OpenRaiser/Intern-Atlas huggingface dataset:
Sparse shape composition: A new framework for shape prior modeling
Authors: Shaoting Zhang 0001, Yiqiang Zhan, Maneesh Dewan, Junzhou Huang, Dimitris N. Metaxas, Xiang Sean Zhou
Venue: CVPR
Year: 2011

Image appearance cues are often used to derive object shapes, which is usually one of the key steps of image understanding tasks. However, when image appearance cues are weak or misleading, shape priors become critical to infer and refine the shape derived by these appearance cues. Effective modeling of shape priors is challenging because: 1) shape variation is complex and cannot always be modeled by a parametric probability distribution; 2) a shape instance derived from image appearance cues (input shape) may have gross errors; and 3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, training shapes are adaptively composed to infer/refine an input shape. The a-priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: 1) the input shape can be approximately represented by a sparse linear combination of training shapes; 2) parts of the input shape may contain gross errors but such errors are usually sparse. Using L1 norm relaxation, our model is formulated as a convex optimization problem, which is solved by an efficient alternating minimization framework. Our method is extensively validated on two real world medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies.
Joint face alignment with a generic deformable face model
Authors: Cong Zhao 0005, Wai-kuen Cham, Xiaogang Wang 0001
Venue: CVPR
Year: 2011

As having multiple images of an object is practically convenient nowadays, to jointly align them is important for subsequent studies and a wide range of applications. In this paper, we propose a model-based approach to jointly align a batch of images of a face undergoing a variety of geometric and appearance variations. The principal idea is to model the non-rigid deformation of a face by means of a learned deformable model. Different from existing model-based methods such as Active Appearance Models, the proposed one does not rely on an accurate appearance model built from a training set. We propose a robust fitting method that simultaneously identifies the appearance space of the input face and brings the images into alignment. The experiments conducted on images in the wild in comparison with competing methods demonstrate the effectiveness of our method in joint alignment of complex objects like human faces.
A robust method for vector field learning with application to mismatch removing
Authors: Ji Zhao 0001, Jiayi Ma 0001, Jinwen Tian, Jie Ma, Dazhi Zhang
Venue: CVPR
Year: 2011

We propose a method for vector field learning with outliers, called vector field consensus (VFC). It could distinguish inliers from outliers and learn a vector field fitting for the inliers simultaneously. A prior is taken to force the smoothness of the field, which is based on the Tiknonov regularization in vector-valued reproducing kernel Hilbert space. Under a Bayesian framework, we associate each sample with a latent variable which indicates whether it is an inlier, and then formulate the problem as maximum a posteriori problem and use Expectation Maximization algorithm to solve it. The proposed method possesses two characteristics: 1) robust to outliers, and being able to tolerate 90% outliers and even more, 2) computationally efficient. As an application, we apply VFC to solve the problem of mismatch removing. The results demonstrate that our method outperforms many state-of-the-art methods, and it is very robust.
Person re-identification by probabilistic relative distance comparison
Authors: Wei-Shi Zheng 0001, Shaogang Gong, Tao Xiang 0002
Venue: CVPR
Year: 2011

Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features that are both distinctive and stable under appearance changes. However, most visual features and their combinations under realistic conditions are neither stable nor distinctive thus should not be used indiscriminately. In this paper, we propose to formulate person re-identification as a distance learning problem, which aims to learn the optimal distance that can maximises matching accuracy regardless the choice of representation. To that end, we introduce a novel Probabilistic Relative Distance Comparison (PRDC) model, which differs from most existing distance learning methods in that, rather than minimising intra-class variation whilst maximising intra-class variation, it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair. This makes our model more tolerant to appearance changes and less susceptible to model over-fitting. Extensive experiments are carried out to demonstrate that 1) by formulating the person re-identification problem as a distance learning problem, notable improvement on matching accuracy can be obtained against conventional person re-identification techniques, which is particularly significant when the training sample size is small; and 2) our PRDC outperforms not only existing distance learning methods but also alternative learning methods based on boosting and learning to rank.
Structure from motion blur in low light
Authors: Yali Zheng, Shohei Nobuhara, Yaser Sheikh
Venue: CVPR
Year: 2011

In theory, the precision of structure from motion estimation is known to increase as camera motion increases. In practice, larger camera motions induce motion blur, particularly in low light where longer exposures are needed. If the camera center moves during exposure, the trajectory traces in a motion-blurred image encode the underlying 3D structure of points and the motion of the camera. In this paper, we propose an algorithm to explicitly estimate the 3D structure of point light sources and camera motion from a motion-blurred image in a low light scene with point light sources. The algorithm identifies extremal points of the traces mapped out by the point sources in the image and classifies them into start and end sets. Each trace is charted out incrementally using local curvature, providing correspondences between start and end points. We use these correspondences to obtain an initial estimate of the epipolar geometry embedded in a motion-blurred image. The reconstruction and the 2D traces are used to estimate the motion of the camera during the interval of capture, and multiple view bundle adjustment is applied to refine the estimates.
A branch and contract algorithm for globally optimal fundamental matrix estimation
Authors: Yinqiang Zheng, Shigeki Sugimoto, Masatoshi Okutomi
Venue: CVPR
Year: 2011

We propose a unified branch and contract method to estimate the fundamental matrix with guaranteed global optimality, by minimizing either the Sampson error or the point to epipolar line distance, and explicitly handling the rank-2 constraint and scale ambiguity. Based on a novel denominator linearization strategy, the fundamental matrix estimation problem can be transformed into an equivalent problem that involves 9 squared univariate, 12 bilinear and 6 trilin-ear terms. We build tight convex and concave relaxations for these nonconvex terms and solve the problem deterministically under the branch and bound framework. For acceleration, a bound contraction mechanism is introduced to reduce the size of the branching region at the root node. Given high-quality correspondences and proper data normalization, our experiments show that the state-of-the-art locally optimal methods generally converge to the globally optimal solution. However, they indeed have the risk of being trapped into local minimum in case of noise. As another important experimental result, we also demonstrate, from the viewpoint of global optimization, that the point to epipolar line distance is slightly inferior to the Sampson error in case of drastically varying object scales across two views.
O(N) implicit subspace embedding for unsupervised multi-scale image segmentation
Authors: Hongbo Zhou 0001, Qiang Cheng 0001
Venue: CVPR
Year: 2011

Subspace embedding is a powerful tool for extracting salient information from matrix, and it has numerous applications in image processing. However, its applicability has been severely limited by the computational complexity of O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) (N is the number of the points) which usually arises in explicitly evaluating the eigenvalues and eigenvectors. In this paper, we propose an implicit subspace embedding method which avoids explicitly evaluating the eigenvectors. Also, we show that this method can be seamlessly incorporated into the unsupervised multi-scale image segmentation framework and the resulted algorithm has a running time of genuine O(N). Moreover, we can explicitly determine the number of iterations for the algorithm by estimating the desired size of the subspace, which also controls the amount of information we want to extract for this unsupervised learning. We performed extensive experiments to verify the validity and effectiveness of our method, and we conclude that it only requires less than 120 seconds (CPU 3.2G and memory 16G) to cut a 1000∗1000 color image and orders of magnitude faster than original multi-scale image segmentation with explicit spectral decomposition while maintaining the same or a better segmentation quality.
2.5D building modeling with topology control
Authors: Qian-Yi Zhou, Ulrich Neumann
Venue: CVPR
Year: 2011

2.5D building reconstruction aims at creating building models composed of complex roofs and vertical walls. In this paper, we define 2.5D building topology as a set of roof features, wall features, and point features; together with the associations between them. Based on this definition, we extend 2.5D dual contouring into a 2.5D modeling method with topology control. Comparing with the previous method, we put less restrictions on the adaptive simplification process. We show results under intense geometry simplifications. Our results preserve significant topology structures while the number of triangles is comparable to that of manually created models or primitive-based models.
Hierarchical anatomical brain networks for MCI prediction by partial least square analysis
Authors: Luping Zhou, Yaping Wang, Yang Li 0010, Pew-Thian Yap, Dinggang Shen
Venue: CVPR
Year: 2011

Owning to its clinical accessibility, T1-weighted MRI has been extensively studied for the prediction of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The tissue volumes of GM, WM and CSF are the most commonly used measures for MCI and AD prediction. We note that disease-induced structural changes may not happen at isolated spots, but in several inter-related regions. Therefore, in this paper we propose to directly extract the inter-region connectivity based features for MCI prediction. This involves constructing a brain network for each subject, with each node representing an ROI and each edge representing regional interactions. This network is also built hierarchically to improve the robustness of classification. Compared with conventional methods, our approach produces a significant larger pool of features, which if improperly dealt with, will result in intractability when used for classifier training. Therefore based on the characteristics of the network features, we employ Partial Least Square analysis to efficiently reduce the feature dimensionality to a manageable level while at the same time preserving discriminative information as much as possible. Our experiment demonstrates that without requiring any new information in addition to T1-weighted images, the prediction accuracy of MCI is statistically improved.
3D motion reconstruction for real-world camera motion
Authors: Yingying Zhu 0004, Mark Cox, Simon Lucey
Venue: CVPR
Year: 2011

This paper addresses the problem of 3D motion reconstruction from a series of 2D projections under low reconstructibility. Reconstructibility defines the accuracy of a 3D reconstruction from 2D projections given a particular trajectory basis, 3D point trajectory, and 3D camera center trajectory. Reconstructibility accuracy is inherently related to the correlation between point and camera trajectories. Poor correlation leads to good reconstruction, high correlation leads to poor reconstruction. Unfortunately, in most real-world situations involving non-rigid objects (e.g. bodies), camera and point motions are highly correlated (i.e., slow and smooth) resulting in poor reconstructibility. In this paper, we propose a novel approach for 3D motion reconstruction of non-rigid body motion in the presence of real-world camera motion. Specifically we: (i) propose the inclusion of a small number of keyframes in the video sequence from which 3D coordinates are inferred/estimated to circumvent ambiguities between point and camera motion, and (ii) employ a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalty term to enforce a sparsity constraint on the trajectory basis coefficients so as to ensure our reconstructions are consistent with the natural compressibility of human motion. We demonstrate impressive 3D motion reconstruction for 2D projection sequences with hitherto low reconstructibility.
High-precision localization using visual landmarks fused with range data
Authors: Zhiwei Zhu, Han-Pang Chiu, Taragay Oskiper, Saad Ali, Raia Hadsell, Supun Samarasekera, Rakesh Kumar 0001
Venue: CVPR
Year: 2011

Visual landmark matching with a pre-built landmark database is a popular technique for localization. Traditionally, landmark database was built with visual odometry system, and the 3D information of each visual landmark is reconstructed from video. Due to the drift of the visual odometry system, a global consistent landmark database is difficult to build, and the inaccuracy of each 3D landmark limits the performance of landmark matching. In this paper, we demonstrated that with the use of precise 3D Li-dar range data, we are able to build a global consistent database of high precision 3D visual landmarks, which improves the landmark matching accuracy dramatically. In order to further improve the accuracy and robustness, landmark matching is fused with a multi-stereo based visual odometry system to estimate the camera pose in two aspects. First, a local visual odometry trajectory based consistency check is performed to reject some bad landmark matchings or those with large errors, and then a kalman filtering is used to further smooth out some landmark matching errors. Finally, a disk-cache-mechanism is proposed to obtain the real-time performance when the size of the landmark grows for a large-scale area. A week-long real time live marine training experiments have demonstrated the high-precision and robustness of our proposed system.
Group action induced distances for averaging and clustering Linear Dynamical Systems with applications to the analysis of dynamic scenes
Authors: Bijan Afsari, Rizwan Chaudhry, Avinash Ravichandran, René Vidal
Venue: CVPR
Year: 2012

We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of orthogonal matrices on the space of statespace realizations of LDSs. This distance can be efficiently computed for large-scale problems, hence it is suitable for applications in the analysis of dynamic visual scenes and other high dimensional time series. Based on this distance we devise a simple LDS averaging algorithm, which can be used for classification and clustering of time-series data. We test the validity as well as the performance of our group-action based distance on synthetic as well as real data and provide comparison with state-of-the-art methods.
Multi view registration for novelty/background separation
Authors: Omid Aghazadeh, Josephine Sullivan, Stefan Carlsson
Venue: CVPR
Year: 2012

We propose a system for the automatic segmentation of novelties from the background in scenarios where multiple images of the same environment are available e.g. obtained by wearable visual cameras. Our method finds the pixels in a query image corresponding to the underlying background environment by comparing it to reference images of the same scene. This is achieved despite the fact that all the images may have different viewpoints, significantly different illumination conditions and contain different objects - cars, people, bicycles, etc. - occluding the background. We estimate the probability of each pixel, in the query image, belonging to the background by computing its appearance inconsistency to the multiple reference images. We then, produce multiple segmentations of the query image using an iterated graph cuts algorithm, initializing from these estimated probabilities and consecutively combine these segmentations to come up with a final segmentation of the background. Detection of the background in turn highlights the novel pixels. We demonstrate the effectiveness of our approach on a challenging outdoors data set.
A theory of multi-layer flat refractive geometry
Authors: Amit K. Agrawal, Srikumar Ramalingam, Yuichi Taguchi, Visesh Chari
Venue: CVPR
Year: 2012

Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm [15] can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.
Sum-product networks for modeling activities with stochastic structure
Authors: Mohamed R. Amer, Sinisa Todorovic
Venue: CVPR
Year: 2012

This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable number of actors. We demonstrate that modeling aggregate counts of visual words is surprisingly expressive enough for such a challenging recognition task. An activity is represented by a sum-product network (SPN). SPN is a mixture of bags-of-words (BoWs) with exponentially many mixture components, where subcomponents are reused by larger ones. SPN consists of terminal nodes representing BoWs, and product and sum nodes organized in a number of layers. The products are aimed at encoding particular configurations of primitive actions, and the sums serve to capture their alternative configurations. The connectivity of SPN and parameters of BoW distributions are learned under weak supervision using the EM algorithm. SPN inference amounts to parsing the SPN graph, which yields the most probable explanation (MPE) of the video in terms of activity detection and localization. SPN inference has linear complexity in the number of nodes, under fairly general conditions, enabling fast and scalable recognition. A new Volleyball dataset is compiled and annotated for evaluation. Our classification accuracy and localization precision and recall are superior to those of the state-of-the-art on the benchmark and our Volleyball datasets.
Enhancing underwater images and videos by fusion
Authors: Cosmin Ancuti, Codruta O. Ancuti, Tom Haber, Philippe Bekaert
Venue: CVPR
Year: 2012

This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.
Discrete-continuous optimization for multi-target tracking
Authors: Anton Andriyenko, Konrad Schindler, Stefan Roth 0001
Venue: CVPR
Year: 2012

The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete-continuous optimization problem that handles each aspect in its natural domain and allows leveraging powerful methods for multi-model fitting. Data association is performed using discrete optimization with label costs, yielding near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-the-art performance on several standard datasets.