OpenRaiser/Intern-Atlas huggingface dataset:
Online Learning for Human-Robot Interaction
Authors: Bogdan Raducanu, Jordi Vitrià
Venue: CVPR
Year: 2007

This paper presents a novel approach for incremental subspace learning based on an online version of the non-parametric discriminant analysis (NDA). For many real-world applications (like the study of visual processes, for instance) there is impossible to know beforehand the number of total classes or the exact number of instances per class. This motivated us to propose a new algorithm, in which new samples can be added asynchronously, at different time stamps, as soon as they become available. The proposed technique for NDA-eigenspace representation has been applied to the problem of online face recognition for human-robot interaction scenario.
Diverse Active Ranking for Multimedia Search
Authors: Shyamsundar Rajaram, Charlie K. Dagli, Nemanja Petrovic, Thomas S. Huang
Venue: CVPR
Year: 2007

Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user's effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues in the bipartite ranking setting. First, we introduce a scheme for picking the query set which will be labeled by an oracle so that it will aid us in learning the ranker in as few active learning rounds as possible. Secondly, we propose a fundamentally motivated, information theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Finally, we report comparative testing and results in a real-time image retrieval setting.
Real-time Gesture Recognition with Minimal Training Requirements and On-line Learning
Authors: Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James
Venue: CVPR
Year: 2007

In this paper, we introduce the semantic network model (SNM), a generalization of the hidden Markov model (HMM) that uses factorization of state transition probabilities to reduce training requirements, increase the efficiency of gesture recognition and on-line learning, and allow more precision in gesture modeling. We demonstrate the advantages both formally and experimentally, using examples such as full-body multimodal gesture recognition via optical motion capture and a pressure sensitive floor, as well as mouse/pen gesture recognition. Our results show that our algorithm performs much better than the traditional approach in situations where training samples are limited and/or the precision of the gesture model is high.
Biometric Authentication using Finger-Back Surface
Authors: Ch. Ravikanth, Ajay Kumar 0001
Venue: CVPR
Year: 2007

This paper investigates a new biometric system based on texture of the hand knuckles. The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. Hand geometry features can be acquired from the same image, at the same time and integrated to improve the performance of the system. The finger back surface images from each of the users are used to extract scale, translation and rotational invariant knuckle images. The proposed system, especially on the peg-free and non-contact imaging setup, achieves promising results when tested over a database of 105 users.
Object Classification in Visual Surveillance Using Adaboost
Authors: John-Paul Renno, Dimitrios Makris 0001, Graeme A. Jones
Venue: CVPR
Year: 2007

In this paper, we present a method of object classification within the context of visual surveillance. Our goal is the classification of tracked objects into one of the two classes: people and cars. Using training data comprised of trajectories tracked from our car-park, a weighted ensemble of Adaboost classifiers is developed. Each ensemble is representative of a particular feature, evaluated and normalised by its significance. Classification is performed using the sub-optimal hyper-plane derived by selection of the N-best performing feature ensembles. The resulting performance is compared to a similar Adaboost classifier, trained using a single ensemble over all dimensions.
Metropolis-Hasting techniques for finite-element-based registration
Authors: Frédéric J. P. Richard, Adeline M. M. Samson
Venue: CVPR
Year: 2007

In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis, this framework has specific features. The main feature is that displacement random fields are Markovian. We construct two hybrid Gibbs/Metropolis-Hasting algorithms which take fully advantage of this Markovian property. The second technique is defined in a coarse-to-fine way by introducing a penalization on the sampled posterior distribution. We present some promising results suggesting that both techniques can accurately register images. Experiments also show that the penalized technique is more robust to local maxima of the posterior distribution than the first technique. This study is a preliminary step towards the estimation of model parameters in complex image registration problems.
3D Probabilistic Feature Point Model for Object Detection and Recognition
Authors: Sami Romdhani, Thomas Vetter
Venue: CVPR
Year: 2007

This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D morphable model (3DMM) and has the property to be viewpoint invariant. This shape model is used to estimate the probability of the position of a feature point given the position of reference feature points, accounting for the uncertainty of the position of the reference points and of the intrinsic variability of the class of objects. The viewpoint invariant detection algorithm maximises a foreground/background likelihood ratio of the relative position of the feature points, their appearance, scale, orientation and occlusion state. Computational efficiency is obtained by using the Bellman principle and an early rejection rule based on 3D to 2D projection constraints. Evaluations of the detection algorithm on the CMU-P1E face images and on a large set of non-face images show high levels of accuracy (zero false alarms for more than 90% detection rate). As well as locating feature points, the detection algorithm also estimates the pose of the object and a few shape parameters. It is shown that it can be used to initialise a 3DMM fitting algorithm and thus enables a fully automatic viewpoint and lighting invariant image analysis solution.
Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework
Authors: Omer Rotem, Hayit Greenspan, Jacob Goldberger
Venue: CVPR
Year: 2007

In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods.
Dense mirroring surface recovery from 1D homographies and sparse correspondences
Authors: Stas Rozenfeld, Ilan Shimshoni, Michael Lindenbaum
Venue: CVPR
Year: 2007

In this work we recover the 3D shape of mirroring objects such as mirrors, sunglasses, and stainless steel objects. A computer monitor displays several images of parallel stripes, each image at a different angle. Reflections of these stripes in a mirroring surface are captured by the camera. For every image point, the directions of the displayed stripes and their reflections in the image are related by a 1D homography which can be computed robustly and using the statistically accurate heteroscedastic model, without monitor-image correspondence, which is generally required by other techniques. Focusing on a small set of image points for which monitor-image correspondence is computed, the depth and the local shape may be calculated relying on this homography. This is done by an optimization process which is related to the one proposed by Savarese, Chen and Perona, but is different and more stable. Then dense surface recovery is performed using constrained interpolation, which does not simply interpolate the surface depth values, but rather solves for the depth, the correspondence, and the local surface shape, simultaneously at each interpolated point. Consistency with the ID homography is thus required. The proposed method as well as the method described in [A. Ripsman and M. Jenkin, IEEE Int. Symposium on Computational Intelligence (2001)] are inherently unstable on a small part of the surface. We propose a method to detect these instabilities and correct them. The method was implemented and the shapes of a mirror, sunglasses, and a stainless steel ashtray were recovered at sub-millimeter accuracy.
Nearest First Traversing Graph for Simultaneous Object Tracking and Recognition
Authors: Junya Sakagaito, Toshikazu Wada
Venue: CVPR
Year: 2007

This paper presents a new method for simultaneous object tracking and recognition using object image database. This application requires two searches: search for object appearance stored in the database and that for pose parameters (position, scale, orientation, and so on) of the tracking object in each image frame. For simplifying this problem, we propose a new method, pose parameter embedding (PPE) that transforms the original problem to an appearance search problem. The nearest neighbor (NN) appearance search in this problem has a special property that gradually changing queries are given. For this problem, graph based NN search is suitable, because the preceding search result can be used as the starting point of the next search. Delaunay graph can be used for this search, however, both the graph construction cost and the degree (number of mean edges connected to a vertex) drastically increase in high-dimensional space. Instead, we propose nearest first traversing graph (NFTG) for avoiding these problems. Based on these two techniques, we successfully realized video-rate tracking and recognition.
Using Multiple Patches for 3D Object Recognition
Authors: Andrea Salgian
Venue: CVPR
Year: 2007

Image patches have become increasingly popular in a variety of applications, due to their resistance to clutter and partial occlusion, as well as their partial insensitivity to object pose. Recently Mikolajczyk and Schmid compared a number of local descriptors and concluded that the SIFT-based ones perform best in image matching tasks. In this paper we analyze the performance of three patch descriptors in the context of 3D object recognition: SIFT, PCA-SIFT and keyed context patches. We use a data set containing images of six objects on clean and cluttered backgrounds, taken around the whole viewing sphere, and we look at individual and fused performances. Individually, the keyed context patches perform best overall, but they are outperformed for some objects by SIFT and PCA-SIFT. Recognition is improved by fusing the rankings generated by these classifiers.
Monocular and Stereo Methods for AAM Learning from Video
Authors: Jason M. Saragih, Roland Göcke
Venue: CVPR
Year: 2007

The active appearance model (AAM) is a powerful method for modeling deformable visual objects. One of the major drawbacks of the AAM is that it requires a training set of pseudo-dense correspondences over the whole database. In this work, we investigate the utility of stereo constraints for automatic model building from video. First, we propose a new method for automatic correspondence finding in monocular images which is based on an adaptive template tracking paradigm. We then extend this method to take the scene geometry into account, proposing three approaches, each accounting for the availability of the fundamental matrix and calibration parameters or the lack thereof. The performance of the monocular method was first evaluated on a pre-annotated database of a talking face. We then compared the monocular method against its three stereo extensions using a stereo database.
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
Authors: Fabien Scalzo, Justus H. Piater
Venue: CVPR
Year: 2007

We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width and height are automatically determined. The optimality criterion is based on minimum-variance analysis, which first computes the variance of the appearance model for various patch deformations, and then selects the patch dimensions that yield the minimum variance over the training data. They are integrated into each level of our hierarchical representation that is learned in an iterative, bottom-up fashion. At each level of the hierarchy, pairs of features are identified that tend to occur at stable positions relative to each other, by clustering the configurational distributions of observed feature co-occurrences using expectation-maximization. For recognition, evidence is propagated using nonparametric belief propagation. Discriminative models are learned on the basis of our feature hierarchy by combining a SVM classifier with feature selection based on the Fisher score. Experiments on two very different, challenging image databases demonstrate the effectiveness of this framework for object class recognition, as well as the contribution of the adaptive patch features towards attaining highly competitive results.
Learning Conditional Random Fields for Stereo
Authors: Daniel Scharstein, Chris Pal
Venue: CVPR
Year: 2007

State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and intensities learned from real images. We have constructed a large number of stereo datasets with ground-truth disparities, and we use a subset of these datasets to learn the parameters of conditional random fields (CRFs). We present experimental results illustrating the potential of our approach for automatically learning the parameters of models with richer structure than standard hand-tuned MRF models.
Inferring Temporal Order of Images From 3D Structure
Authors: Grant Schindler, Frank Dellaert, Sing Bing Kang
Venue: CVPR
Year: 2007

In this paper, we describe a technique to temporally sort a collection of photos that span many years. By reasoning about persistence of visible structures, we show how this sorting task can be formulated as a constraint satisfaction problem (CSP). Casting this problem as a CSP allows us to efficiently find a suitable ordering of the images despite the large size of the solution space (factorial in the number of images) and the presence of occlusions. We present experimental results for photographs of a city acquired over a one hundred year period.
Inferring Grammar-based Structure Models from 3D Microscopy Data
Authors: Joseph Schlecht, Kobus Barnard, Ekaterina H. Spriggs, Barry Pryor
Venue: CVPR
Year: 2007

We present a new method to fit grammar-based stochastic models for biological structure to stacks of microscopic images captured at incremental focal lengths. Providing the ability to quantitatively represent structure and automatically fit it to image data enables important biological research. We consider the case where individuals can be represented as an instance of a stochastic grammar, similar to L-systems used in graphics to produce realistic plant models. In particular, we construct a stochastic grammar of Alternaria, a genus of fungus, and fit instances of it to microscopic image stacks. We express the image data as the result of a generative process composed of the underlying probabilistic structure model together with the parameters of the imaging system. Fitting the model then becomes probabilistic inference. For this we create a reversible-jump MCMC sampler to traverse the parameter space. We observe that incorporating spatial structure helps fit the model parts, and that simultaneously fitting the imaging system is also very helpful.
Towards Robust Pedestrian Detection in Crowded Image Sequences
Authors: Edgar Seemann, Mario Fritz, Bernt Schiele
Venue: CVPR
Year: 2007

Object class detection in scenes of realistic complexity remains a challenging task in computer vision. Most recent approaches focus on a single and general model for object class detection. However, in particular in the context of image sequences, it may be advantageous to adapt the general model to a more object-instance specific model in order to detect this particular object reliably within the image sequence. In this work we present a generative object model that is capable to scale from a general object class model to a more specific object-instance model. This allows to detect class instances as well as to distinguish between individual object instances reliably. We experimentally evaluate the performance of the proposed system on both still images and image sequences.
Influence of numerical conditioning on the accuracy of relative orientation
Authors: Sinisa Segvic, Gerald Schweighofer, Axel Pinz
Venue: CVPR
Year: 2007

We study the influence of numerical conditioning on the accuracy of two closed-form solutions to the overconstrained relative orientation problem. We consider the well known eight-point algorithm and the recent five-point algorithm, and evaluate changes in their performance due to Hartley's normalization and Muehlich's equilibration. The need for numerical conditioning is introduced by explaining the known occurence of the bias of the eight-point algorithm towards the forward motion. Then it is shown how conditioning can be used to improve the results of the recent five-point algorithm. This is not straightforward since the conditioning disturbs the calibration of the input data. The conditioning therefore needs to be reverted before enforcing the internal cubic constraints of the essential matrix. The obtained improvements are less dramatic than in the case of the eight-point algorithm, for which we offer a plausible explanation. The theoretical claims are backed up with extensive experimentation on noisy artificial datasets, under a variety of geometric and imaging parameters.
Simultaneous Optimization of Structure and Motion in Dynamic Scenes Using Unsynchronized Stereo Cameras
Authors: Akihito Seki, Masatoshi Okutomi
Venue: CVPR
Year: 2007

In this paper, we propose a simultaneous estimation method of structure and motion in dynamic scenes. Usual methods for obtaining structure and motion using stereo cameras require two kinds of operations: stereo correspondence and tracking. Therefore, we must separately determine the correspondence between stereo images and sequential images. This necessity complicates the algorithm and increases the possibility of mismatches because of the object's motion and visibility change in the images. Our proposed method makes two contributions. The first contribution is the method of corresponding all stereo images and sequential images at once. Therefore, we can obtain the structure and motion simultaneously and more accurately. On the other hand, most stereo correspondence algorithms are limited to use under a synchronized status. In a stereo rig using unsynchronized cameras, as are most commercially available cameras, the structure cannot be obtained by stereo correspondence and triangulation because of the unknown time offset between cameras. Therefore, our second contribution is a method of estimating structure, motion, and time offset simultaneously using unsynchronized stereo cameras. This latter task is accomplished by taking advantage of the first contribution scheme. Additionally, our method requires no preprocessing such as motion segmentation for separating identical-motion objects and advance calibration of the time offset. Finally, we present the experimental results using both synthetic and real images.
Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing
Authors: Vinay Sharma, James W. Davis
Venue: CVPR
Year: 2007

We present a method for the simultaneous detection and segmentation of people from static images. The proposed technique requires no manual segmentation during training, and exploits top-down and bottom-up processing within a single framework for both object localization and 2D shape estimation. First, the coarse shape of the object is learned from a simple training phase utilizing low-level edge features. Motivated by the observation that most object categories have regular shapes and closed boundaries, relations between these features are then exploited to derive mid-level cues, such as continuity and closure. A novel Markov random field defined on the edge features is presented that integrates the coarse shape information with our expectation that objects are likely to have boundaries that are regular and closed. The algorithm is evaluated on pedestrian datasets of varying difficulty, including a wide range of camera viewpoints, and person orientations. Quantitative results are presented for person detection and segmentation, demonstrating the effectiveness of the proposed technique to simultaneously address both these tasks.
On the Direct Estimation of the Fundamental Matrix
Authors: Yaser Sheikh, Asaad Hakeem, Mubarak Shah
Venue: CVPR
Year: 2007

The fundamental matrix is a central construct in the analysis of images captured from a pair of cameras and many feature-based methods have been proposed for its computation. In this paper, we propose a direct method for estimating the fundamental matrix where the motion between the frames is small (e.g. between successive frames of a video). To achieve this, a warping function is presented for the fundamental matrix by using the brightness constancy constraint in conjunction with geometric constraints. Using this warping function, an iterative hierarchical algorithm is described to recover accurate estimates of the fundamental matrix. We present results of experimentation to evaluate the performance of the proposed approach and demonstrate improved accuracy in the computation of the fundamental matrix.
Trajectory Association across Non-overlapping Moving Cameras in Planar Scenes
Authors: Yaser Sheikh, Xin Li 0022, Mubarak Shah
Venue: CVPR
Year: 2007

The ability to associate objects across multiple views allows co-operative use of an ensemble cameras for scene understanding. In this paper, we present a principled solution to object association where both the scene and the object motion are modeled. By making the motion model of each object with respect to time explicit, we are able to solve the trajectory association problem in a unified framework for overlapping or non-overlapping cameras. We recover the assignment of associations while simultaneously computing the maximum likelihood estimates of the inter-camera homographies and the trajectory parameters using the expectation maximization algorithm. Quantitative results on simulations are reported along with several results on real data.