pwc-archive/papers-with-abstracts huggingface dataset:
Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration
Authors: Rui Shi, Wei Zeng, Zhengyu Su, Hanna Damasio, Zhonglin Lu, Yalin Wang, Shing-Tung Yau, Xianfeng GU
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquer this problem by changing the Riemannian metric on the target surface to a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on the Ricci flow method and the method is general and robust. We apply our algorithm to study constrained human brain surface registration problem. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, and achieve relative high performance when evaluated with some popular cortical surface registration evaluation standards.
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
Authors: Ravi Garg, Anastasios Roussos, Lourdes Agapito
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D Reconstruction, GPU

This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion ( NRS f M ) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to NRS f M , which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.
Fusing Depth from Defocus and Stereo with Coded Apertures
Authors: Yuichi Takeda, Shinsaku Hiura, Kosuke Sato
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

In this paper we propose a novel depth measurement method by fusing depth from defocus (DFD) and stereo. One of the problems of passive stereo method is the difficulty of finding correct correspondence between images when an object has a repetitive pattern or edges parallel to the epipolar line. On the other hand, the accuracy of DFD method is inherently limited by the effective diameter of the lens. Therefore, we propose the fusion of stereo method and DFD by giving different focus distances for left and right cameras of a stereo camera with coded apertures. Two types of depth cues, defocus and disparity, are naturally integrated by the magnification and phase shift of a single point spread function (PSF) per camera. In this paper we give the proof of the proportional relationship between the diameter of defocus and disparity which makes the calibration easy. We also show the outstanding performance of our method which has both advantages of two depth cues through simulation and actual experiments.
A Non-parametric Framework for Document Bleed-through Removal
Authors: Roisin Rowley-Brooke, Francois Pitie, Anil Kokaram
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification, Image Inpainting

This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems
Authors: J. Kappes, B. Andres, F. Hamprecht, C. Schnorr, S. Nowozin, D. Batra, S. Kim, B. Kausler, J. Lellmann, N. Komodakis, C. Rother
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.
Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction
Authors: Jian Cheng, Cong Leng, Jiaxiang Wu, Hainan Cui, Hanqing Lu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. Therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.
Predicting Matchability
Authors: Wilfried Hartmann, Michal Havlena, Konrad Schindler
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

The initial steps of many computer vision algorithms are interest point extraction and matching. In larger image sets the pairwise matching of interest point descriptors between images is an important bottleneck. For each descriptor in one image the (approximate) nearest neighbor in the other one has to be found and checked against the second-nearest neighbor to ensure the correspondence is unambiguous. Here, we asked the question how to best decimate the list of interest points without losing matches, i.e. we aim to speed up matching by filtering out, in advance, those points which would not survive the matching stage. It turns out that the best filtering criterion is not the response of the interest point detector, which in fact is not surprising: the goal of detection are repeatable and well-localized points, whereas the objective of the selection are points whose descriptors can be matched successfully. We show that one can in fact learn to predict which descriptors are matchable, and thus reduce the number of interest points significantly without losing too many matches. We show that this strategy, as simple as it is, greatly improves the matching success with the same number of points per image. Moreover, we embed the prediction in a state-of-the-art Structure-from-Motion pipeline and demonstrate that it also outperforms other selection methods at system level.
Trinocular Geometry Revisited
Authors: Jean Ponce, Martial Hebert
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

When do the visual rays associated with triplets of point correspondences converge, that is, intersect in a common point? Classical models of trinocular geometry based on the fundamental matrices and trifocal tensor associated with the corresponding cameras only provide partial answers to this fundamental question, in large part because of underlying, but seldom explicit, general configuration assumptions. This paper uses elementary tools from projective line geometry to provide necessary and sufficient geometric and analytical conditions for convergence in terms of transversals to triplets of visual rays, without any such assumptions. In turn, this yields a novel and simple minimal parameterization of trinocular geometry for cameras with non-collinear or collinear pinholes.
Critical Configurations For Radial Distortion Self-Calibration
Authors: Changchang Wu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

In this paper, we study the configurations of motion and structure that lead to inherent ambiguities in radial distortion estimation  (or 3D reconstruction with unknown radial distortions). By analyzing the motion field of radially distorted images, we solve for critical surface pairs that can lead to the same motion field under different radial distortions and possibly different camera motions. We study the properties of the discovered critical configurations and discuss the practically important configurations that often occur in real applications. We demonstrate the impact of the radial distortion ambiguity on multi-view reconstruction with synthetic experiments and real experiments.
Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion
Authors: Yubin Kuang, Jan E. Solem, Fredrik Kahl, Kalle Astrom
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this paper, we study the problems of estimating relative pose between two cameras in the presence of radial distortion.  Specifically, we consider minimal problems where one of the cameras has no or known radial distortion.  There are three useful cases for this setup with a single unknown distortion: (i) fundamental matrix estimation where the two cameras are uncalibrated, (ii) essential matrix estimation for a partially calibrated camera pair, (iii) essential matrix estimation for one calibrated camera and one camera with unknown focal length. We study the parameterization of these three problems and derive fast polynomial solvers based on Grobner basis methods.  We demonstrate the numerical stability of the solvers on synthetic data.  The minimal solvers have also been applied to real imagery with convincing results
Reconstructing PASCAL VOC
Authors: Sara Vicente, Joao Carreira, Lourdes Agapito, Jorge Batista
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object

We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion, then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets, PASCAL VOC. Our results may re-stimulate once popular geometry-oriented model-based recognition approaches.
Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation
Authors: Fabio Galasso, Margret Keuper, Thomas Brox, Bernt Schiele
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

Computational and memory costs restrict spectral techniques to rather small graphs, which is a serious limitation especially in video segmentation. In this paper, we propose the use of a reduced graph based on superpixels. In contrast to previous work, the reduced graph is reweighted such that the resulting segmentation is equivalent, under certain assumptions, to that of the full graph. We consider equivalence in terms of the normalized cut and of its spectral clustering relaxation. The proposed method reduces runtime and memory consumption and yields on par results in image and video segmentation. Further, it enables an efficient data representation and update for a new streaming video segmentation approach that also achieves state-of-the-art performance.
Weakly Supervised Multiclass Video Segmentation
Authors: Xiao Liu, DaCheng Tao, Mingli Song, Ying Ruan, Chun Chen, Jiajun Bu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Segmentation, Semantic Similarity, Semantic Textual Similarity, Video Segmentation, Video Semantic Segmentation, Video Understanding, Weakly supervised segmentation

The desire of enabling computers to learn semantic concepts from large quantities of Internet videos has motivated increasing interests on semantic video understanding, while video segmentation is important yet challenging for understanding videos. The main difficulty of video segmentation arises from the burden of labeling training samples, making the problem largely unsolved. In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation. Whereas previous weakly supervised video segmentation methods have been limited to the two-class case, our proposed scheme focuses on more challenging multiclass video segmentation, which finds a semantically meaningful label for every pixel in a video. Our scheme enjoys several favorable properties when compared with conventional methods. First, a weakly supervised hashing procedure is carried out to handle both metric and semantic similarity. Second, the proposed nearest neighbor-based label transfer algorithm effectively avoids overfitting caused by weakly supervised data. Third, a multi-video graph model is built to encourage smoothness between regions that are spatiotemporally adjacent and similar in appearance. We demonstrate the effectiveness of the proposed scheme by comparing it with several other state-of-the-art weakly supervised segmentation methods on one new Wild8 dataset and two other publicly available datasets.
Propagated Image Filtering
Authors: Jen-Hao Rick Chang, Yu-Chiang Frank Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Denoising, Image Denoising

In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions. In particular, our filter does not to utilize explicit spatial kernel functions as bilateral and guided filters do. We will show that our propagation filter can be viewed as a robust estimator, which minimizes the expected difference between the filtered and desirable image outputs. We will also relate propagation filtering to belief propagation, and suggest techniques if further speedup of the filtering process is necessary. In our experiments, we apply our propagation filter to a variety of applications such as image denoising, smoothing, fusion, and high-dynamic-range (HDR) compression. We will show that improved performance over existing image filters can be achieved.
Web Scale Photo Hash Clustering on A Single Machine
Authors: Yunchao Gong, Marcin Pawlowski, Fei Yang, Louis Brandy, Lubomir Bourdev, Rob Fergus
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Online Clustering, Spam detection

This paper addresses the problem of clustering a very large number of photos (i.e. hundreds of millions a day) in a stream into millions of clusters. This is particularly important as the popularity of photo sharing websites, such as Facebook, Google, and Instagram. Given large number of photos available online, how to efficiently organize them is an open problem.   To address this problem, we propose to cluster the binary hash codes of a large number of photos into binary cluster centers. We present a fast binary k-means algorithm that works directly on the similarity-preserving hashes of images and clusters them into binary centers on which we can build hash indexes to speedup computation. The proposed method is capable of clustering millions of photos on a single machine in a few minutes. We show that this approach is usually several magnitude faster than standard k-means and produces comparable clustering accuracy.  In addition, we propose an online clustering method based on binary k-means that is capable of clustering large photo stream on a single machine, and show applications to spam detection and trending photo discovery.
Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos
Authors: Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, Leonid Sigal
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Domain Adaptation, Incremental Learning, Object, object-detection, Object Detection

Over the last several years it has been shown that image-based object detectors are sensitive to  the training data and often fail to generalize to examples that fall outside the original training  sample domain (e.g., videos).  A number of domain adaptation (DA) techniques have been proposed to address this problem.  DA approaches are designed to adapt a fixed complexity model to the new (e.g., video) domain. We posit that unlabeled data should not only allow adaptation, but also improve (or at least maintain) performance on  the original and  other domains by dynamically adjusting model complexity and parameters.  We call this notion domain expansion.  To this end, we develop a new scalable and accurate incremental object detection algorithm,  based on several extensions of large-margin embedding (LME). Our detection model consists of an embedding space and multiple class prototypes in that embedding space,  that represent object classes; distance to those prototypes allows us to reason about multi-class detection. By incrementally detecting object instances in video and adding confident detections into the model, we are able to dynamically adjust the complexity of the detector over time  by instantiating new prototypes to span all domains  the model has seen. We test performance of our approach by expanding an object detector trained on ImageNet to detect objects  in ego-centric videos of Activity Daily Living (ADL) dataset and challenging YouTube Objects (YTO) dataset.
What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?
Authors: Mihir Jain, Jan C. van Gemert, Cees G. M. Snoek
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Classification, Action Recognition, General Classification, Object, Temporal Action Localization

This paper contributes to automatic classification and localization of human actions in video. Whereas motion is the key ingredient in modern approaches, we assess the benefits of having objects in the video representation. Rather than considering a handful of carefully selected and localized objects, we conduct an empirical study on the benefit of encoding 15,000 object categories for action using 6 datasets totaling more than 200 hours of video and covering 180 action classes. Our key contributions are i) the first in-depth study of encoding objects for actions, ii) we show that objects matter for actions, and are often semantically relevant as well. iii) We establish that actions have object preferences. Rather than using all objects, selection is advantageous for action recognition. iv) We reveal that object-action relations are generic, which allows to transferring these relationships from the one domain to the other. And, v) objects, when combined with motion, improve the state-of-the-art for both action classification and localization.
Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation
Authors: Rahaf Aljundi, Remi Emonet, Damien Muselet, Marc Sebban
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches.
Designing Effective Inter-Pixel Information Flow for Natural Image Matting
Authors: Yagiz Aksoy, Tunc Ozan Aydin, Marc Pollefeys
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Matting

We present a novel, purely affinity-based natural image matting algorithm. Our method relies on carefully defined pixel-to-pixel connections that enable effective use of information available in the image and the trimap. We control the information flow from the known-opacity regions into the unknown region, as well as within the unknown region itself, by utilizing multiple definitions of pixel affinities. This way we achieve significant improvements on matte quality near challenging regions of the foreground object. Among other forms of information flow, we introduce color-mixture flow, which builds upon local linear embedding and effectively encapsulates the relation between different pixel opacities. Our resulting novel linear system formulation can be solved in closed-form and is robust against several fundamental challenges in natural matting such as holes and remote intricate structures. While our method is primarily designed as a standalone natural matting tool, we show that it can also be used for regularizing mattes obtained by various sampling-based methods. Our evaluation using the public alpha matting benchmark suggests a significant performance improvement over the state-of-the-art.