OpenRaiser/Intern-Atlas huggingface dataset:
Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction
Authors: Kai Ni 0001, Drew Steedly, Frank Dellaert
Venue: ICCV
Year: 2007

Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However, traditional bundle adjustment algorithms require a considerable amount of memory and computational resources. In this paper, we present an extremely efficient, inherently out-of-core bundle adjustment algorithm. We decouple the original problem into several submaps that have their own local coordinate systems and can be optimized in parallel. A key contribution to our algorithm is making as much progress towards optimizing the global non-linear cost function as possible using the fragments of the reconstruction that are currently in core memory. This allows us to converge with very few global sweeps (often only two) through the entire reconstruction. We present experimental results on large-scale 3D reconstruction datasets, both synthetic and real.
Discriminative Subsequence Mining for Action Classification
Authors: Sebastian Nowozin, Gökhan H. Bakir, Koji Tsuda
Venue: ICCV
Year: 2007

Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But this ordering information can contain important information about the action itself e.g. consider the sport disciplines of hurdle race and long jump, where the global temporal order of motions (running, jumping) is important to discriminate between the two. In this work we propose to use a sequential representation which retains this temporal order. Further, we introduce Discriminative Subsequence Mining to find optimal discriminative subsequence patterns. In combination with the LPBoost classifier, this amounts to simultaneously learning a classification function and performing feature selection in the space of all possible feature sequences. The resulting classifier linearly combines a small number of interpretable decision functions, each checking for the presence of a single discriminative pattern. The classifier is benchmarked on the KTH action classification data set and outperforms the best known results in the literature.
Interactive Left Ventricular Segmentation in Cardiac Images
Authors: Wendeson da Silva Oliveira, Ângela Vilhena Dias
Venue: ICCV
Year: 2007

This work presents a technique to perform segmentation of the left ventricle in images of the human myocardium. Segmentation is one of the first steps of the image analysis. Edge-based segmentation provides the detection of region boundaries, but the contours are only based on local computations, and is often sensitive to local variations in intensity, noise and physical artifacts. In interactive methods, problems like these fatigue the user and require many interventions at the contour delineation process. To reduce these problems, we propose the addition of two new features in the Live-Wire method: a region intensity and a proximity feature. Experimental results show that this alternative approach can achieve accurate edge localization and improved efficiency.
High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors
Authors: Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis
Venue: ICCV
Year: 2007

Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features. In this work we highlight effects pertinent to high-dimensional spaces that are significant for matching, yet have not been explicitly accounted for in previous work. In our approach, we require every nearest neighbor to be meaningful, that is, sufficiently close to a query feature such that it is an out-lier to a background feature distribution. We estimate the background feature distribution from the extended neighborhood of a query feature given by its k nearest neighbors. Based on the concept of meaningful nearest neighbors, we develop a novel high-dimensional feature matching method and evaluate its performance by conducting image matching on two challenging image data sets. A superior performance in terms of accuracy is shown in comparison to several state-of-the-art approaches. Additionally, to make search for k nearest neighbors more efficient, we develop a novel approximate nearest neighbors search method based on sparse coding with an overcomplete basis set that provides a ten-fold speed-up over an exhaustive search even for high dimensional spaces and retains excellent approximation to an exact nearest neighbors search.
Simultaneous Segmentation and 3D Reconstruction of Monocular Image Sequences
Authors: Kemal Egemen Ozden, Konrad Schindler, Luc Van Gool
Venue: ICCV
Year: 2007

When trying to extract 3D scene information and camera motion from an image sequence alone, it is often necessary to cope with independently moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a general and practical algorithm, which can handle long, realistic sequences, is still missing. In this paper, we identify the necessary parts of such an algorithm, highlight both unexplored theoretical issues and practical challenges, and propose solutions. Theoretical issues include proper handling of different situations, in which the number of independent motions changes: objects can enter the scene, objects previously moving together can split and follow independent trajectories, or independently moving objects can merge into one common motion. We derive model scoring criteria to handle these changes in the number of segments. A further theoretical issue is the resolution of the relative scale ambiguity between such changes. Practical issues include robust 3D reconstruction of freely moving foreground objects, which often have few and short feature tracks. The proposed framework simultaneously tracks features, groups them into rigidly moving segments, and reconstructs all segments in 3D. Such an online approach, as opposed to batch processing techniques, which first track features, and then perform segmentation and reconstruction, is vital in order to handle small foreground objects.
Implicit Meshing for Finite Element Methods using Levelsets
Authors: Théodore Papadopoulo, Sylvain Vallaghé
Venue: ICCV
Year: 2007

Finite Element methods (FEM) usually require a mesh to describe the geometric domain on which the computations are occuring. These meshes must have several properties: 1) they must approximate the geometrical domain accurately, 2) they must have good numerical properties, and 3) they must be small enough so that the computations take a reasonable amount of time. These goals are somewhat contradictory and in many cases such as biomedical images - and particularly in the case of the head -, even though the geometric domains can effectively be extracted, eg from Magnetic Resonance Images (MRI), the generation of such meshes is quite difficult. This paper describes a technique that bypasses this mesh generation step going directly from a description by levelsets of the interfaces separating the various domains to the matrix associated to the FEM method. Using the levelsets description is quite convenient as it is already used by many segmentation tools. The technique is illustrated on spherical and realistic geometries for the Electroencephalography (EEG) direct problem.
Automatic Cardiac View Classification of Echocardiogram
Authors: Jin Hyeong Park, Shaohua Kevin Zhou, Costas Simopoulos, Joanne Otsuki, Dorin Comaniciu
Venue: ICCV
Year: 2007

We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific knowledge, and 3) employing a multi-class Logit-boost algorithm. In our prototype system, we classify four standard cardiac views: apical four chamber and apical two chamber, parasternal long axis and parasternal short axis (at mid cavity). We achieve a classification accuracy over 96% both of training and test data sets and the system runs in a second in the environment of Pentium 4 PC with 3.4 GHz CPU and 1.5 G RAM.
Fast training and selection of Haar features using statistics in boosting-based face detection
Authors: Minh-Tri Pham, Tat-Jen Cham
Venue: ICCV
Year: 2007

Training a cascade-based face detector using boosting and Haar features is computationally expensive, often requiring weeks on single CPU machines. The bottleneck is at training and selecting Haar features for a single weak classifier, currently in minutes. Traditional techniques for training a weak classifier usually run in 0(NT log N), with N examples (approximately 10,000), and T features (approximately 40,000). We present a method to train a weak classifier in time 0(Nd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + T), where d is the number of pixels of the probed image sub-window (usually from 350 to 500), by using only the statistics of the weighted input data. Experimental results revealed a significantly reduced training time of a weak classifier to the order of seconds. In particular, this method suffers very minimal immerse in training time with very large increases in members of Haar features, enjoying a significant gain in accuracy, even with reduced training time.
Latent Model Clustering and Applications to Visual Recognition
Authors: Simon Polak, Amnon Shashua
Venue: ICCV
Year: 2007

We consider clustering situations in which the pairwise affinity between data points depends on a latent "context" variable. For example, when clustering features arising from multiple object classes the affinity value between two image features depends on the object class that generated those features. We show that clustering in the context of a latent variable can be represented as a special 3D hyper- graph and introduce an algorithm for obtaining the clusters. We use the latent clustering model for an unsupervised multiple object class recognition where feature fragments are shared among multiple clusters and those in turn are shared among multiple object classes.
Probabilistic Linear Discriminant Analysis for Inferences About Identity
Authors: Simon J. D. Prince, James H. Elder
Venue: ICCV
Year: 2007

Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.
Detection of Complex Vascular Structures using Polar Neighborhood Intensity Profile
Authors: Xiaoning Qian, Matthew P. Brennan, Donald P. Dione, Lawrence W. Dobrucki
Venue: ICCV
Year: 2007

Modern medical imaging techniques enable the acquisition of in-vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that, at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all but the most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder constraint. Instead, we extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile enabling us to detect vessels even near branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and MRA 3D animal vascular images, particularly close to vessel branching regions. This methodology is also applicable to the detection of other structures such as sheets with the appropriate choice of operators.
Objects in Context
Authors: Andrew Rabinovich, Andrea Vedaldi, Carolina Galleguillos, Eric Wiewiora, Serge J. Belongie
Venue: ICCV
Year: 2007

In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model. Using a conditional random field (CRF) framework, our approach maximizes object label agreement according to contextual relevance. We compare two sources of context: one learned from training data and another queried from Google Sets. The overall performance of the proposed framework is evaluated on the PASCAL and MSRC datasets. Our findings conclude that incorporating context into object categorization greatly improves categorization accuracy.
Fast Bilinear SfM with Side Information
Authors: Mahesh Ramachandran, Ashok Veeraraghavan, Rama Chellappa
Venue: ICCV
Year: 2007

We study the beneficial effect of side information on the Structure from Motion (SfM) estimation problem. The side information that we consider is measurement of a 'reference vector' and distance from fixed plane perpendicular to that reference vector. Firstly, we show that in the presence of this information, the SfM equations can be rewritten similar to a bilinear form in its unknowns. Secondly, we describe a fast iterative estimation procedure to recover the structure of both stationary scenes and moving objects that capitalizes on this information. We also provide a refinement procedure in order to tackle incomplete or noisy side information. We characterize the algorithm with respect to its reconstruction accuracy, memory requirements and stability. Finally, we describe two classes of commonly occurring real-world scenarios in which this algorithm will be effective: (a) presence of a dominant ground plane in the scene and (b) presence of an inertial measurement unit on board. Experiments using both real data and rigorous simulations show the efficacy of the algorithm.
Leveragingarchivalvideo for building face datasets
Authors: Deva Ramanan, Simon Baker, Sham M. Kakade
Venue: ICCV
Year: 2007

We introduce a semi-supervised method for building large, labeled datasets effaces by leveraging archival video. Specifically, we have implemented a system for labeling 11 years worth of archival footage from a television show. We have compiled a dataset of 611,770 faces, orders of magnitude larger than existing collections. It includes variation in appearance due to age, weight gain, changes in hairstyles, and other factors difficult to observe in smaller-scale collections. Face recognition in an uncontrolled setting can be difficult. We argue (and demonstrate) that there is much structure at varying timescales in the video data that make recognition much easier. At local time scales, one can use motion and tracking to group face images together - we may not know the identity, but we know a single label applies to all faces in a track. At medium time scales (say, within a scene), one can use appearance features such as hair and clothing to group tracks across shot boundaries. However, at longer timescales (say, across episodes), one can no longer use clothing as a cue. This suggests that one needs to carefully encode representations of appearance, depending on the timescale at which one intends to match. We assemble our final dataset by classifying groups of tracks in a nearest-neighbors framework. We use a face library obtained by labeling track clusters in a reference episode. We show that this classification is significantly easier when exploiting the hierarchical structure naturally present in the video sequences. From a data-collection point of view, tracking is vital because it adds non-frontal poses to our face collection. This is important because we know of no other method for collecting images of non-frontal faces "in the wild".
Symmetries of non-rigid shapes
Authors: Dan Raviv, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel
Venue: ICCV
Year: 2007

Symmetry and self-similarity is the cornerstone of Nature, exhibiting itself through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence of symmetry can often be an indication of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important in numerous practical applications, including crystallography, medical imaging, and face recognition, to mention a few. Conversely, the assumption of underlying shape symmetry can facilitate solutions to many problems in shape reconstruction and analysis. Traditionally, symmetries are described as extrinsic geometric properties of the shape. While being adequate for rigid shapes, such a description is inappropriate for non-rigid ones. Extrinsic symmetry can be broken as a result of shape deformations, while its intrinsic symmetry is preserved. In this paper, we pose the problem of finding intrinsic symmetries of non-rigid shapes and propose an efficient method for their computation.
Quadratic Markovian Probability Fields for Image Binary Segmentation
Authors: Mariano Rivera, Pedro Pablo Mayorga
Venue: ICCV
Year: 2007

We present a Markov random field model for image binary segmentation that computes the probability that each pixel belongs to a given class. We show that the computation of a real valued field has noticeable computational and performance advantages with respect to the computation of binary valued field; the proposed energy function is efficiently minimized with standard fast linear order algorithms as conjugate gradient or multigrid Gauss-Seidel schemes. By providing a good initial guesses as starting point we avoid to construct from scratch a new solution, accelerating the computational process, and allow us to naturally implement efficient multigrid algorithms. For applications with limited computational time, a good partial solution can be obtained by stopping the iterations even if the global optimum is not yet reached. We present a meticulous comparison with state of the art methods: graph cut, random walker and GMMF The algorithms' performance are compared using a cross-validation procedure and an automatics algorithm for learning the parameter set.
PR: More than Meets the Eye
Authors: Anderson Rocha 0001, Siome Goldenstein
Venue: ICCV
Year: 2007

In this paper, we introduce a new image descriptor for broad Image Categorization, the Progressive Randomization (PR) that uses perturbations on the values of the Least Significant Bits (LSB) of images. We show that different classes of images have a distinct behavior under our methodology and that using statistical descriptors of LSB occurrences and enough training examples, the method already performs as well or better than comparable existing techniques in the literature. With few training examples PR still has good separability and its accuracy increases with the size of the training set. We validate our method using four image databases with different categories.
3D Topology Preserving Flows for Viewpoint-Based Cortical Unfolding
Authors: Kelvin R. Rocha, Ganesh Sundaramoorthi, Anthony J. Yezzi
Venue: ICCV
Year: 2007

We present a variational method for unfolding of the cortex based on a user-chosen point of view as an alternative to more traditional global flattening methods, which incur more distortion around the region of interest. Our approach involves two novel contributions. The first is an energy function and its corresponding gradient flow to measure the average visibility of a region of interest of a surface from a given viewpoint. The second is an additional energy function and flow designed to preserve the 3D topology of the evolving surface. This latter contribution receives significant focus in this paper as it is crucial to obtain the desired unfolding effect derived from the first energy functional and flow. Without it, the resulting topology changes render the unconstrained evolution uninteresting for the purpose of cortical visualization, exploration, and inspection.
Steerable Random Fields
Authors: Stefan Roth 0001, Michael J. Black
Venue: ICCV
Year: 2007

In contrast to traditional Markov random field (MRF) models, we develop a steerable random field (SRF) in which the field potentials are defined in terms of filter responses that are steered to the local image structure. In particular, we use the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure, and analyze the statistics of these steered filter responses in natural images. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random field models with anisotropic regularization and provides a statistical motivation for the latter. We demonstrate that steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.
Using the Pn Potts model with learning methods to segment live cell images
Authors: Christopher Russell 0001, Dimitris N. Metaxas, Christophe Restif, Philip H. S. Torr
Venue: ICCV
Year: 2007

We present a segmentation method for live cell images, using graph cuts and learning methods. The images used here are particularly challenging because of the shared grey-level distributions of cells and background, which only differ by their textures, and the local imprecision around cell borders. We use the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> Potts model recently presented by Kohli et al. [9]: functions on higher-order cliques of pixels are included into the traditional Potts model, allowing us to account for local texture features, and to find the optimal solution efficiently. We use learning methods to define the potential functions used in the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> Potts model. We present the model and the learning methods we used, and compare our segmentation results with similar work in cytometry. While our method performs similarly, it requires little manual tuning and thus is straightforward to adapt to other images.