OpenRaiser/Intern-Atlas huggingface dataset:
Flat refractive geometry
Authors: Tali Treibitz, Yoav Y. Schechner, Hanumant Singh
Venue: CVPR
Year: 2008

While the study of geometry has mainly concentrated on single-viewpoint (SVP) cameras, there is growing attention to more general non-SVP systems. Here we study an important class of systems that inherently have a non-SVP: a perspective camera imaging through an interface into a medium. Such systems are ubiquitous: they are common when looking into water-based environments. The paper analyzes the common flat-interface class of systems. It characterizes the locus of the viewpoints (caustic) of this class, and proves that the SVP model is invalid in it. This may explain geometrical errors encountered in prior studies. Our physics-based model is parameterized by the distance of the lens from the medium interface, beside the focal length. The physical parameters are calibrated by a simple approach that can be based on a single-frame. This directly determines the system geometry. The calibration is then used to compensate for modeled system distortion. Based on this model, geometrical measurements of objects are significantly more accurate, than if based on an SVP model. This is demonstrated in real-world experiments.
Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision
Authors: Pavan K. Turaga, Ashok Veeraraghavan, Rama Chellappa
Venue: CVPR
Year: 2008

Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.
Learning on lie groups for invariant detection and tracking
Authors: Oncel Tuzel, Fatih Murat Porikli, Peter Meer
Venue: CVPR
Year: 2008

This paper presents a novel learning based tracking model combined with object detection. The existing techniques proceed by linearizing the motion, which makes an implicit Euclidean space assumption. Most of the transformations used in computer vision have matrix Lie group structure. We learn the motion model on the Lie algebra and show that the formulation minimizes a first order approximation to the geodesic error. The learning model is extended to train a class specific tracking function, which is then integrated to an existing pose dependent object detector to build a pose invariant object detection algorithm. The proposed model can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing object detection methods. The detection rate of the original detector is improved by more than 90% for large transformations.
A recursive filter for linear systems on Riemannian manifolds
Authors: Ambrish Tyagi, James W. Davis
Venue: CVPR
Year: 2008

We present an online, recursive filtering technique to model linear dynamical systems that operate on the state space of symmetric positive definite matrices (tensors) that lie on a Riemannian manifold. The proposed approach describes a predict-and-update computational paradigm, similar to a vector Kalman filter, to estimate the optimal tensor state. We adapt the original Kalman filtering algorithm to appropriately propagate the state over time and assimilate observations, while conforming to the geometry of the manifold. We validate our algorithm with synthetic data experiments and demonstrate its application to visual object tracking using covariance features.
Image based rendering for motion compensation in angiographic roadmapping
Authors: Christian Unger, Martin Groher, Nassir Navab
Venue: CVPR
Year: 2008

2D angiographic roadmapping is used frequently during image guided interventions to superimpose vessel structures onto currently acquired fluoroscopic images. While the fluoroscopic images, acquired with 12-15 frames per second, show patient bone anatomy as well as the current location of the inserted catheter, the roadmap delineates vessels to provide path information and to avoid accidental vessel wall punctures during catheter advancement.
Accurate eye center location and tracking using isophote curvature
Authors: Roberto Valenti, Theo Gevers
Venue: CVPR
Year: 2008

The ubiquitous application of eye tracking is precluded by the requirement of dedicated and expensive hardware, such as infrared high definition cameras. Therefore, systems based solely on appearance (i.e. not involving active infrared illumination) are being proposed in literature. However, although these systems are able to successfully locate eyes, their accuracy is significantly lower than commercial eye tracking devices. Our aim is to perform very accurate eye center location and tracking, using a simple Web cam. By means of a novel relevance mechanism, the proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve rotational invariance and to keep low computational costs. In this paper we test our approach for accurate eye location and robustness to changes in illumination and pose, using the BioIDand the Yale Face B databases, respectively. We demonstrate that our system can achieve a considerable improvement in accuracy over state of the art techniques.
Characterizing the shadow space of camera-light pairs
Authors: Daniel A. Vaquero, Rogério Schmidt Feris, Matthew Turk 0001, Ramesh Raskar
Venue: CVPR
Year: 2008

We present a theoretical analysis for characterizing the shadows cast by a point light source given its relative position to the camera. In particular, we analyze the epipolar geometry of camera-light pairs, including unusual camera-light configurations such as light sources aligned with the camerapsilas optical axis as well as convenient arrangements such as lights placed in the camera plane. A mathematical characterization of the shadows is derived to determine the orientations and locations of depth discontinuities when projected onto the image plane that could potentially be associated with cast shadows. The resulting theory is applied to compute a lower bound on the number of lights needed to extract all depth discontinuities from a general scene using a multiflash camera. We also provide a characterization of which discontinuities are missed and which are correctly detected by the algorithm, and a foundation for choosing an optimal light placement. Experiments with depth edges computed using two-flash setups and a four-flash setup illustrate the theory, and an additional configuration with a flash at the camerapsilas center of projection is exploited as a solution for some degenerate cases.
Dense specular shape from multiple specular flows
Authors: Yuriy Vasilyev, Yair Adato, Todd E. Zickler, Ohad Ben-Shahar
Venue: CVPR
Year: 2008

The inference of specular (mirror-like) shape is a particularly difficult problem because an image of a specular object is nothing but a distortion of the surrounding environment. Consequently, when the environment is unknown, such an image would seem to convey little information about the shape itself. It has recently been suggested (Adato et al., ICCV 2007) that observations of relative motion between a specular object and its environment can dramatically simplify the inference problem and allow one to recover shape without explicit knowledge of the environment content. However, this approach requires solving a non-linear PDE (the dasiashape from specular flow equationpsila) and analytic solutions are only known to exist for very constrained motions. In this paper, we consider the recovery of shape from specular flow under general motions. We show that while the dasiashape from specular flowpsila PDE for a single motion is non-linear, we can combine observations of multiple specular flows from distinct relative motions to yield a linear set of equations. We derive necessary conditions for this procedure, discuss several numerical issues with their solution, and validate our results quantitatively using image data.
Relaxed matching kernels for robust image comparison
Authors: Andrea Vedaldi, Stefano Soatto
Venue: CVPR
Year: 2008

The popular bag-of-features representation for object recognition collects signatures of local image patches and discards spatial information. Some have recently attempted to at least partially overcome this limitation, for instance by ldquospatial pyramidsrdquo and ldquoproximityrdquo kernels. We introduce the general formalism of ldquorelaxed matching kernelsrdquo (RMKs) that includes such approaches as special cases, allow us to derive useful general properties of these kernels, and to introduce new ones. As an example, we introduce a kernel based on matching graphs of features and one based on matching information-compressed features. We show that all RMKs are competitive and outperform in several cases recently published state-of-the-art results on standard datasets. However, we also show that a proper implementation of a baseline bag-of-features algorithm can be extremely competitive, and outperform the other methods in some cases.
Photometric stereo with coherent outlier handling and confidence estimation
Authors: Frank Verbiest, Luc Van Gool
Venue: CVPR
Year: 2008

In photometric stereo a robust method is required to deal with outliers, such as shadows and non-Lambertian reflections. In this paper we rely on a probabilistic imaging model that distinguishes between inliers and outliers, and formulate the problem as a Maximum-Likelihood estimation problem. To signal which imaging model to use a hidden binary inlier map is introduced, which, to account for the fact that inlier/outlier pixels typically group together, is modelled as a Markov Random Field. To make inference of model parameters and hidden variables tractable a mean field Expectation-Maximization (EM) algorithm is used. If for each pixel we add the scaled normal, i.e. albedo and normal combined, to the model parameters, it would not be possible to obtain a confidence estimate in the result. Instead, each scaled normal is added as a hidden variable, the distribution of which, approximated by a Gaussian, is also estimated in the EM algorithm. The covariance matrix of the recovered approximate Gaussian distribution serves as a confidence estimate of the scaled normal. We demonstrate experimentally the effectiveness or our approach.
Simultaneous learning of a discriminative projection and prototypes for Nearest-Neighbor classification
Authors: Mauricio Villegas, Roberto Paredes
Venue: CVPR
Year: 2008

Computer vision and image recognition research have a great interest in dimensionality reduction techniques. Generally these techniques are independent of the classifier being used and the learning of the classifier is carried out after the dimensionality reduction is performed, possibly discarding valuable information. In this paper we propose an iterative algorithm that simultaneously learns a linear projection base and a reduced set of prototypes optimized for the Nearest-Neighbor classifier. The algorithm is derived by minimizing a suitable estimation of the classification error probability. The proposed approach is assessed through a series of experiments showing a good behavior and a real potential for practical applications.
Action recognition using ballistic dynamics
Authors: Shiv Naga Prasad Vitaladevuni, Vili Kellokumpu, Larry S. Davis
Venue: CVPR
Year: 2008

We present a Bayesian framework for action recognition through ballistic dynamics. Psycho-kinesiological studies indicate that ballistic movements form the natural units for human movement planning. The framework leads to an efficient and robust algorithm for temporally segmenting videos into atomic movements. Individual movements are annotated with person-centric morphological labels called ballistic verbs. This is tested on a dataset of interactive movements, achieving high recognition rates. The approach is also applied on a gesture recognition task, improving a previously reported recognition rate from 84% to 92%. Consideration of ballistic dynamics enhances the performance of the popular Motion History Image feature. We also illustrate the approachpsilas general utility on real-world videos. Experiments indicate that the method is robust to view, style and appearance variations.
Object image retrieval by exploiting online knowledge resources
Authors: Gang Wang 0012, David A. Forsyth
Venue: CVPR
Year: 2008

We describe a method to retrieve images found on web pages with specified object class labels, using an analysis of text around the image and of image appearance. Our method determines whether an object is both described in text and appears in a image using a discriminative image model and a generative text model. Our models are learnt by exploiting established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for image). These resources provide rich text and object appearance information. We describe results on two data sets. The first is Berg’s collection of ten animal categories; on this data set, we outperform previous approaches [7, 33]. We have also collected five more categories. Experimental results show the effectiveness of our approach on this new data set.
Moving shape dynamics: A signal processing perspective
Authors: Liang Wang 0001, Xin Geng 0001, Christopher Leckie, Kotagiri Ramamohanarao
Venue: CVPR
Year: 2008

This paper provides a new perspective on human motion analysis, namely regarding human motions in video as general discrete time signals. While this seems an intuitive idea, research on human motion analysis has attracted little attention from the signal processing community. Sophisticated signal processing techniques create important opportunities for new solutions to the problem of human motion analysis. This paper investigates how the deformations of human silhouettes (or shapes) during articulated motion can be used as discriminating features to implicitly capture motion dynamics. In particular, we demonstrate the applicability of two widely used signal transform methods, namely the discrete Fourier transform (DFT) and discrete wavelet transform (DWT), for characterization and recognition of human motion sequences. Experimental results show the effectiveness of the proposed method on two state-of-the-art data sets.
Normalized tree partitioning for image segmentation
Authors: Jingdong Wang 0001, Yangqing Jia, Xian-Sheng Hua 0001, Changshui Zhang, Long Quan
Venue: CVPR
Year: 2008

In this paper, we propose a novel graph based clustering approach with satisfactory clustering performance and low computational cost. It consists of two main steps: tree fitting and partitioning. We first introduce a probabilistic method to fit a tree to a data graph under the sense of minimum entropy. Then, we propose a novel tree partitioning method under a normalized cut criterion, called Normalized Tree Partitioning (NTP), in which a fast combinatorial algorithm is designed for exact bipartitioning. Moreover, we extend it to k-way tree partitioning by proposing an efficient best-first recursive bipartitioning scheme. Compared with spectral clustering, NTP produces the exact global optimal bipartition, introduces fewer approximations for k-way partitioning and can intrinsically produce superior performance. Compared with bottom-up aggregation methods, NTP adopts a global criterion and hence performs better. Last, experimental results on image segmentation demonstrate that our approach is more powerful compared with existing graph-based approaches.
Correspondence-free multi-camera activity analysis and scene modeling
Authors: Xiaogang Wang 0001, Kinh Tieu, W. Eric L. Grimson
Venue: CVPR
Year: 2008

We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. We assume that the topology of camera views is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are tracked in each of the camera views independently, and the positions and velocities of objects along trajectories are computed as features. Under a generative model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. It accomplishes two tasks: (1) grouping trajectories in different camera views belonging to the same activity into one cluster; (2) modeling paths commonly taken by objects across camera views. To our knowledge, no prior result of co-clustering trajectories in multiple camera views has been published. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and the learning is unsupervised. Our approach is evaluated on two very large data sets with 22, 951 and 14, 985 trajectories.
Quasi-perspective projection with applications to 3D factorization from uncalibrated image sequences
Authors: Guanghui Wang 0001, Q. M. Jonathan Wu, Guoqiang Sun
Venue: CVPR
Year: 2008

The paper addresses the problem of factorization-based 3D reconstruction from uncalibrated image sequences. We propose a quasi-perspective projection model and apply the model to structure and motion recovery of rigid and nonrigid objects based on factorization of tracking matrix. The novelty and contribution of the paper lies in three aspects. First, under the assumption that the camera is far away from the object with small rotations, we propose and prove that the imaging process can be modeled by quasi-perspective projection. The model is more accurate than affine since the projective depths are implicitly embedded. Second, we apply the model to the factorization algorithm and establish the framework of rigid and nonrigid factorization under quasi-perspective assumption. Third, we propose a new and robust method to recover the transformation matrix that upgrades the factorization to the Euclidean space. The proposed method is validated and evaluated on synthetic and real image sequences and good improvements over existing solutions are observed.
Pattern discovery in motion time series via structure-based spectral clustering
Authors: Xiaozhe Wang, Liang Wang 0001, Anthony Wirth
Venue: CVPR
Year: 2008

This paper proposes an approach called ‘structure-based spectral clustering’ to identify clusters in motion time series for sequential pattern discovery. The proposed approach deploys a ‘statistical feature-based distance computation’ for spectral clustering algorithm. Compared to traditional spectral clustering approaches, in which the similarity matrix is constructed from the original data points by applying some similarity functions, the proposed approach builds the matrix based on a finite set of feature vectors. When the proposed approach uses less data points and simpler similarity function to computing the similarity matrix input for spectral clustering, it can improve the computational efficiency in constructing the similarity graph in spectral clustering compared to conventional approach. Promising experimental results with high accuracy on real world data sets demonstrate the capability and effectiveness of the proposed approach for pattern discovery in motion video sequences.
Bayesian tactile face
Authors: Zheshen Wang, Xinyu Xu, Baoxin Li
Venue: CVPR
Year: 2008

Computer users with visual impairment cannot access the rich graphical contents in print or digital media unless relying on visual-to-tactile conversion, which is done primarily by human specialists. Automated approaches to this conversion are an emerging research field, in which currently only simple graphics such as diagrams are handled. This paper proposes a systematic method for automatically converting a human portrait image into its tactile form. We model the face based on deformable active shape model (ASM) (Cootes et al., 1995), which is enriched by local appearance models in terms of gradient profiles along the shape. The generic face model including the appearance components is learnt from a set of training face images. Given a new portrait image, the prior model is updated through Bayesian inference. To facilitate the incorporation of a pose-dependent appearance model, we propose a statistical sampling scheme for the inference task. Furthermore, to compensate for the simplicity of the face model, edge segments of a given image are used to enrich the basic face model in generating the final tactile printout. Experiments are designed to evaluate the performance of the proposed method.
Filtering Internet image search results towards keyword based category recognition
Authors: Kamil Wnuk, Stefano Soatto
Venue: CVPR
Year: 2008

In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set. Instead we use a nonparametric measure of strangeness [8] in the space of holistic image representations, and perform an iterative feature elimination algorithm to remove the most strange examples from the category. This is the equivalent of keeping only features that are found to be consistent with others in the class. We show that applying our method to image search data before training improves average recognition performance, and demonstrate that we obtain comparative precision and recall results to the current state of the art, all the while maintaining a significantly simpler approach. In the process we also extend the strangeness-based feature elimination algorithm to automatically select good threshold values and perform filtering of a single class when the background is given.
An experimental study of employing visual appearance as a phenotype
Authors: Lior Wolf, Yoni Donner
Venue: CVPR
Year: 2008

Visual and non-visual data are often related through complex, indirect links, thus making the prediction of one from the other difficult. Examples include the partially- understood connections between firing of VI neurons and visual stimuli, the coupling between recorded speech and video of the corresponding lip movements, and the attempts to infer criminal intentions from surveillance videos. In this study, we explore the exploitation of the visual/non-visual relation between genetic sequences and visual appearance. This exploitation is currently considered infeasible due to the many hidden variables and unknown factors involved, the considerable variability and noise that exist in images and the high-dimensionality of the data. Despite the difficulties, we show convincing evidence that the application of correlations between genotype and visual phenotype for identification is feasible with current technologies. To this end, we employ sensitive forced- matching tests, that can accurately detect correlations between data sets. These tests are used to compare the performance of several existing algorithms, as well as novel ones that we have designed for the task.