able to accurately recover GM in deep sulci. We apply this methodology to a longitudinal study of gray matter atrophy in a patient cohort diagnosed with frontotemporal dementia (FTD) spectrum disorders. Following image-based normalization of GM thickness maps, results show significant reduction in cortical thickness in several Brodmann areas spanning temporal, parietal and frontal lobes across subjects.
Population Shape Regression From Random Design Data
Authors: Bradley C. Davis, P. Thomas Fletcher, Elizabeth Bullitt, Sarang C. Joshi
Venue: ICCV
Year: 2007

Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. When the underlying process can be modeled by parameters in a Euclidean space, classical regression techniques are applicable and have been studied extensively. However, recent work suggests that attempts to describe anatomical shapes using flat Euclidean spaces undermines our ability to represent natural biological variability. In this paper we develop a method for regression analysis of general, manifold-valued data. Specifically, we extend Nadaraya-Watson kernel regression by recasting the regression problem in terms of Frechet expectation. Although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data. We demonstrate our method by analyzing shape change in the brain from a random design dataset of MR images of 89 healthy adults ranging in age from 22 to 79 years. To study the small scale changes in anatomy, we use the infinite dimensional manifold of diffeomorphic transformations, with an associated metric. We regress a representative anatomical shape, as a function of age, from this population.
Nonrigid Intraoperative Cortical Surface Tracking Using Game Theory
Authors: Christine DeLorenzo, Xenophon Papademetris, Lawrence H. Staib, Kenneth P. Vives, Dennis D. Spencer, James S. Duncan
Venue: ICCV
Year: 2007

During neurosurgery, nonrigid brain deformation prevents preoperatively acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration and surface deformation, a framework is needed which can solve for disparate and often competing variables. Game theory, which was developed specifically to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, we apply game theory to cortical surface tracking and use it to infer information about the physical processes of brain deformation and image acquisition.
Signals on Pencils of Lines
Authors: Justin Domke, Yiannis Aloimonos
Venue: ICCV
Year: 2007

This paper proposes the "epipolar pencil transformation" (EPT). This is a tool for comparing the signals in different images, with no use of feature detection, yet taking advantage of the constraints given by epipolar geometry. The idea is to develop a descriptor for each point, summarizing the signals on the pencil of lines intersecting at that point. To compute the EPT, first find compact descriptors for each line, then combine these appropriately for each pencil. Given the EPT for two images, computing the epipolar geometry reduces to a closest pairs problem- select one pencil from each set such that the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> distance (in descriptor space) is minimized. By this reduction to a high dimensional closest pairs problem, recent advances in computational geometry can be used to efficiently identify the best global solution. This technique is robust, as each potential solution is evaluated by comparing the signals for all the lines passing through the two hypothesized epipoles. At the same time, as the closest pairs algorithm performs a global search, the solution is not distracted by local minima. The EPT is used here both for the problem of two-view rigid motion, and many-view place recognition.
Fast Crowd Segmentation Using Shape Indexing
Authors: Lan Dong, Vasu Parameswaran, Visvanathan Ramesh, Imad Zoghlami
Venue: ICCV
Year: 2007

This paper presents a fast, accurate, and novel method for the problem of estimating the number of humans and their positions from background differenced images obtained from a single camera where inter-human occlusion is significant. The problem is challenging firstly because the state space formed by the number, positions, and articulations of people is large. Secondly, in spite of many advances in background maintenance and change detection, background differencing remains a noisy and imprecise process, and its output is far from ideal: holes, fill-ins, irregular boundaries etc. pose additional challenges for our "mid- level" problem of segmenting it to localize humans. We propose a novel example-based algorithm which maps the global shape feature by Fourier descriptors to various configurations of humans directly. We use locally weighted averaging to interpolate for the best possible candidate configuration. The inherent ambiguity resulting from the lack of depth and layer information in the background difference images is mitigated by the use of dynamic programming, which finds the trajectory in state space that best explains the evolution of the projected shapes. The key components of our solution are simple and fast. We demonstrate the accuracy and speed of our approach on real image sequences.
Relative Epipolar Motion of Tracked Features for Correspondence in Binocular Stereo
Authors: Hao Du 0004, Danping Zou, Yan Qiu Chen
Venue: ICCV
Year: 2007

Most 3D reconstruction solutions focus on surfaces, and there has not been much research attention paid to the problem of reconstructing 3D scenes made up of large numbers of particles, while the ability to reconstruct such dynamic scenes is potentially very useful in many areas such as colony behavior research and visual modeling. This paper proposes an approach - relative epipolar motion (REM) - towards solving the correspondence problem in stereopsis by utilizing the motion clue. It matches feature trajectories instead of the features themselves as used by existing methods. The proposed method has the following new capabilities: (1) it supports reconstructing dynamic 3D scenes of large number of undistinguishable drifting particles; (2) It is applicable to correspondence establishment for dynamic surfaces made up of repetitive textures; (3) It offers an alternative way to project structured light in active mode for deforming surface reconstruction. Experiment results on both simulated and real-world scenes demonstrate its effectiveness.
Efficient Generic Calibration Method for General Cameras with Single Centre of Projection
Authors: Aubrey K. Dunne, John Mallon, Paul F. Whelan
Venue: ICCV
Year: 2007

Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method.
Omnidirectional Cameras as Backing-Up Aid
Authors: Tobias Ehlgen, Markus Thorn, Markus Glaser
Venue: ICCV
Year: 2007

Omnidirectional cameras are well suited for maneuvering tasks due to to their large field of view. We mounted two catadioptric cameras on a vehicle and provide the driver with a bird's-eye view of the surrounding area behind the vehicle as well as the area on the left and right hand side. In order to enlarge the field of view of the bird's-eye view image, we will show an extension to the bird's-eye view image that provides a larger field of view and thus simplifies challenging maneuvering tasks. An overlay onto the resulting image shows the motion path of the vehicle while reversing. This motion path is connected to the steering angle. The driver can easily see where the vehicle will move when the steering angle is not changed throughout the movement.
A Variational Approach for Combined Segmentation and Estimation of Respiratory Motion in Temporal Image Sequences
Authors: Jan Ehrhardt, Alexander Schmidt-Richberg, Heinz Handels
Venue: ICCV
Year: 2007

In this paper a variational approach for the combined segmentation and registration of temporal image sequences is presented. The purpose of the proposed method is to estimate respiratory-induced organ motion in temporal CT image sequences and to segment a structure of interest simultaneously. In this model the segmentation of all images in the sequences is obtained by finding a non-linear registration to an initial segmentation in a reference image. A dense non-linear displacement field is estimated using image intensities and segmentation information in the images. Both problems (registration and segmentation) are formulated in a joint variational approach and solved simultaneously. A validation of the combined registration and segmentation approach is presented and demonstrates that the simultaneous solution of both problems improves the segmentation performance over a sequential application of the registration and segmentation steps.
Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints
Authors: Anders P. Eriksson, Carl Olsson, Fredrik Kahl
Venue: ICCV
Year: 2007

Indisputably Normalized Cuts is one of the most popular segmentation algorithms in computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these suggested methods is that they are noticeably limited and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle all types of linear equality constraints for an arbitrary number of classes. This is done by restating the problem and showing how linear constraints can be enforced exactly through duality. This allows us to add group priors, for example, that certain pixels should belong to a given class. In addition, it provides a principled way to perform multi-class segmentation for tasks like interactive segmentation. The method has been tested on real data with convincing results.
Shape Priors using Manifold Learning Techniques
Authors: Patrick Etyngier, Florent Ségonne, Renaud Keriven
Venue: ICCV
Year: 2007

We introduce a non-linear shape prior for the de- formable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category of shapes as a finite dimensional manifold which we approximate using Diffusion maps, that we call the shape prior manifold. Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its Nystrom extension. Our contribution lies in three aspects. First, we propose a solution to the pre-image problem and define the projection of a shape onto the manifold. Based on closest neighbors for the Diffusion distance, we then describe a variational framework for manifold denoising. Finally, we introduce a shape prior term for the deformable framework through a non-linear energy term designed to attract a shape towards the manifold at given constant embedding. Results on shapes of cars and ventricule nuclei are presented and demonstrate the potentials of our method.