OpenRaiser/Intern-Atlas huggingface dataset:
Non-local sparse models for image restoration
Authors: Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman
Venue: ICCV
Year: 2009

We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.
Max-margin additive classifiers for detection
Authors: Subhransu Maji, Alexander C. Berg
Venue: ICCV
Year: 2009

We present methods for training high quality object detectors very quickly. The core contribution is a pair of fast training algorithms for piece-wise linear classifiers, which can approximate arbitrary additive models. The classifiers are trained in a max-margin framework and significantly outperform linear classifiers on a variety of vision datasets. We report experimental results quantifying training time and accuracy on image classification tasks and pedestrian detection, including detection results better than the best previous on the INRIA dataset with faster training.
A framework for visual saliency detection with applications to image thumbnailing
Authors: Luca Marchesotti, Claudio Cifarelli, Gabriela Csurka
Venue: ICCV
Year: 2009

We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.
Subspace matching: Unique solution to point matching with geometric constraints
Authors: Manuel Marques, Marko Stosic, João Paulo Costeira
Venue: ICCV
Year: 2009

Finding correspondences between feature points is one of the most relevant problems in the whole set of visual tasks. In this paper we address the problem of matching a feature vector (or a matrix) to a given subspace. Given any vector base of such a subspace, we observe a linear combination of its elements with all entries swapped by an unknown permutation. We prove that such a computationally hard integer problem is uniquely solved in a convex set resulting from relaxing the original problem. Also, if noise is present, based on this result, we provide a robust estimate recurring to a linear programming-based algorithm. We use structure-from-motion and object recognition as motivating examples.
Robust visual tracking using ℓ1 minimization
Authors: Xue Mei, Haibin Ling
Venue: ICCV
Year: 2009

In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers.
Sparse representation of cast shadows via l1-regularized least squares
Authors: Xue Mei, Haibin Ling, David W. Jacobs
Venue: ICCV
Year: 2009

Scenes with cast shadows can produce complex sets of images. These images cannot be well approximated by low-dimensional linear subspaces. However, in this paper we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an ℓ1-regularized least squares formulation, with nonnegativity constraints. This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods.
Scene shape priors for superpixel segmentation
Authors: Alastair Philip Moore, Simon J. D. Prince, Jonathan Warrell, Umar Mohammed, Graham Jones
Venue: ICCV
Year: 2009

Unsupervised over-segmentation of an image into super-pixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. In this paper we investigate incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of the object boundaries in the image. We then describe an over-segmentation algorithm that partitions this density roughly equally between superpixels whilst still attempting to capture local object boundaries. We demonstrate this approach using road scenes where objects in the center of the image tend to be more distant and smaller than those at the edge. We show that our algorithm successfully learns this foveated spatial distribution and can exploit this knowledge to improve the segmentation. Lastly, we introduce a new metric for evaluating vision labeling problems. We measure performance on a challenging real-world dataset and illustrate the limitations of conventional evaluation metrics.
Non-Euclidean image-adaptive Radial Basis Functions for 3D interactive segmentation
Authors: Benoit Mory, Roberto Ardon, Anthony J. Yezzi, Jean-Philippe Thiran
Venue: ICCV
Year: 2009

In the context of variational image segmentation, we propose a new finite-dimensional implicit surface representation. The key idea is to span a subset of implicit functions with linear combinations of spatially-localized kernels that follow image features. This is achieved by replacing the Euclidean distance in conventional Radial Basis Functions with non-Euclidean, image-dependent distances. For the minimization of an objective region-based criterion, this representation yields more accurate results with fewer control points than its Euclidean counterpart. If the user positions these control points, the non-Euclidean distance enables to further specify our localized kernels for a target object in the image. Moreover, an intuitive control of the result of the segmentation is obtained by casting inside/outside labels as linear inequality constraints. Finally, we discuss several algorithmic aspects needed for a responsive interactive workflow. We have applied this framework to 3D medical imaging and built a real-time prototype with which the segmentation of whole organs is only a few clicks away.
A direct approach for efficiently tracking with 3D morphable models
Authors: Enrique Muñoz, José Miguel Buenaposada, Luis Baumela
Venue: ICCV
Year: 2009

We present an efficient algorithm for fitting a morphable model to an image sequence. It is built on a projective geometry formulation of perspective projection, which results in a linear mapping from 3D shape to the projective plane, and a factorisation of this mapping into matrices that can be partially computed off-line. This algorithm can cope with full 360 degrees object rotation and linear deformations. We validate our approach using synthetically generated and real sequences. Compared to a plain Lucas-Kanade implementation, we achieve a six fold increase in performance for a rigid object and two fold for a non-rigid face.
Recovering planar homographies between 2D shapes
Authors: József Németh, Csaba Domokos, Zoltan Kato
Venue: ICCV
Year: 2009

Images taken from different views of a planar object are related by planar homography. Recovering the parameters of such transformations is a fundamental problem in computer vision with various applications. This paper proposes a novel method to estimate the parameters of a homography that aligns two binary images. It is obtained by solving a system of nonlinear equations generated by integrating linearly independent functions over the domains determined by the shapes. The advantage of the proposed solution is that it is easy to implement, less sensitive to the strength of the deformation, works without established correspondences and robust against segmentation errors. The method has been tested on synthetic as well as on real images and its efficiency has been demonstrated in the context of two different applications: alignment of hip prosthesis X-ray images and matching of traffic signs.
Radiometric compensation using stratified inverses
Authors: Tian-Tsong Ng, Ramanpreet Singh Pahwa, Jiamin Bai, Tony Q. S. Quek, Kar-Han Tan
Venue: ICCV
Year: 2009

Through radiometric compensation, a projector-camera system can project a desired image onto a non-flat and non-white surface. This can be achieved by computing the inverse light transport of a scene. A light transport matrix is in general large and on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> elements. Therefore, computing the inverse light transport matrix is computationally and memory intensive. Two prior methods were proposed to simplify matrix inversion by ignoring scene inter-reflection between individual or clusters of camera pixels. However, compromising scene inter-reflection in spatial domain introduces spatial artifacts and how to systematically adjust the compensation quality is not obvious. In this work, we show how scene inter-reflection can be systematically approximated by stratifying the light transport of a scene. The stratified light transport enables a similar stratification in the inverse light transport. We can show that the stratified inverse light transport converges to the true inverse. For radiometric compensation, the set of stratified inverse light transport provides a systematic way of quantifying the tradeoff between computational efficiency and accuracy. The framework of stratified matrix inversion is general and can have other applications, especially for applications that involve large-size sparse matrices.
Display-camera calibration from eye reflections
Authors: Christian Nitschke, Atsushi Nakazawa, Haruo Takemura
Venue: ICCV
Year: 2009

We present a novel technique for calibrating display-camera systems from reflections in the user's eyes. Display-camera systems enable a range of vision applications that need controlled illumination, including 3D object reconstruction, facial modeling and human computer interaction. One important issue, though, is the geometric calibration of the display, which requires additional hardware and tedious user interaction. The proposed approach eliminates this requirement by analyzing patterns that are reflected in the cornea, a mirroring device that naturally exists in any display-camera system. We introduce an optimization strategy that is able to refine eye and spherical mirror calibration results. When applied to the eye, it even outperforms spherical mirror calibration unoptimized. Furthermore, we obtain a robust estimation of eye poses which can be used for eye tracking applications. Despite the difficult working conditions, the calibration results are good and should be sufficient for many applications.
Improving accuracy of geometric parameter estimation using projected score method
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: ICCV
Year: 2009

A fundamental problem in computer vision (CV) is the estimation of geometric parameters from multiple observations obtained from images; examples of such problems range from ellipse fitting to multi-view structure from motion (SFM). The maximum likelihood (ML) method is widely used to estimate the parameters in such problems, assuming Gaussian noises to be present in the observations, for example, bundle adjustment for SFM. According to the theory of statistics, the ML estimates are nearly optimal for these problems, provided that the variance of the observation noises is sufficiently small. This implies that when noises are not small, more accurate estimates can be derived as compared to the ML estimates. In this study, we propose the application of a method called the projected score method, developed in statistics for computing higher-accuracy estimates, to the CV problems. We describe how it can be customized to solve the CV problems and propose a numerical algorithm to implement the method. We show that the method works effectively for such problems.
Extending continuous cuts: Anisotropic metrics and expansion moves
Authors: Carl Olsson, Martin Byröd, Niels Chr. Overgaard, Fredrik Kahl
Venue: ICCV
Year: 2009

The concept of graph cuts is by now a standard method for all sorts of low level vision problems. Its popularity is largely due to the fact that globally or near globally optimal solutions can be computed using efficient max flow algorithms. On the other hand it has been observed that this method may suffer from metrication errors. Recent work has begun studying continuous versions of graph cuts, which give smaller metrication errors. Another advantage is that continuous cuts are straightforward to parallelize. In this paper we extend the class of functionals that can be optimized in the continuous setting to include anisotropic TV-norms. We show that there is a so called coarea formula for these functionals making it possible to minimize them by solving a convex problem. We also show that the concept of a-expansion moves can be reformulated to fit the continuous formulation, and we derive approximation bounds in analogy with the discrete case. A continuous version of the Potts model for multi-class segmentation problems is presented, and it is shown how to obtain provably good solutions using continuous α-expansions.
Robust facial feature tracking using selected multi-resolution linear predictors
Authors: Eng-Jon Ong, Yuxuan Lan, Barry-John Theobald, Richard W. Harvey, Richard Bowden
Venue: ICCV
Year: 2009

This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models or temporal models for dynamics are not required or used. Tracking facial features simply becomes the independent tracking of a set of points on the face. This allows us to cope with facial configurations not present in the training data. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel-level information to tracked feature position displacements. To improve on this, a novel and robust biased linear predictor is proposed in this paper. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Experimental results also show that this method performs more robustly and accurately than AAMs, without any a priori shape information and with minimal training examples.
Fast and robust Earth Mover's Distances
Authors: Ofir Pele, Michael Werman
Venue: ICCV
Year: 2009

We present a new algorithm for a robust family of Earth Mover's Distances - EMDs with thresholded ground distances. The algorithm transforms the flow-network of the EMD so that the number of edges is reduced by an order of magnitude. As a result, we compute the EMD by an order of magnitude faster than the original algorithm, which makes it possible to compute the EMD on large histograms and databases. In addition, we show that EMDs with thresholded ground distances have many desirable properties. First, they correspond to the way humans perceive distances. Second, they are robust to outlier noise and quantization effects. Third, they are metrics. Finally, experimental results on image retrieval show that thresholding the ground distance of the EMD improves both accuracy and speed.
You'll never walk alone: Modeling social behavior for multi-target tracking
Authors: Stefano Pellegrini, Andreas Ess, Konrad Schindler, Luc Van Gool
Venue: ICCV
Year: 2009

Object tracking typically relies on a dynamic model to predict the object's location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.
Correlated probabilistic trajectories for pedestrian motion detection
Authors: Frank Perbet, Atsuto Maki, Björn Stenger
Venue: ICCV
Year: 2009

This paper introduces an algorithm for detecting walking motion using point trajectories in video sequences. Given a number of point trajectories, we identify those which are spatio-temporally correlated as arising from feet in walking motion. Unlike existing techniques we do not assume clean point tracks but instead propose “probabilistic trajectories” as new features to classify. These are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. This representation tolerates the inherent trajectory ambiguity, for example due to occlusions. We then learn the correlation between the movement of two feet using a random forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera.
A hybrid generative/discriminative classification framework based on free-energy terms
Authors: Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic
Venue: ICCV
Year: 2009

Hybrid, generative-discriminative, techniques have proven to be valuable approaches in tackling difficult object or scene recognition problems. In general, a generative model over the available data for each image class is first learned providing a relatively comprehensive statistical multi-level representation. In this way, new meaningful image features become available, which encode the degree of fitness of the data with respect to the model at different representation levels. Such features are then fed into a discriminative classifier which can exploit the intrinsic data separability. In this paper, we propose the use of variational free energy terms as feature vectors, so that the degree of fitness of the data and the uncertainty over the generative process are explicitly included in the data description. The proposed method is automatically superior to a pure generative classification, and we also experimentally validate it on a wide selection of generative models applied to challenging benchmarks in hard computer vision tasks such as scene, object, and shape recognition. In several instances, the proposed approach outperforms the current state-of-the-art techniques as for classification results, while also showing to be computationally inexpensive.
Local distance functions: A taxonomy, new algorithms, and an evaluation
Authors: Deva Ramanan, Simon Baker
Venue: ICCV
Year: 2009

We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categorize existing algorithms by how, where and when they estimate the metric tensor. We also extend the taxonomy along each axis. How: We introduce hybrid algorithms that use a combination of dimensionality reduction and metric learning to ameliorate over-fitting. Where: We present an exact polynomial time algorithm to integrate the metric tensor along the lines between the test and training points under the assumption that the metric tensor is piecewise constant. When: We propose an interpolation algorithm where the metric tensor is sampled at a number of references points during the offline phase, which are then interpolated during online classification. We also present a comprehensive evaluation of all the algorithms on tasks in face recognition, object recognition, and digit recognition.
Incremental action recognition using feature-tree
Authors: Kishore K. Reddy, Jingen Liu, Mubarak Shah
Venue: ICCV
Year: 2009

Action recognition methods suffer from many drawbacks in practice, which include (1)the inability to cope with incremental recognition problems; (2)the requirement of an intensive training stage to obtain good performance; (3) the inability to recognize simultaneous multiple actions; and (4) difficulty in performing recognition frame by frame. In order to overcome all these drawbacks using a single method, we propose a novel framework involving the feature-tree to index large scale motion features using Sphere/Rectangle-tree (SR-tree). The recognition consists of the following two steps: 1) recognizing the local features by non-parametric nearest neighbor (NN), 2) using a simple voting strategy to label the action. The proposed method can provide the localization of the action. Since our method does not require feature quantization, the feature- tree can be efficiently grown by adding features from new training examples of actions or categories. Our method provides an effective way for practical incremental action recognition. Furthermore, it can handle large scale datasets due to the fact that the SR-tree is a disk-based data structure. We have tested our approach on two publicly available datasets, the KTH and the IXMAS multi-view datasets, and obtained promising results.