OpenRaiser/Intern-Atlas huggingface dataset:
Incorporating the Boltzmann Prior in Object Detection Using SVM
Authors: Margarita Osadchy, Daniel Keren
Venue: CVPR
Year: 2006

In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of natural images into support vector machines. SVMs are known to be robust to overfitting; however, a few training examples usually do not represent well the structure of the class. Thus the resulting detectors are not robust and highly depend on the choice of the training examples. We incorporate the prior on natural images by requiring that the separating hyperplane will not only yield a wide margin, but also that the corresponding positive half space will have a low probability to contain natural images (the background). Our experiments on real data sets show that the resulting detector is more robust to the choice of training examples, and substantially improves both linear and kernel SVMwhen trained on 10 positive and 10 negative examples.
Augmenting Shape with Appearance in Vehicle Category Recognition
Authors: Özge Can Özcanli, Amir Tamrakar, Benjamin B. Kimia, Joseph L. Mundy
Venue: CVPR
Year: 2006

Shape is an important cue for generic object recognition but can be insufficient without other cues such as object appearance. We explore a number of ways in which the geometric aspects of an object can be augmented with its appearance. The main idea is to construct a dense correspondence between the interior regions of two shapes based on a shape-based correspondence so that the intensity and gradient distributions can be compared, e.g., using a mutual information paradigm. Three methods for regional alignment are suggested and compared here, based on: (i) propagation of correspondences from the silhouette to parallel curves in the interior, (ii) intersection of line segments anchored on corresponding points on the contour, and (iii) correspondence of shape skeletons. These methods have been implemented and applied to vehicle category recognition from aerial videos under known viewing and illumination conditions. We have constructed a photo-realistic synthetic video database to explore the performance of these methods under controlled conditions. We have also tested these algorithms on real video collected for this purpose from a balloon. Our findings indicate that (i) augmenting shape with appearance significantly increases recognition rate, and (ii) the region correspondence induced by the shape skeleton yields the highest performance.
A Framework for Feature Selection for Background Subtraction
Authors: Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
Venue: CVPR
Year: 2006

Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. In this paper we introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely RealBoost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.
Tunable Kernels for Tracking
Authors: Vasu Parameswaran, Visvanathan Ramesh, Imad Zoghlami
Venue: CVPR
Year: 2006

We present a tunable representation for tracking that simultaneously encodes appearance and geometry in a manner that enables the use of mean-shift iterations for tracking. The classic formulation of the tracking problem using mean-shift iterations encodes spatial information very loosely (i.e. using radially symmetric kernels). A problem with such a formulation is that it becomes easy for the tracker to get confused with other objects having the same feature distribution but different spatial configurations of features. Subsequent approaches have addressed this issue but not to the degree of generality required for tracking specific classes of objects and motions (e.g. humans walking). In this paper, we formulate the tracking problem in a manner that encodes the spatial configuration of features along with their density and yet retains robustness to spatial deformations and feature density variations. The encoding of spatial configuration is done using a set of kernels whose parameters can be optimized for a given class of objects and motions, off-line. The formulation enables the use of meanshift iterations and runs in real-time. We demonstrate better tracking results on synthetic and real image sequences as compared to the original mean-shift tracker.
Recursive estimation of generative models of video
Authors: Nemanja Petrovic, Aleksandar Ivanovic, Nebojsa Jojic
Venue: CVPR
Year: 2006

In this paper we present a generative model and learning procedure for unsupervised video clustering into scenes. The work addresses two important problems: realistic modeling of the sources of variability in the video and fast transformation invariant frame clustering. We suggest a solution to the problem of computationally intensive learning in this model by combining the recursive model estimation, fast inference, and on-line learning. Thus, we achieve real time frame clustering performance. Novel aspects of this method include an algorithm for the clustering of Gaussian mixtures, and the fast computation of the KL divergence between two mixtures of Gaussians. The efficiency and the performance of clustering and KL approximation methods are demonstrated. We also present novel video browsing tool based on the visualization of the variables in the generative model.
Control Theory and Fast Marching Techniques for Brain Connectivity Mapping
Authors: Emmanuel Prados, Stefano Soatto, Christophe Lenglet, Jean-Philippe Pons, Nicolas Wotawa, Rachid Deriche, Olivier D. Faugeras
Venue: CVPR
Year: 2006

We propose a novel, fast and robust technique for the computation of anatomical connectivity in the brain. Our approach exploits the information provided by Diffusion Tensor Magnetic Resonance Imaging (or DTI) and models the white matter by using Riemannian geometry and control theory. We show that it is possible, from a region of interest, to compute the geodesic distance to any other point and the associated optimal vector field. The latter can be used to trace shortest paths coinciding with neural fiber bundles. We also demonstrate that no explicit computation of those 3D curves is necessary to assess the degree of connectivity of the region of interest with the rest of the brain. We finally introduce a general local connectivity measure whose statistics along the optimal paths may be used to evaluate the degree of connectivity of any pair of voxels. All those quantities can be computed simultaneously in a Fast Marching framework, directly yielding the connectivity maps. Apart from being extremely fast, this method has other advantages such as the strict respect of the convoluted geometry of white matter, the fact that it is parameter-free, and its robustness to noise. We illustrate our technique by showing results on real and synthetic datasets. OurGCM(Geodesic Connectivity Mapping) algorithm is implemented in C++ and will be soon available on the web.
Single View Reconstruction of Curved Surfaces
Authors: Mukta Prasad, Andrew W. Fitzgibbon
Venue: CVPR
Year: 2006

Recent advances in single-view reconstruction (SVR) have been in modelling power (curved 2.5D surfaces) and automation (automatic photo pop-up). We extend SVR along both of these directions. We increase modelling power in several ways: (i) We represent general 3D surfaces, rather than 2.5D Monge patches; (ii) We describe a closed-form method to reconstruct a smooth surface from its image apparent contour, including multilocal singularities ("kidney-bean" self-occlusions); (iii) We show how to incorporate user-specified data such as surface normals, interpolation and approximation constraints; (iv) We show how this algorithm can be adapted to deal with surfaces of arbitrary genus. We also show how the modelling process can be automated for simple object shapes and views, using a-priori object class information. We demonstrate these advances on natural images drawn from a number of object classes.
Real-Time Visual SLAM with Resilience to Erratic Motion
Authors: Mark Pupilli, Andrew Calway
Venue: CVPR
Year: 2006

Simultaneous localisation and mapping using a single camera becomes difficult when erratic motions violate predictive motion models. This problem needs to be addressed when visual SLAM algorithms are transferred from robots or mobile vehicles onto hand-held or wearable devices. In this paper we describe a novel SLAM extension to a camera localisation algorithm based on particle filtering which provides resilience to erratic motion. The mapping component is based on auxiliary unscented Kalman filters coupled to the main particle filter via measurement covariances. This coupling allows the system to survive unpredictable motions such as camera shake, and enables a return to full SLAM operation once normal motion resumes. We present results demonstrating the effectiveness of the approach when operating within a desktop environment.
Motion Patterns: High-Level Representation of Natural Video Sequences
Authors: Duangmanee Putthividhya, Te-Won Lee
Venue: CVPR
Year: 2006

This work investigates the use of nonlinear dependencies in natural image sequence statistics to learn higher-order structures in natural videos. We propose a two-layer model that learns variance correlation between linear ICA coefficients and present a novel nonlinear representation of natural videos. The first layer performs a linear mapping from pixel values to ICA coefficients. In doing so, the spatiotemporal dynamics in natural videos are decomposed into a set of bases each encoding "independent motion." By assuming that the nonlinear dependency of ICA coefficients takes the form of variance correlation, the second layer learns the joint distribution of ICA sources that captures how these independent bases co-activate. Experimental results show that the abstract representation correspond to various activation patterns of bases with similar motion, hence the term "motion patterns." Our model offers a novel description of higher-order structures in natural videos. We illustrate the usefulness of the proposed representation in video segmentation and denoising tasks.
Real-time Image-Based Guidance Method for Lung-Cancer Assessment
Authors: Lav Rai, Scott A. Merritt, William E. Higgins
Venue: CVPR
Year: 2006

The assessment of lung cancer involves off-line threedimensional (3D) computed-tomography (CT) image assessment followed by live bronchoscopy. While standard bronchoscopy provides live video of information inside the lung airways, it does not give any information outside the airways. This results in a low procedure success rate. The success rate can be improved if the physician receives 3D CT-based image guidance. We describe a fast robust method that provides 3D CT-based image guidance during live bronchoscopy. The method enables a continuous registration between the 3D CT image space and the real bronchoscopic video. At a top level, it involves an interleaving of continuous video tracking and CT-video fine registration. During video tracking, the bronchoscope’s 3D motion is estimated using a point-based 3D-2D pose estimation method. Registration is performed via a warpingbased Gauss-Newton method that uses a normalized crosscorrelation- based cost for measuring similarity between the bronchoscopic video and CT image data. The method operates at a real-time rate of 10 frames per second, which is over an order of magnitude faster than past approaches. This real-time performance enables live guidance during a procedure. The method is incorporated into a computerbased system for image-guided bronchoscopy and has been applied to human lung-cancer patients. Results are presented for a phantom and lung-cancer patients. Keywords: 3D medical imaging, video tracking, image registration, virtual endoscopy, image-guided surgery
MRF's forMRI's: Bayesian Reconstruction of MR Images via Graph Cuts
Authors: Ashish Raj, Gurmeet Singh, Ramin Zabih
Venue: CVPR
Year: 2006

Markov Random Fields (MRF’s) are an effective way to impose spatial smoothness in computer vision. We describe an application of MRF’s to a non-traditional but important problem in medical imaging: the reconstruction of MR images from raw fourier data. This can be formulated as a linear inverse problem, where the goal is to find a spatially smooth solution while permitting discontinuities. Although it is easy to apply MRF’s to the MR reconstruction problem, the resulting energy minimization problem poses some interesting challenges. It lies outside of the class of energy functions that can be straightforwardlyminimized with graph cuts. We show how graph cuts can nonetheless be adapted to solve this problem, and provide some theoretical analysis of the properties of our algorithm. Experimentally, our method gives very strong performance, with a substantial improvement in SNR when compared with widely-used methods for MR reconstruction.
Segmentation by Level Sets and Symmetry
Authors: Tammy Riklin-Raviv, Nahum Kiryati, Nir A. Sochen
Venue: CVPR
Year: 2006

Shape symmetry is an important cue for image understanding. In the absence of more detailed prior shape information, segmentation can be significantly facilitated by symmetry. However, when symmetry is distorted by perspectivity, the detection of symmetry becomes non-trivial, thus complicating symmetry-aided segmentation. We present an original approach for segmentation of symmetrical objects accommodating perspective distortion. The key idea is the use of the replicative form induced by the symmetry for challenging segmentation tasks. This is accomplished by dynamic extraction of the object boundaries, based on the image gradients, gray levels or colors, concurrently with registration of the image symmetrical counterpart (e.g. reflection) to itself. The symmetrical counterpart of the evolving object contour supports the segmentation by resolving possible ambiguities due to noise, clutter, distortion, shadows, occlusions and assimilation with the background. The symmetry constraint is integrated in a comprehensive level-set functional for segmentation that determines the evolution of the delineating contour. The proposed framework is exemplified on various images of skewsymmetrical objects and its superiority over state of the art variational segmentation techniques is demonstrated.
Specular Flow and the Recovery of Surface Structure
Authors: Stefan Roth 0001, Michael J. Black
Venue: CVPR
Year: 2006

In scenes containing specular objects, the image motion observed by a moving camera may be an intermixed combination of optical flow resulting from diffuse reflectance (diffuse flow) and specular reflection (specular flow). Here, with few assumptions, we formalize the notion of specular flow, show how it relates to the 3D structure of the world, and develop an algorithm for estimating scene structure from 2D image motion. Unlike previous work on isolated specular highlights we use two image frames and estimate the semi-dense flow arising from the specular reflections of textured scenes. We parametrically model the image motion of a quadratic surface patch viewed from a moving camera. The flow is modeled as a probabilistic mixture of diffuse and specular components and the 3D shape is recovered using an Expectation-Maximization algorithm. Rather than treating specular reflections as noise to be removed or ignored, we show that the specular flow provides additional constraints on scene geometry that improve estimation of 3D structure when compared with reconstruction from diffuse flow alone. We demonstrate this for a set of synthetic and real sequences of mixed specular-diffuse objects.
Particle Video: Long-Range Motion Estimation using Point Trajectories
Authors: Peter Sand, Seth J. Teller
Venue: CVPR
Year: 2006

This paper describes a new approach to motion estimation in video. We represent video motion using a set of particles. Each particle is an image point sample with a longduration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and cannot be directly obtained using existing methods such as optical flow or feature tracking. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformations.
Multi-Aspect Detection of Articulated Objects
Authors: Edgar Seemann, Bastian Leibe, Bernt Schiele
Venue: CVPR
Year: 2006

A wide range of methods have been proposed to detect and recognize objects. However, effective and efficient multiviewpoint detection of objects is still in its infancy, since most current approaches can only handle single viewpoints or aspects. This paper proposes a general approach for multiaspect detection of objects. As the running example for detection we use pedestrians, which add another difficulty to the problem, namely human body articulations. Global appearance changes caused by different articulations and viewpoints of pedestrians are handled in a unified manner by a generalization of the Implicit Shape Model [5]. An important property of this new approach is to share local appearance across different articulations and viewpoints, therefore requiring relatively few training samples. The effectiveness of the approach is shown and compared to previous approaches on two datasets containing pedestrians with different articulations and from multiple viewpoints.
Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects
Authors: Ying Shan, Feng Han 0002, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2006

This paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. The key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. A novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. The individual processes employ a single objective function that is optimized using two nested AdaBoost loops. The outer AdaBoost loop is used to select discriminative exemplars and the inner AdaBoost is used to select discriminative features on the selected exemplars. The proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. Also, our approach fully complies with the standard AdaBoost-based object detection framework in terms of real-time implementation. Experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.
Shape from Dynamic Texture for Planes
Authors: Yaser Sheikh, Niels C. Haering, Mubarak Shah
Venue: CVPR
Year: 2006

We propose a method for recovering the affine geometry of a dynamically textured plane from a video sequence taken by an uncalibrated, fixed, perspective camera. Some instances of approximately planar surfaces that are coated with a dynamic texture include large water bodies, (such as lakes and oceans), heavy traffic, dense crowds, escalators, and foliage in the wind. Under the assumption of translational dynamic textures, we propose a direct algorithm for the estimation of the inter-frame elation that does not require explicit identification of texels or movetons. In addition, we develop a general algorithm for recovering the affine geometry of homogeneous dynamic textures by identifying a constraint on the expected values of motion magnitudes. We report experimental results on several real videos of dynamic texture found in the world.
Spatial Reflectance Recovery under Complex Illumination from Sparse Images
Authors: Li Shen 0006, Haruo Takemura
Venue: CVPR
Year: 2006

A major challenge in inverse reflectometry is the acquisition of spatially varying materials. In this paper, we introduce a method to recover spatial reflectance from a sparse set of images under general illumination. Specifically, we first remove the high-frequency varying diffuse reflection term by using a low-order spherical harmonic approximation. This allows us to directly estimate the specular properties with a cluster fitting process, which simplifies the fitting processes and addresses the problem of data inadequacy for sparse images. As a result, we can reconstruct a truly spatially varying BRDF model of the surface from less than 10 images. Experimental results will be presented in order to demonstrate the effectiveness of the proposed algorithm.
Learning Temporal Sequence Model from Partially Labeled Data
Authors: Yifan Shi, Aaron F. Bobick, Irfan A. Essa
Venue: CVPR
Year: 2006

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure - the nodes - are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types - vision and inertial measurements - in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.
Video Completion by Motion Field Transfer
Authors: Takaaki Shiratori, Yasuyuki Matsushita, Xiaoou Tang, Sing Bing Kang
Venue: CVPR
Year: 2006

Existing methods for video completion typically rely on periodic color transitions, layer extraction, or temporally local motion. However, periodicity may be imperceptible or absent, layer extraction is difficult, and temporally local motion cannot handle large holes. This paper presents a new approach for video completion using motion field transfer to avoid such problems. Unlike prior methods, we fill in missing video parts by sampling spatio-temporal patches of local motion instead of directly sampling color. Once the local motion field has been computed within the missing parts of the video, color can then be propagated to produce a seamless hole-free video. We have validated our method on many videos spanning a variety of scenes. We can also use the same approach to perform frame interpolation using motion fields from different videos.