OpenRaiser/Intern-Atlas huggingface dataset:
A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure
Authors: Shaohua Kevin Zhou, Feng Guo, Gustavo Carneiro 0001, John Jackson, M. Brendel, Costas Simopoulos, Joanne Otsuki, Dorin Comaniciu
Venue: ICCV
Year: 2007

We propose a probabilistic, hierarchical, and discriminant (PHD) framework for fast and accurate detection of deformable anatomic structures from medical images. The PHD framework has three characteristics. First, it integrates distinctive primitives of the anatomic structures at global, segmental, and landmark levels in a probabilistic manner. Second, since the configuration of the anatomic structures lies in a high-dimensional parameter space, it seeks the best configuration via a hierarchical evaluation of the detection probability that quickly prunes the search space. Finally, to separate the primitive from the background, it adopts a discriminative boosting learning implementation. We apply the PHD framework for accurately detecting various deformable anatomic structures from M- mode and Doppler echocardiograms in about a second.
Ten-fold Improvement in Visual Odometry Using Landmark Matching
Authors: Zhiwei Zhu, Taragay Oskiper, Supun Samarasekera, Rakesh Kumar 0001, Harpreet S. Sawhney
Venue: ICCV
Year: 2007

Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction. Traditional visual odometry systems drift over time as the frame-to-frame errors accumulate. In this paper, we propose to improve visual odometry using visual landmarks in the scene. First, a dynamic local landmark tracking technique is proposed to track a set of local landmarks across image frames and select an optimal set of tracked local landmarks for pose computation. As a result, the error associated with each pose computation is minimized to reduce the drift significantly. Second, a global landmark based drift correction technique is proposed to recognize previously visited locations and use them to correct drift accumulated during motion. At each visited location along the route, a set of distinctive visual landmarks is automatically extracted and inserted into a landmark database dynamically. We integrate the landmark based approach into a navigation system with 2 stereo pairs and a low-cost inertial measurement unit (IMU) for increased robustness. We demonstrate that a real-time visual odometry system using local and global landmarks can precisely locate a user within 1 meter over 1000 meters in unknown indoor/outdoor environments with challenging situations such as climbing stairs, opening doors, moving foreground objects etc..
Efficient Silhouette Extraction with Dynamic Viewpoint
Authors: Yueting Zhuang, Cheng Chen 0023
Venue: ICCV
Year: 2007

A novel approach is proposed that extends the classical background subtraction method to extract silhouettes from videos in real time with dynamic viewpoint variation caused by camera movement. First, manifold learning is used to model the background under viewpoint variations. Then, for each new frame, the background image corresponding to the same viewpoint is synthesized on the fly by examining the local neighborhood on the manifold, and the silhouette is extracted via background subtraction. An extension is also presented to generate stabilized silhouettes at any fixed viewpoint within the training range. Experiments show that our approach can efficiently extract accurate silhouettes in complex situations while maintaining a low noise level.
A Robust Algorithm for Fiber-Bundle Atlas Construction
Authors: Ulas Ziyan, Mert R. Sabuncu, W. Eric L. Grimson, Carl-Fredrik Westin
Venue: ICCV
Year: 2007

In this paper we demonstrate an integrated registration and clustering algorithm to compute an atlas of fiber-bundles from a set of multi-subject diffusion weighted MR images. We formulate a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, the algorithm employs an outlier rejection and denoising strategy to produce sharp probabilistic maps of certain bundles of interest. This map is potentially useful for making diffusion measurements in a common coordinate system to identify pathology related changes or developmental trends.
Building Rome in a day
Authors: Sameer Agarwal 0001, Noah Snavely, Ian Simon, Steven M. Seitz, Richard Szeliski
Venue: ICCV
Year: 2009

We present a system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo sharing sites. Our system uses a collection of novel parallel distributed matching and reconstruction algorithms, designed to maximize parallelism at each stage in the pipeline and minimize serialization bottlenecks. It is designed to scale gracefully with both the size of the problem and the amount of available computation. We have experimented with a variety of alternative algorithms at each stage of the pipeline and report on which ones work best in a parallel computing environment. Our experimental results demonstrate that it is now possible to reconstruct cities consisting of 150 K images in less than a day on a cluster with 500 compute cores.
Patch-based within-object classification
Authors: Jania Aghajanian, Jonathan Warrell, Simon J. D. Prince, Peng Li, Jennifer L. Rohn, Buzz Baum
Venue: ICCV
Year: 2009

Advances in object detection have made it possible to collect large databases of certain objects. In this paper we exploit these datasets for within-object classification. For example, we classify gender in face images, pose in pedestrian images and phenotype in cell images. Previous work has mainly targeted the above tasks individually using object specific representations. Here, we propose a general Bayesian framework for within-object classification. Images are represented as a regular grid of non-overlapping patches. In training, these patches are approximated by a predefined library. In inference, the choice of approximating patch determines the classification decision. We propose a Bayesian framework in which we marginalize over the patch frequency parameters to provide a posterior probability for the class. We test our algorithm on several challenging “real world” databases.
Structure and kinematics triangulation with a rolling shutter stereo rig
Authors: Omar Ait-Aider, François Berry
Venue: ICCV
Year: 2009

We describe a spatio-temporal triangulation method to be used with rolling shutter cameras. We show how a single pair of rolling shutter images enables the computation of both structure and motion of rigid moving objects. Starting from a set of point correspondences in the left and right images, we introduce the velocity and shutter characteristics in the triangulation equations. This results in a non-linear error criterion whose minimization in the least square sense provides the shape and velocity parameters. Unlike previous work on rolling shutter cameras, the constraining assumption of a-priori knowledge about the object geometry is removed and a full 3D motion model is considered. The aim of this work is thus to make the use of rolling shutter cameras of a broader interest. Experimental evaluation results confirm the feasibility of the approach.
Matching as a non-cooperative game
Authors: Andrea Albarelli, Samuel Rota Bulò, Andrea Torsello, Marcello Pelillo
Venue: ICCV
Year: 2009

With this paper we offer a game-theoretic perspective for the all-pervasive matching problem in computer vision. Specifically, we formulate the matching problem as a (population) non-cooperative game where the potential associations between the items to be matched correspond to (pure) strategies, while payoffs reflect the degree of compatibility between competing hypotheses. Within this formulation, the solutions of the matching problem correspond to evolutionary stable states (ESS's), a robust population-based generalization of the notion of a Nash equilibrium. In order to find ESS's of our matching game, we propose using a novel, fast evolutionary game dynamics motivated by Darwinian selection processes, which let the pure strategies play against each other until an equilibrium is reached. A distinguishing feature of the proposed framework is that it allows one to naturally deal with general many-to-many matching problems even in the presence of asymmetric compatibilities. The potential of the proposed approach is demonstrated via two sets of image matching experiments, both of which show that our results outperform those obtained using well-known domain-specific algorithms.
Joint pose estimator and feature learning for object detection
Authors: Karim Ali 0002, François Fleuret, David Hasler, Pascal Fua
Venue: ICCV
Year: 2009

A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. Specifically, we train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators instead of the usual image features. This allows the learning process to select and combine various estimates of the pose with features able to implicitly compensate for variations in pose. We demonstrate that a detector built in such a manner provides noticeable gains on two hand video sequences and analyze the performance of our detector as these data sets are synthetically enriched in pose while not increased in size.
Non-rigid object localization and segmentation using eigenspace representation
Authors: Omar Arif, Patricio A. Vela
Venue: ICCV
Year: 2009

This paper presents a novel non-rigid object localization and segmentation algorithm using an eigenspace representation. Previous approaches to eigenspace methods for object tracking use vectorized image regions as observations, whereas the proposed method uses each individual pixel as an observation. Localization using the pixel-wise eigenspace representation is robust to noise and occlusions. A unique feature of the approach is that it permits segmentation in addition to localization. Localization and segmentation are carried out by deriving a similarity function in the eigenspace. The algorithm is tested on synthetic and real world tracking examples to demonstrate the performance.
Kernel map compression using generalized radial basis functions
Authors: Omar Arif, Patricio A. Vela
Venue: ICCV
Year: 2009

The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately the computational complexity of the resulting method is of the order of the training set, which is quite large for many applications. This paper proposes a two step procedure for arriving at a compact and computationally efficient learning procedure. After learning, the second step takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate the empirical kernel maps. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss.
Stabilizing motion tracking using retrieved motion priors
Authors: Andreas Baak, Bodo Rosenhahn, Meinard Müller, Hans-Peter Seidel
Venue: ICCV
Year: 2009

In this paper, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion capturing. The basic idea is to start human tracking without prior knowledge about the performed actions. The resulting 3D motion sequences, which may be corrupted due to tracking errors, are locally classified according to available motion categories. Depending on the classification result, a retrieval system supplies suitable motion priors, which are then used to regularize and stabilize the tracking in the next iteration step. Experiments with the HumanEVA-II benchmark show that tracking and classification are remarkably improved after few iterations.
Similarity metrics for categorization: From monolithic to category specific
Authors: Boris Babenko, Steve Branson, Serge J. Belongie
Venue: ICCV
Year: 2009

Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjunction with a nearest neighbor classifier, or can be plugged in as kernels to an SVM. For the task of categorization two scenarios have thus far been explored. The first is to train a single “monolithic” similarity metric that is then used for all examples. The other is to train a metric for each category in a 1-vs-all manner. While the former approach seems to be at a disadvantage in terms of performance, the latter is not practical for large numbers of categories. In this paper we explore the space in between these two extremes. We present an algorithm that learns a few similarity metrics, while simultaneously grouping categories together and assigning one of these metrics to each group. We present promising results and show how the learned metrics generalize to novel categories.
Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification
Authors: François Bardet, Thierry Chateau, Datta Ramadasan
Venue: ICCV
Year: 2009

This paper addresses real-time automatic visual tracking, labeling and classification of a variable number of objects such as pedestrians or/and vehicles, under time-varying illumination conditions. The illumination and multi-object configuration are jointly tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF). The measurement is provided by a static camera, associated to a basic foreground / background segmentation. As a first contribution, we propose in this paper to jointly track the light source within the Particle Filter, considering it as an additionnal object. Illumination-dependant shadows cast by objects are modeled and treated as foreground, thus avoiding the difficult task of shadow segmentation. As a second contribution, we estimate object category as a random variable also tracked within the Particle Filter, thus unifying object tracking and classification into a single process. Real time tracking results are shown and discussed on sequences involving various categories of users such as pedestrians, cars, light trucks and heavy trucks.
Time series prediction by chaotic modeling of nonlinear dynamical systems
Authors: Arslan Basharat, Mubarak Shah
Venue: ICCV
Year: 2009

We use concepts from chaos theory in order to model nonlinear dynamical systems that exhibit deterministic behavior. Observed time series from such a system can be embedded into a higher dimensional phase space without the knowledge of an exact model of the underlying dynamics. Such an embedding warps the observed data to a strange attractor, in the phase space, which provides precise information about the dynamics involved. We extract this information from the strange attractor and utilize it to predict future observations. Given an initial condition, the predictions in the phase space are computed through kernel regression. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (linear, polynomial, radial basis, etc.) of the mapping function. The predicted points are then warped back to the observed time series. We demonstrate the utility of these predictions for human action synthesis, and dynamic texture synthesis. Our main contributions are: multivariate phase space reconstruction for human actions and dynamic textures, a deterministic approach to model dynamics in contrast to the popular noise-driven approaches for dynamic textures, and video synthesis from kernel regression in the phase space. Experimental results provide qualitative and quantitative analysis of our approach on standard data sets.
Image compression with anisotropic triangulations
Authors: Sébastien Bougleux, Gabriel Peyré, Laurent D. Cohen
Venue: ICCV
Year: 2009

We propose a new image compression method based on geodesic Delaunay triangulations. Triangulations are generated by a progressive geodesic meshing algorithm which exploits the anisotropy of images through a farthest point sampling strategy. This seeding is performed according to anisotropic geodesic distances which force the anisotropic Delaunay triangles to follow the geometry of the image. Geodesic computations are performed using a Riemannian Fast Marching, which recursively updates the geodesic distance to the seed points. A linear spline approximation on this triangulation allows to approximate faithfully sharp edges and directional features in images. The compression is achieved by coding both the coefficients of the spline approximation and the deviation of the geodesic triangulation from an Euclidean Delaunay triangulation. Numerical results show that taking into account the anisotropy improves the approximation by isotropic triangulations of complex images. The resulting encoder competes well with wavelet-based encoder such as JPEG-2000 on geometric images.
Robust tracking-by-detection using a detector confidence particle filter
Authors: Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
Venue: ICCV
Year: 2009

We propose a novel approach for multi-person tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. A main contribution of this paper is the exploration of how these unreliable information sources can be used for multi-person tracking. The resulting algorithm robustly tracks a large number of dynamically moving persons in complex scenes with occlusions, does not rely on background modeling, and operates entirely in 2D (requiring no camera or ground plane calibration). Our Markovian approach relies only on information from the past and is suitable for online applications. We evaluate the performance on a variety of datasets and show that it improves upon state-of-the-art methods.
Video object segmentation by tracking regions
Authors: William Brendel, Sinisa Todorovic
Venue: ICCV
Year: 2009

This paper presents an approach to unsupervised segmentation of moving and static objects occurring in a video. Objects are, in general, spatially cohesive and characterized by locally smooth motion trajectories. Therefore, they occupy regions within each frame, while the shape and location of these regions vary slowly from frame to frame. Thus, video segmentation can be done by tracking regions across the frames such that the resulting tracks are locally smooth. To this end, we use a low-level segmentation to extract regions in all frames, and then we transitively match and cluster the similar regions across the video. The similarity is defined with respect to the region photometric, geometric, and motion properties. We formulate a new circular dynamic-time warping (CDTW) algorithm that generalizes DTW to match closed boundaries of two regions, without compromising DTW's guarantees of achieving the optimal solution with linear complexity. Our quantitative evaluation and comparison with the state of the art suggest that the proposed approach is a competitive alternative to currently prevailing point-based methods.
Efficient multi-label ranking for multi-class learning: Application to object recognition
Authors: Serhat Selcuk Bucak, Pavan Kumar Mallapragada, Rong Jin 0001, Anil K. Jain 0001
Venue: ICCV
Year: 2009

Multi-label learning is useful in visual object recognition when several objects are present in an image. Conventional approaches implement multi-label learning as a set of binary classification problems, but they suffer from imbalanced data distributions when the number of classes is large. In this paper, we address multi-label learning with many classes via a ranking approach, termed multi-label ranking. Given a test image, the proposed scheme aims to order all the object classes such that the relevant classes are ranked higher than the irrelevant ones. We present an efficient algorithm for multi-label ranking based on the idea of block coordinate descent. The proposed algorithm is applied to visual object recognition. Empirical results on the PASCAL VOC 2006 and 2007 data sets show promising results in comparison to the state-of-the-art algorithms for multi-label learning.
Efficient segmentation using feature-based graph partitioning active contours
Authors: Filiz Bunyak, Kannappan Palaniappan
Venue: ICCV
Year: 2009

Graph partitioning active contours (GPAC) is a recently introduced approach that elegantly embeds the graph-based image segmentation problem within a continuous optimization framework. GPAC can be used within parametric snake-based or implicit level set-based active contour continuous paradigms for image partitioning. However, GPAC similar to many other graph-based approaches has quadratic memory requirements which severely limits the scalability of the algorithm to practical problem domains. An N xN image requires O(N(4)) computation and memory to create and store the full graph of pixel inter-relationships even before the start of the contour optimization process. For example, an 1024x1024 grayscale image needs over one terabyte of memory. Approximations using tile/block-based or superpixel-based multiscale grouping of the pixels reduces this complexity by trading off accuracy. This paper describes a new algorithm that implements the exact GPAC algorithm using a constant memory requirement of a few kilobytes, independent of image size.
Compact signatures for high-speed interest point description and matching
Authors: Michael Calonder, Vincent Lepetit, Pascal Fua, Kurt Konolige, James Bowman, Patrick Mihelich
Venue: ICCV
Year: 2009

Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.