OpenRaiser/Intern-Atlas huggingface dataset:
Weighted graph characteristics from oriented line graph polynomials
Authors: Peng Ren 0001, Richard C. Wilson 0001, Edwin R. Hancock
Venue: ICCV
Year: 2009

We develop a novel method for extracting graph characteristics from edge-weighted graphs, based on an extension of the Ihara zeta function from unweighted to edge-weighted graphs. This is effected by generalizing the determinant form of the Ihara zeta function. We use the set of the reciprocal polynomial coefficients of the resulting Ihara zeta function, i.e. the Ihara coefficients, to construct our characterization. We also present a spectral analysis of the edge-weighted graph Ihara coefficients and indicate their advantages over graph spectral methods. Experimental results reveal that the Ihara coefficients are effective for the purpose of clustering edge-weighted graphs.
Seeing 3D objects in a single 2D image
Authors: Diego Rother, Guillermo Sapiro
Venue: ICCV
Year: 2009

A general framework simultaneously addressing pose estimation, 2D segmentation, object recognition, and 3D reconstruction from a single image is introduced in this paper. The proposed approach partitions 3D space into voxels and estimates the voxel states that maximize a likelihood integrating two components: the object fidelity, that is, the probability that an object occupies the given voxels, here encoded as a 3D shape prior learned from 3D samples of objects in a class; and the image fidelity, meaning the probability that the given voxels would produce the input image when properly projected to the image plane. We derive a loop-less graphical model for this likelihood and propose a computationally efficient optimization algorithm that is guaranteed to produce the global likelihood maximum. Furthermore, we derive a multi-resolution implementation of this algorithm that permits to trade reconstruction and estimation accuracy for computation. The presentation of the proposed framework is complemented with experiments on real data demonstrating the accuracy of the proposed approach.
Diagram techniques for multiple view geometry
Authors: Alberto Ruiz, Pedro E. López-de-Teruel
Venue: ICCV
Year: 2009

Multilinear algebra is a powerful theoretical tool for visual geometry, but widespread usage of traditional typographical notation often hides its conceptual elegance and simplicity. As demonstrated in other scientific fields, we can take full advantage of multilinear methods using graphical notation. In this paper we adapt standard tensor diagrammatic techniques to the specific requirements of visual geometry, so that geometric relations are represented by circuits which can be manipulated using simple rules. The advantages of this approach are illustrated in several constructions, including straightforward derivations of the standard multiview relations (Fundamental Matrix, Trifocal and Quadrifocal Tensors), and nearly mechanical procedures for camera extraction.
Dense 3D reconstruction method using a single pattern for fast moving object
Authors: Ryusuke Sagawa, Yuichi Ota, Yasushi Yagi, Ryo Furukawa 0001, Naoki Asada, Hiroshi Kawasaki
Venue: ICCV
Year: 2009

Dense 3D reconstruction of extremely fast moving objects could contribute to various applications such as body structure analysis and accident avoidance and so on. The actual cases for scanning we assume are, for example, acquiring sequential shape at the moment when an object explodes, or observing fast rotating turbine's blades. In this paper, we propose such a technique based on a one-shot scanning method that reconstructs 3D shape from a single image where dense and simple pattern are projected onto an object. To realize dense 3D reconstruction from a single image, there are several issues to be solved; e.g. instability derived from using multiple colors, and difficulty on detecting dense pattern because of influence of object color and texture compression. This paper describes the solutions of the issues by combining two methods, that is (1) an efficient line detection technique based on de Bruijn sequence and belief propagation, and (2) an extension of shape from intersections of lines method. As a result, a scanning system that can capture an object in fast motion has been actually developed by using a high-speed camera. In the experiments, the proposed method successfully captured the sequence of dense shapes of an exploding balloon, and a breaking ceramic dish at 300–1000 fps.
Deformable model fitting with a mixture of local experts
Authors: Jason M. Saragih, Simon Lucey, Jeffrey F. Cohn
Venue: ICCV
Year: 2009

Local experts have been used to great effect for fitting deformable models to images. Typically, the best location in an image for the deformable model's landmarks are found through a locally exhaustive search using these experts. In order to achieve efficient fitting, these experts should afford an efficient evaluation, which often leads to forms with restricted discriminative capacity. In this work, a framework is proposed in which multiple simple experts can be utilized to increase the capacity of the detections overall. In particular, the use of a mixture of linear classifiers is proposed, the computational complexity of which scales linearly with the number of mixture components. The fitting objective is maximized using the expectation maximization (EM) algorithm, where approximations to the true objective are made in order to facilitate efficient and numerically stable fitting. The efficacy of the proposed approach is evaluated on the task of generic face fitting where performance improvement is observed over two existing methods.
SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter
Authors: Torsten Sattler, Bastian Leibe, Leif Kobbelt
Venue: ICCV
Year: 2009

Geometric verification with RANSAC has become a crucial step for many local feature based matching applications. Therefore, the details of its implementation are directly relevant for an application's run-time and the quality of the estimated results. In this paper, we propose a RANSAC extension that is several orders of magnitude faster than standard RANSAC and as fast as and more robust to degenerate configurations than PROSAC, the currently fastest RANSAC extension from the literature. In addition, our proposed method is simple to implement and does not require parameter tuning. Its main component is a spatial consistency check that results in a reduced correspondence set with a significantly increased inlier ratio, leading to faster convergence of the remaining estimation steps. In addition, we experimentally demonstrate that RANSAC can operate entirely on the reduced set not only for sampling, but also for its consensus step, leading to additional speed-ups. The resulting approach is widely applicable and can be readily combined with other extensions from the literature. We quantitatively evaluate our approach's robustness on a variety of challenging datasets and compare its performance to the state-of-the-art.
Human detection using partial least squares analysis
Authors: William Robson Schwartz, Aniruddha Kembhavi, David Harwood, Larry S. Davis
Venue: ICCV
Year: 2009

Significant research has been devoted to detecting people in images and videos. In this paper we describe a human detection method that augments widely used edge-based features with texture and color information, providing us with a much richer descriptor set. This augmentation results in an extremely high-dimensional feature space (more than 170,000 dimensions). In such high-dimensional spaces, classical machine learning algorithms such as SVMs are nearly intractable with respect to training. Furthermore, the number of training samples is much smaller than the dimensionality of the feature space, by at least an order of magnitude. Finally, the extraction of features from a densely sampled grid structure leads to a high degree of multicollinearity. To circumvent these data characteristics, we employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions, reduced from the original 170,000). Our human detection system, employing PLS analysis over the enriched descriptor set, is shown to outperform state-of-the-art techniques on three varied datasets including the popular INRIA pedestrian dataset, the low-resolution gray-scale DaimlerChrysler pedestrian dataset, and the ETHZ pedestrian dataset consisting of full-length videos of crowded scenes.
A near optimal acceptance-rejection algorithm for exact cross-correlation search
Authors: Haim Schweitzer, Robert Finis Anderson, Rui A. Deng
Venue: ICCV
Year: 2009

We describe a fast algorithm that searches for the k most likely locations of a template in an image according to the standard normalized correlations criterion. The algorithm is exact; it always finds the best matches. Its speed is achieved by utilizing an acceptance-rejection pruning scheme, applied to easily computed bounds on the normalized correlation values. Previously proposed rejection schemes require a rejection threshold that has to be provided or estimated from the data. Our algorithm does not use such thresholds explicitly, but performs as well as if the perfect rejection threshold is known.
Learning pedestrian dynamics from the real world
Authors: Paul Scovanner, Marshall F. Tappen
Venue: ICCV
Year: 2009

In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and allows us to efficiently optimize a continuous pedestrian cost model. We show that this model can be trained on automatic tracking results, and provides realistic and accurate pedestrian motions.
Filter flow
Authors: Steven M. Seitz, Simon Baker
Venue: ICCV
Year: 2009

The filter flow problem is to compute a space-variant linear filter that transforms one image into another. This framework encompasses a broad range of transformations including stereo, optical flow, lighting changes, blur, and combinations of these effects. Parametric models such as affine motion, vignetting, and radial distortion can also be modeled within the same framework. All such transformations are modeled by selecting a number of constraints and objectives on the filter entries from a catalog which we enumerate. Most of the constraints are linear, leading to globally optimal solutions (via linear programming) for affine transformations, depth-from-defocus, and other problems. Adding a (non-convex) compactness objective enables solutions for optical flow with illumination changes, space-variant defocus, and higher-order smoothness.
Detection of human actions from a single example
Authors: Hae Jong Seo, Peyman Milanfar
Venue: ICCV
Year: 2009

We present an algorithm for detecting human actions based upon a single given video example of such actions. The proposed method is unsupervised, does not require learning, segmentation, or motion estimation. The novel features employed in our method are based on space-time locally adaptive regression kernels. Our method is based on the dense computation of so-called space-time local regression kernels (i.e. local descriptors) from a query video, which measure the likeness of a voxel to its spatio-temporal surroundings. Salient features are then extracted from these descriptors using principal components analysis (PCA). These are efficiently compared against analogous features from the target video using a matrix generalization of the cosine similarity measure. The algorithm yields a scalar resemblance volume; each voxel indicating the like-lihood of similarity between the query video and all cubes in the target video. By employing non-parametric significance tests and non-maxima suppression, we accurately detect the presence and location of actions similar to the given query video. High performance is demonstrated on a challenging set of action data indicating successful detection of multiple complex actions even in the presence of fast motions.
Mode-detection via median-shift
Authors: Lior Shapira, Shai Avidan, Ariel Shamir
Venue: ICCV
Year: 2009

Median-shift is a mode seeking algorithm that relies on computing the median of local neighborhoods, instead of the mean. We further combine median-shift with Locality Sensitive Hashing (LSH) and show that the combined algorithm is suitable for clustering large scale, high dimensional data sets. In particular, we propose a new mode detection step that greatly accelerates performance. In the past, LSH was used in conjunction with mean shift only to accelerate nearest neighbor queries. Here we show that we can analyze the density of the LSH bins to quickly detect potential mode candidates and use only them to initialize the median-shift procedure. We use the median, instead of the mean (or its discrete counterpart - the medoid) because the median is more robust and because the median of a set is a point in the set. A median is well defined for scalars but there is no single agreed upon extension of the median to high dimensional data. We adopt a particular extension, known as the Tukey median, and show that it can be computed efficiently using random projections of the high dimensional data onto 1D lines, just like LSH, leading to a tightly integrated and efficient algorithm.
Background Subtraction for Freely Moving Cameras
Authors: Yaser Sheikh, Omar Javed, Takeo Kanade
Venue: ICCV
Year: 2009

Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. In this paper, we extend the concept of `subtracting' areas at rest to apply to video captured from a freely moving camera. We do not assume that the background is well-approximated by a plane or that the camera center remains stationary during motion. The method operates entirely using 2D image measurements without requiring an explicit 3D reconstruction of the scene. A sparse model of background is built by robustly estimating a compact trajectory basis from trajectories of salient features across the video, and the background is `subtracted' by removing trajectories that lie within the space spanned by the basis. Foreground and background appearance models are then built, and an optimal pixel-wise foreground/background labeling is obtained by efficiently maximizing a posterior function.
Untangling fibers by quotient appearance manifold mapping for grayscale shape classification
Authors: Yoshihisa Shinagawa, Yuping Lin
Venue: ICCV
Year: 2009

Appearance manifolds have been one of the most powerful methods for object recognition. However, they could not be used for grayscale shape classification, particularly in three dimensions, such as classifying medical lesion volumes or galaxy images. The main cause of the difficulty is that the appearance manifolds of shape classes have entangled fibers in their embedded Euclidean space. This paper proposes a novel appearance-based method called the quotient appearance manifold mapping to untangle the fibers of the appearance manifolds. First, the quotient manifold is constructed to untangle the fiber bundles of appearance manifolds. The mapping from each point of the manifold to the quotient submanifold is then proposed to classify grayscale shapes. We show the effectiveness in grayscale 3D shape recognition using medical images.
The Normalized Subspace Inclusion: Robust clustering of motion subspaces
Authors: Nuno Pinho da Silva, João Paulo Costeira
Venue: ICCV
Year: 2009

Perceiving dynamic scenes of rigid bodies, through affine projections of moving 3D point clouds, boils down to clustering the rigid motion subspaces supported by the points' image trajectories. For a physically meaningful interpretation, clusters must be consistent with the geometry of the underlying subspaces. Most of the existing measures for subspace clustering are ambiguous, or geometrically inconsistent. A practical consequence is that methods based on such (dis)similarities are unstable when the number of rigid bodies increase. This paper introduces the Normalized Subspace Inclusion (NSI) criterion to resolve these issues. Relying on this similarity, we propose a robust methodology for rigid motion segmentation, and test it, extensively, on the Hopkins155 database. The geometric consistency of the NSI assures the method's accuracy when the number of rigid bodies increases, while robustness proves to be suitable for dealing with challenging imaging conditions.
Simultaneous and orthogonal decomposition of data using Multimodal Discriminant Analysis
Authors: Terence Sim, Sheng Zhang 0007, Jianran Li, Yan Chen
Venue: ICCV
Year: 2009

We present Multimodal Discriminant Analysis (MMDA), a novel method for decomposing variations in a dataset into independent factors (modes). For face images, MMDA effectively separates personal identity, illumination and pose into orthogonal subspaces. MMDA is based on maximizing the Fisher Criterion on all modes at the same time, and is therefore well-suited for multimodal and mode-invariant pattern recognition. We also show that MMDA may be used for dimension reduction, and for synthesizing images under novel illumination and even novel personal identity.
Fast Ray features for learning irregular shapes
Authors: Kevin Smith 0001, Alan Carleton, Vincent Lepetit
Venue: ICCV
Year: 2009

We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to represent with standard feature sets. This property allows Ray features to efficiently and robustly recognize deformable or irregular shapes, such as cells in microscopic imagery. Experiments show Ray features clearly outperform other powerful features including Haar-like features and Histograms of Oriented Gradients when applied to detecting irregularly shaped neuron nuclei and mitochondria. Ray features can also provide important complementary information to Haar features for other tasks such as face detection, reducing the number of weak learners and computational cost. Ray features can be efficiently precomputed to reduce cost, just as precomputing integral images reduces the overall cost of Haar features. While Rays are slightly more expensive to precompute, their computational cost is less than that of Haar features for scanning an AdaBoost-based detector window across an image at run-time.
Efficient subset selection via the kernelized Rényi distance
Authors: Balaji Vasan Srinivasan, Ramani Duraiswami
Venue: ICCV
Year: 2009

With improved sensors, the amount of data available in many vision problems has increased dramatically and allows the use of sophisticated learning algorithms to perform inference on the data. However, since these algorithms scale with data size, pruning the data is sometimes necessary. The pruning procedure must be statistically valid and a representative subset of the data must be selected without introducing selection bias. Information theoretic measures have been used for sampling the data, retaining its original information content. We propose an efficient Rényi entropy based subset selection algorithm. The algorithm is first validated and then applied to two sample applications where machine learning and data pruning are used. In the first application, Gaussian process regression is used to learn object pose. Here it is shown that the algorithm combined with the subset selection is significantly more efficient. In the second application, our subset selection approach is used to replace vector quantization in a standard object recognition algorithm, and improvements are shown.
Optimizing parametric total variation models
Authors: Petter Strandmark, Fredrik Kahl, Niels Chr. Overgaard
Venue: ICCV
Year: 2009

One of the key factors for the success of recent energy minimization methods is that they seek to compute global solutions. Even for non-convex energy functionals, optimization methods such as graph cuts have proven to produce high-quality solutions by iterative minimization based on large neighborhoods, making them less vulnerable to local minima. Our approach takes this a step further by enlarging the search neighborhood with one dimension. In this paper we consider binary total variation problems that depend on an additional set of parameters. Examples include: (i) the Chan-Vese model that we solve globally (ii) ratio and constrained minimization which can be formulated as parametric problems, and (iii) variants of the Mumford-Shah functional. Our approach is based on a recent theorem of Chambolle which states that solving a one-parameter family of binary problems amounts to solving a single convex variational problem. We prove a generalization of this result and show how it can be applied to parametric optimization.
Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories
Authors: Hao Su 0001, Min Sun 0001, Li Fei-Fei 0001, Silvio Savarese
Venue: ICCV
Year: 2009

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.
Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait
Authors: Daisuke Sugimura, Kris Makoto Kitani, Takahiro Okabe, Yoichi Sato 0001, Akihiro Sugimoto
Venue: ICCV
Year: 2009

In this work, we propose a method for tracking individuals in crowds. Our method is based on a trajectory-based clustering approach that groups trajectories of image features that belong to the same person. The key novelty of our method is to make use of a person's individuality, that is, the gait features and the temporal consistency of local appearance to track each individual in a crowd. Gait features in the frequency domain have been shown to be an effective biometric cue in discriminating between individuals, and our method uses such features for tracking people in crowds for the first time. Unlike existing trajectory-based tracking methods, our method evaluates the dissimilarity of trajectories with respect to a group of three adjacent trajectories. In this way, we incorporate the temporal consistency of local patch appearance to differentiate trajectories of multiple people moving in close proximity. Our experiments show that the use of gait features and the temporal consistency of local appearance contributes to significant performance improvement in tracking people in crowded scenes.