OpenRaiser/Intern-Atlas huggingface dataset:
Region-Based Segmentation via Non-Rigid Template Matching
Authors: Kinda Anna Saddi, Christophe Chefd&apos, Hotel, Mikaël Rousson, Farida Cheriet
Venue: ICCV
Year: 2007

We propose a new region segmentation method based on non-rigid template matching. We align a binary template to an image by maximizing the likelihood of intensity distributions within a region of interest and its background. The intensity model and the corresponding a posteriori distributions are estimated and updated throughout the alignment. The geometric deformation of the template is based on a fluid registration model. Unlike contour-based segmentation techniques, this registration framework allows for a global regularization of the template variations. This enables the segmentation of irregular shapes while avoiding leaks. We apply our method to the segmentation of the liver in computed tomography images, a challenging task due to the high inter-patient variability in the shape of this organ. We show that our segmentation results are equivalent or superior in accuracy to results obtained using existing techniques based on 3D shape models.
Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval
Authors: Hichem Sahbi, Jean-Yves Audibert, Renaud Keriven
Venue: ICCV
Year: 2007

Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of questions and answers (Q&A). The user's feedback to these questions provides a CBIR system with a partial labeling of the data and makes it possible to iteratively refine a decision rule on the unlabeled data. Training of this decision rule is referred to as transductive learning. This work is an original approach to relevance feedback (RF) based on graph-cuts. Training consists in implicitly modeling the manifold enclosing both the labeled and unlabeled dataset and finding a partition of this manifold using a min-cut. The contribution of this work is two-fold (i) this is the first comprehensive study of relevance feedback using graph cuts and (ii) our RF model exploits the structure of the data manifold by considering also the structure of the unlabeled data. Experiments conducted on generic as well as specific databases show that our graph-cut based approach is very effective, outperforms other existing methods and makes it possible to converge to almost all the images of the user's "class of interest" with a very small labeling effort. A demo is available through our image retrieval tool kit (IRTK).
Fuzzy classificationof brain MRI using a priori knowledge: weighted fuzzy C-means
Authors: Olivier Salvado, Pierrick Bourgeat, Oscar Acosta Tamayo, Maria A. Zuluaga, Sébastien Ourselin
Venue: ICCV
Year: 2007

We report in this communication a new formulation for the cost function of the well-known fuzzy C-means classification technique whereby we introduce weights. We derive the equations of this new weighted fuzzy C-means algorithm (WFCM) in the presence of additive and multiplicative bias field. We show that the weights can be designed in the same manner as prior probabilities commonly used in maximum a posteriori classifier (MAP) to introduce prior knowledge (e.g. using atlas), and increase robustness to noise (e.g. using Markov random field). Using prior probabilities of three popular MAP algorithms, we compare the performances of our proposed WFCM scheme using the simulated MRI T1W BrainWeb datasets, as well as five T1W MR patient scans. Our results show that WFCM achieves superior performances for low SNR conditions, whereas a Gaussian mixture model is desirable for high noise levels. WFCM allows rigorous comparison of fuzzy and probabilistic classifiers, and offers a framework where improvements can be shared between those two types of classifier.
Convex Optimization for Deformable Surface 3-D Tracking
Authors: Mathieu Salzmann, Richard I. Hartley, Pascal Fua
Venue: ICCV
Year: 2007

3-D shape recovery of non-rigid surfaces from 3-D to 2-D correspondences is an under-constrained problem that requires prior knowledge of the possible deformations. State-of-the-art solutions involve enforcing smoothness constraints that limit their applicability and prevent the recovery of sharply folding and creasing surfaces. Here, we propose a method that does not require such smoothness constraints. Instead, we represent surfaces as triangulated meshes and, assuming the pose in the first frame to be known, disallow large changes of edge orientation between consecutive frames, which is a generally applicable constraint when tracking surfaces in a 25 frames- per-second video sequence. We will show that tracking under these constraints can be formulated as a Second Order Cone Programming feasibility problem. This yields a convex optimization problem with stable solutions for a wide range of surfaces with very different physical properties.
Harvesting Image Databases from the Web
Authors: Florian Schroff, Antonio Criminisi, Andrew Zisserman
Venue: ICCV
Year: 2007

The objective of this work is to automatically generate a large number of images for a specified object class (for example, penguin). A multi-modal approach employing both text, meta data and visual features is used to gather many, high-quality images from the Web. Candidate images are obtained by a text based Web search querying on the object identifier (the word penguin). The Web pages and the images they contain are downloaded. The task is then to remove irrelevant images and re-rank the remainder. First, the images are re-ranked using a Bayes posterior estimator trained on the text surrounding the image and meta data features (such as the image alternative tag, image title tag, and image filename). No visual information is used at this stage. Second, the top-ranked images are used as (noisy) training data and a SVM visual classifier is learnt to improve the ranking further. The principal novelty is in combining text/meta-data and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals (e.g. camels, sharks, penguins), vehicles (cars, airplanes, bikes) and other classes (guitar, wristwatch), totalling 18 classes. The results are assessed by precision/recall curves on ground truth annotated data and by comparison to previous approaches including those of Berg et al. (on an additional six classes) and Fergus et al.
A Fast Method to Minimize L Error Norm for Geometric Vision Problems
Authors: Yongduek Seo, Richard I. Hartley
Venue: ICCV
Year: 2007

Minimizing L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> error norm for some geometric vision problems provides global optimization using the well- developed algorithm called SOCP (second order cone programming). Because the error norm belongs to quasi- convex functions, bisection method is utilized to attain the global optimum. It tests the feasibility of the intersection of all the second order cones due to measurements, repeatedly adjusting the global error level. The computation time increases according to the size of measurement data since the number of second order cones for the feasibility test inflates correspondingly. We observe in this paper that not all the data need be included for the feasibility test because we minimize the maximum of the errors; we may use only a subset of the measurements to obtain the optimal estimate, and therefore we obtain a decreased computation time. In addition, by using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> image error instead of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Euclidean distance, we show that the problem is still a quasi-convex problem and can be solved by bisection method but with linear programming (LP). Our algorithm and experimental results are provided.
Gradient Intensity-Based Registration of Multi-Modal Images of the Brain
Authors: Ramtin Shams, Rodney A. Kennedy, Parastoo Sadeghi, Richard I. Hartley
Venue: ICCV
Year: 2007

We present a fast and accurate framework for registration of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial information in the form of 'gradient intensity'. We introduce gradient intensity as a measure of spatial strength of an image in a given direction and show that it can be used to determine the rotational misalignment independent of translation between the images. The rotation parameters are obtained by maximizing the mutual information of 2D gradient intensity matrices obtained from 3D images, hence reducing the dimensionality of the problem and improving efficiency. The rotation parameters along with estimations of translation are then used to initialize an optimization step over a conventional pixel intensity-based method to achieve sub-voxel accuracy. Our optimization algorithm converges quickly and is less subject to the common problem of misregistration due to local extrema. Experiments show that our method significantly improves the robustness, performance and efficiency of registration compared to conventional pixel intensity-based methods.
Integrating Appearance and Motion Cues for Simultaneous Detection and Segmentation of Pedestrians
Authors: Vinay Sharma, James W. Davis
Venue: ICCV
Year: 2007

We present a unified method for simultaneously acquiring both the location and the silhouette shape of people in outdoor scenes. The proposed algorithm integrates top-down and bottom-up processes in a balanced manner, employing both appearance and motion cues at different perceptual levels. Without requiring manually segmented training data, the algorithm employs a simple top-down procedure to capture the high-level cue of object familiarity. Motivated by regularities in the shape and motion characteristics of humans, interactions among low-level contour features are exploited to extract mid-level perceptual cues such as smooth continuation, common fate, and closure. A Markov random field formulation is presented that effectively combines the various cues from the top-down and bottom-up processes. The algorithm is extensively evaluated on static and moving pedestrian datasets for both detection and segmentation.
Limited view CT reconstruction via constrained metric labeling
Authors: Vikas Singh, Petru M. Dinu, Lopamudra Mukherjee, Jinhui Xu 0001, Kenneth R. Hoffmann
Venue: ICCV
Year: 2007

This paper proposes an new optimization framework for tomographic reconstruction of 3D volumes when only a limited number of projection views are available. The problem has several important clinical applications spanning coronary angiographic imaging, breast tomosynthesis and dental imaging. We first show that the limited view reconstruction problem can be formulated as a "constrained" version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings together metric labeling classification and classical algebraic tomographic reconstruction (ART) in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients, given a regular limited view reconstruction as an input, we can view it as a "denoising" task - where voxels must be reassigned subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct volumes with several multiple contrast objects as well as the simpler binary contrast case which can be solved near-optimally in practice. We present evaluations on cone bean computed tomography, it can also be readily extended to other tomographic modalities as a viable approach for limited-view tomographic reconstruction.
Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh
Authors: Sudipta N. Sinha, Philippos Mordohai, Marc Pollefeys
Venue: ICCV
Year: 2007

We formulate multi-view 3D shape reconstruction as the computation of a minimum cut on the dual graph of a semi- regular, multi-resolution, tetrahedral mesh. Our method does not assume that the surface lies within a finite band around the visual hull or any other base surface. Instead, it uses photo-consistency to guide the adaptive subdivision of a coarse mesh of the bounding volume. This generates a multi-resolution volumetric mesh that is densely tesselated in the parts likely to contain the unknown surface. The graph-cut on the dual graph of this tetrahedral mesh produces a minimum cut corresponding to a triangulated surface that minimizes a global surface cost functional. Our method makes no assumptions about topology and can recover deep concavities when enough cameras observe them. Our formulation also allows silhouette constraints to be enforced during the graph-cut step to counter its inherent bias for producing minimal surfaces. Local shape refinement via surface deformation is used to recover details in the reconstructed surface. Reconstructions of the Multi- View Stereo Evaluation benchmark datasets and other real datasets show the effectiveness of our method.
A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm
Authors: Ali Kemal Sinop, Leo J. Grady
Venue: ICCV
Year: 2007

In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases - The Graph Cuts and the Random Walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> or an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm. Here, we explore the segmentation algorithm defined by an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the Graph Cuts or Random Walker methods.
Uninitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation The Opposing Metrics Method
Authors: Ali Kemal Sinop, Leo J. Grady
Venue: ICCV
Year: 2007

We present a new approach for the incorporation of shape information into a segmentation algorithm. Unlike previous approaches to the problem, our method requires no initialization, is non-iterative and finds a steady-state (i.e., global optimum) solution. In the present work, we are specifically focused on the segmentation of rectilinear shapes. The key idea is to use the fact that certain shape classes optimize the ratio of specific metrics, which can be expressed as graph Laplacian matrices applied to indicator vectors. We show that a relaxation of the binary formulation of this problem allows a global solution via generalized eigenvectors. The approach is tested on both synthetic examples and natural images.
Multi-start Method with Prior Learning for Image Registration
Authors: Gang Song, Brian B. Avants, James C. Gee
Venue: ICCV
Year: 2007

We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite- time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial search position. While global optimization methods can resolve this problem, they are are often very time consuming. Our goal is to build an explicit prior model of the gap between a typical registration solution and the solution gained by a global optimization method. We use this learned prior model to restrict randomized search in the relevant parameter space surrounding the initial solution. Global optimization in this restricted parameter space provides, in finite time, results that are superior to both gradient descent and the general multi-start strategy. The performance of our method is illustrated on a data set of 67 elderly and neurodegenerative brains. Our novel learning strategy and the associated registration method are shown to outperform other approaches. Theoretical, synthetic and real-world examples illustrate this improvement.
Stochastic Adaptive Tracking In A Camera Network
Authors: Bi Song, Amit K. Roy-Chowdhury
Venue: ICCV
Year: 2007

We present a novel stochastic, adaptive strategy for tracking multiple people in a large network of video cameras. Similarities between features (appearance and biometrics) observed at different cameras are continuously adapted and the stochastically optimal path for each person computed. The following are the major contributions of the proposed approach. First, we consider situations where the feature similarities are uncertain and treat them as random variables. We show how the distributions of these random variables can be learned and how to compute the tracks in a stochastically optimal manner. Second, we consider the possibility of long-term interdependence of the features over space and time. This allows us to adoptively evolve the feature correspondences by observing the system performance over a time window, and correct for errors in the similarity computations. Third, we show that the above two conditions can be addressed by treating the issue of tracking in a camera network as an optimization problem in a stochastic adaptive system. We show results on data collected by a large camera network. The proposed approach is particularly suitable for distributed processing over the entire network.
Video-based Face Recognition on Real-World Data
Authors: Johannes Stallkamp, Hazim Kemal Ekenel, Rainer Stiefelhagen
Venue: ICCV
Year: 2007

In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application scenario poses many challenges. Continuous, uncontrolled variations of facial appearance due to illumination, pose, expression, and occlusion need to be handled to allow for successful recognition. Faces are classified by a local appearance-based face recognition algorithm. The obtained confidence scores from each classification are progressively combined to provide the identity estimate of the entire sequence. We introduce three different measures to weight the contribution of each individual frame to the overall classification decision. They are distance- to-model (DTM), distance-to-second-closest (DT2ND), and their combination. Both a k-nearest neighbor approach and a set of Gaussian mixtures are evaluated to produce individual frame scores. We have conducted closed set and open set identification experiments on a database of 41 subjects. The experimental results show that the proposed system is able to reach high correct recognition rates in a difficult scenario.
How Good are Local Features for Classes of Geometric Objects
Authors: Michael Stark 0003, Bernt Schiele
Venue: ICCV
Year: 2007

Recent work in object categorization often uses local image descriptors such as SIFT to learn and detect object categories. Such descriptors explicitly code local appearance and have shown impressive results on objects with sufficient local appearance statistics. However, many important object classes such as tools, cups and other man-made artifacts seem to require features that capture the respective shape and geometric layout of those object classes. Therefore this paper compares, on a novel data collection of 10 geometric object classes, various shape-based features with appearance-based descriptors such as SIFT. The analysis includes a direct comparison of feature statistics as well as results within standard recognition frameworks, which are partly intuitive, but sometimes surprising.
Learning to Find Object Boundaries Using Motion Cues
Authors: Andrew N. Stein, Derek Hoiem, Martial Hebert
Venue: ICCV
Year: 2007

While great strides have been made in detecting and localizing specific objects in natural images, the bottom-up segmentation of unknown, generic objects remains a difficult challenge. We believe that occlusion can provide a strong cue for object segmentation and "pop-out", but detecting an object's occlusion boundaries using appearance alone is a difficult problem in itself. If the camera or the scene is moving, however, that motion provides an additional powerful indicator of occlusion. Thus, we use standard appearance cues (e.g. brightness/color gradient) in addition to motion cues that capture subtle differences in the relative surface motion (i.e. parallax) on either side of an occlusion boundary. We describe a learned local classifier and global inference approach which provide a frame-work for combining and reasoning about these appearance and motion cues to estimate which region boundaries of an initial over-segmentation correspond to object/occlusion boundaries in the scene. Through results on a dataset which contains short videos with labeled boundaries, we demonstrate the effectiveness of motion cues for this task.
Soylent Grid: it's Made of People
Authors: Stephan Steinbach, Vincent C. Rabaud, Serge J. Belongie
Venue: ICCV
Year: 2007

The ground truth labeling of an image dataset is a task that often requires a large amount of human time and labor. We present an infrastructure for distributed human labeling that can exploit the modularity of common vision problems involving segmentation and recognition. We present the different elements of this infrastructure in detail, in particular the different vision human computational tasks (HCTs) and machine computable tasks (MCTs). We also discuss the impact of such a system on Internet security vs. the current state of the art. Finally, we present our prototype implementation of such a system, named soylent grid, on typical problems.
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition
Authors: Yu Su 0010, Shiguang Shan, Xilin Chen 0001, Wen Gao 0001
Venue: ICCV
Year: 2007

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.