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Face recognition with learning-based descriptor

Face recognition with learning-based descriptor

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conference on computer vision and pattern recognition (CVPR)

作者： Zhimin Cao Qi Yin Xiaoou Tang Jian Sun Chinese University of Hong Kong Hong Kong China Institute for Theoretical Computer Science ITCS Tsinghua University China Shenzhen Institutes of Advanced Technology Chinese Academy and Sciences China Microsoft Research Asia China

We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45% recognition rate), while maintaining excellent compactness, simplicity, and generalization aability across different datasets.

关键词： Face recognition Encoding Histograms Principal component analysis Robustness Face detection Unsupervised learning Design methodology Asia Lighting

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A novel riemannian framework for shape analysis of 3D objects

A novel riemannian framework for shape analysis of 3D object...

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conference on computer vision and pattern recognition (CVPR)

作者： Sebastian Kurtek Eric Klassen Zhaohua Ding Anuj Srivastava Department of Statistics Florida State University Tallahassee FL USA Institute of Imaging Science Vanderbilt University Nashville TN USA

In this paper we introduce a novel Riemannian framework for shape analysis of parameterized surfaces. We derive a distance function between any two surfaces that is invariant to rigid motion, global scaling, and re-parametrization. It is the last part that presents the main difficulty. Our solution to this problem is twofold: (1) we define a special representation, called a q-map, to represent each surface, and (2) we develop a gradient-based algorithm to optimize over different re-parameterizations of a surface. The second step is akin to deforming the mesh on a fixed surface to optimize its placement. (This is different from the current methods that treat the given meshes as fixed.) Under the chosen representation, with the L 2 metric, the action of the re-parametrization group is by isometries. This results in, to our knowledge, the first Riemannian distance between parameterized surfaces to have all the desired invariances. We demonstrate this framework with several examples using some toy shapes, and real data with anatomical structures, and cropped facial surfaces. We also successfully demonstrate clustering and classification of these objects under the proposed metric.

关键词： Shape Surface treatment Image analysis Surface reconstruction Statistical analysis Anatomical structure Humans Mathematics Optimization methods computer displays

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Beyond trees: MRF inference via outer-planar decomposition

Beyond trees: MRF inference via outer-planar decomposition

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conference on computer vision and pattern recognition (CVPR)

作者： Dhruv Batra A. C. Gallagher Devi Parikh Tsuhan Chen Carnegie Mellon University USA Eastman Kodak (Japan) Limited USA TTI-C USA Cornell University USA

Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. trees), or approximate algorithms (e.g. Loopy Belief Propagation (BP) and Tree-reweighted (TRW) methods). This paper presents a unifying perspective of these approximate techniques called "Decomposition Methods". These are methods that decompose the given problem over a graph into tractable subproblems over subgraphs and then employ message passing over these subgraphs to merge the solutions of the subproblems into a global solution. This provides a new way of thinking about BP and TRW as successive steps in a hierarchy of decomposition methods. Using this framework, we take a principled first step towards extending this hierarchy beyond trees. We leverage a new class of graphs amenable to exact inference, called outer-planar graphs, and propose an approximate inference algorithm called Outer-Planar Decomposition (OPD). OPD is a strict generalization of BP and TRW, and contains both of them as special cases. Our experiments show that this extension beyond trees is indeed very powerful -OPD outperforms current state-of-art inference methods on hard non-submodular synthetic problems and is competitive on real computer vision applications.

关键词： Tree graphs Inference algorithms Message passing Labeling Markov random fields Belief propagation NP-hard problem computer vision Application software Writing

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Label propagation in video sequences

Label propagation in video sequences

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conference on computer vision and pattern recognition (CVPR)

作者： Vijay Badrinarayanan Fabio Galasso Roberto Cipolla University of Cambridge UK

This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled video sequences along with the class probabilities at each pixel. Our novel algorithm provides an essential tool to reduce tedious hand labelling of video sequences, thus producing copious amounts of useable ground truth data. A novel application of this algorithm is in semi-supervised learning of discriminative classifiers for video segmentation and scene parsing. The label propagation scheme can be based on pixel-wise correspondences obtained from motion estimation, image patch based similarities as seen in epitomic models or even the more recent, semantically consistent hierarchical regions. We compare the abilities of each of these variants, both via quantitative and qualitative studies against ground truth data. We then report studies on a state of the art Random forest classifier based video segmentation scheme, trained using fully ground truth data and with data obtained from label propagation. The results of this study strongly support and encourage the use of the proposed label propagation algorithm.

关键词： Video sequences Labeling Hidden Markov models Semisupervised learning Pixel Machine learning Image motion analysis computer vision Image databases Graphical models

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Model-based respiratory motion compensation for image-guided cardiac interventions

Model-based respiratory motion compensation for image-guided...

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conference on computer vision and pattern recognition (CVPR)

作者： Matthias Schneider Hari Sundar Rui Liao Joachim Hornegger Chenyang Xu Pattern Recognition Lab University of Erlangen Nuremberg Erlangen Germany Siemens AG Corporate Research and Development Princeton NJ USA

In this paper we propose and validate a PCA-based respiratory motion model for motion compensation during image-guided cardiac interventions. In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiac gated biplane cineangiogram, and used to build a respiratory motion model. This motion model is subsequently used as a prior within the intraoperative registration process for motion compensation to restrict the search space. Our hypothesis is that the use of this model-constrained registration increases the robustness and registration accuracy, especially for weak data constraints such as low signal-to-noise ratio, the lack of contrast information, or an intraoperative monoplane setting. This allows for reducing radiation exposure without compromising on registration accuracy. Synthetic data as well as phantom and clinical datasets have been used to validate the model-based registration in terms of registration accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with a 3-D TRE of less than 2 mm for both monoplane images and intraprocedure settings with missing contrast information based on 2-D guidewire tracking, which makes it feasible for motion correction in clinical procedures.

关键词： Motion compensation Magnetic resonance imaging Arteries Robustness Computed tomography Electrocardiography Transducers Principal component analysis Image segmentation Surges

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Live dense reconstruction with a single moving camera

Live dense reconstruction with a single moving camera

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conference on computer vision and pattern recognition (CVPR)

作者： Richard A. Newcombe Andrew J. Davison Department of Computing Imperial College London London UK

We present a method which enables rapid and dense reconstruction of scenes browsed by a single live camera. We take point-based real-time structure from motion (SFM) as our starting point, generating accurate 3D camera pose estimates and a sparse point cloud. Our main novel contribution is to use an approximate but smooth base mesh generated from the SFM to predict the view at a bundle of poses around automatically selected reference frames spanning the scene, and then warp the base mesh into highly accurate depth maps based on view-predictive optical flow and a constrained scene flow update. The quality of the resulting depth maps means that a convincing global scene model can be obtained simply by placing them side by side and removing overlapping regions. We show that a cluttered indoor environment can be reconstructed from a live hand-held camera in a few seconds, with all processing performed by current desktop hardware. Real-time monocular dense reconstruction opens up many application areas, and we demonstrate both real-time novel view synthesis and advanced augmented reality where augmentations interact physically with the 3D scene and are correctly clipped by occlusions.

关键词： Cameras Layout Image reconstruction Surface reconstruction Clouds Image motion analysis Augmented reality Robot vision systems Simultaneous localization and mapping Machine vision

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Latent hierarchical structural learning for object detection

Latent hierarchical structural learning for object detection

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conference on computer vision and pattern recognition (CVPR)

作者： Long Zhu Yuanhao Chen Alan Yuille William Freeman CSAIL MIT USA Department of Statistics UCLA USA

We present a latent hierarchical structural learning method for object detection. An object is represented by a mixture of hierarchical tree models where the nodes represent object parts. The nodes can move spatially to allow both local and global shape deformations. The models can be trained discriminatively using latent structural SVM learning, where the latent variables are the node positions and the mixture component. But current learning methods are slow, due to the large number of parameters and latent variables, and have been restricted to hierarchies with two layers. In this paper we describe an incremental concave-convex procedure (iCCCP) which allows us to learn both two and three layer models efficiently. We show that iCCCP leads to a simple training algorithm which avoids complex multi-stage layer-wise training, careful part selection, and achieves good performance without requiring elaborate initialization. We perform object detection using our learnt models and obtain performance comparable with state-of-the-art methods when evaluated on challenging public PASCAL datasets. We demonstrate the advantages of three layer hierarchies - outperforming Felzenszwalb et al.'s two layer models on all 20 classes.

关键词： Object detection Training data Support vector machines Learning systems Shape Statistics Performance evaluation Joining processes computer vision Context modeling

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Geodesic graph cut for interactive image segmentation

Geodesic graph cut for interactive image segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Brian L. Price Bryan Morse Scott Cohen Brigham Young University USA Adobe Systems Inc. USA

Interactive segmentation is useful for selecting objects of interest in images and continues to be a topic of much study. Methods that grow regions from foreground/background seeds, such as the recent geodesic segmentation approach, avoid the boundary-length bias of graph-cut methods but have their own bias towards minimizing paths to the seeds, resulting in increased sensitivity to seed placement. The lack of edge modeling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. This paper presents a method for combining geodesic-distance information with edge information in a graphcut optimization framework, leveraging the complementary strengths of each. Rather than a fixed combination we use the distinctiveness of the foreground/background color models to predict the effectiveness of the geodesic distance term and adjust the weighting accordingly. We also introduce a spatially varying weighting that decreases the potential for shortcutting in object interiors while transferring greater control to the edge term for better localization near object boundaries. Results show our method is less prone to shortcutting than typical graph cut methods while being less sensitive to seed placement and better at edge localization than geodesic methods. This leads to increased segmentation accuracy and reduced effort on the part of the user.

关键词： Image segmentation Optimization methods Color Predictive models Programmable control Adaptive control Automatic control computer vision Labeling Costs

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Simultaneous pose, correspondence and non-rigid shape

Simultaneous pose, correspondence and non-rigid shape

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conference on computer vision and pattern recognition (CVPR)

作者： Jordi Sánchez-Riera Jonas Östlund Pascal Fua Francesc Moreno-Noguer Perception Team INRIA Grenoble France EPFL - CVLab Lausanne Switzerland Informàtica Industrial (CSIC-UPC) Institut de Robòtica i Barcelona Spain

Recent works have shown that 3D shape of non-rigid surfaces can be accurately retrieved from a single image given a set of 3D-to-2D correspondences between that image and another one for which the shape is known. However, existing approaches assume that such correspondences can be readily established, which is not necessarily true when large deformations produce significant appearance changes between the input and the reference images. Furthermore, it is either assumed that the pose of the camera is known, or the estimated solution is pose-ambiguous. In this paper we relax all these assumptions and, given a set of 3D and 2D unmatched points, we present an approach to simultaneously solve their correspondences, compute the camera pose and retrieve the shape of the surface in the input image. This is achieved by introducing weak priors on the pose and shape that we model as Gaussian Mixtures. By combining them into a Kalman filter we can progressively reduce the number of 2D candidates that can be potentially matched to each 3D point, while pose and shape are refined. This lets us to perform a complete and efficient exploration of the solution space and retain the best solution.

关键词： Shape Cameras Image reconstruction Image retrieval Surface reconstruction computer vision Application software Orbital robotics Robot vision systems computer graphics

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Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes

Chaotic invariants of Lagrangian particle trajectories for a...

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conference on computer vision and pattern recognition (CVPR)

作者： Shandong Wu Brian E. Moore Mubarak Shah Computer Vision Lab University of Central Florida USA Department of Mathematics University of Central Florida USA

A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique utilization of particle trajectories for modeling crowded scenes, in which we propose new and efficient representative trajectories for modeling arbitrarily complicated crowd flows. Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting anomalies. Third, a probabilistic framework for anomaly detection and localization is formulated. The overall work-flow begins with particle advection based on optical flow. Then particle trajectories are clustered to obtain representative trajectories for a crowd flow. Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension. Probabilistic model is learned from these chaotic feature set, and finally, a maximum likelihood estimation criterion is adopted to identify a query video of a scene as normal or abnormal. Furthermore, an effective anomaly localization algorithm is designed to locate the position and size of an anomaly. Experiments are conducted on known crowd data set, and results show that our method achieves higher accuracy in anomaly detection and can effectively localize anomalies.

关键词： Chaos Lagrangian functions Layout Hidden Markov models computer vision Event detection Image motion analysis Motion analysis Humans Mathematics

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