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Conference on Computer Vision and Pattern Recognition (CVPR)
2014 IEEE Conference on Computer Vision and Pattern Recognition
23-28 June 2014
Showing 1-25 of 550
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
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Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction
Publication Year: 2014,Page(s):1 - 8
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, ...Show More
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 unambi...Show More
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 ass...Show More
Critical Configurations for Radial Distortion Self-Calibration
Publication Year: 2014,Page(s):25 - 32
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...Show More
Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion
Publication Year: 2014,Page(s):33 - 40
Cited by: Papers (27)
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 ...Show More
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 ...Show More
Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation
Publication Year: 2014,Page(s):49 - 56
Cited by: Papers (45)
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 consid...Show More
Weakly Supervised Multiclass Video Segmentation
Xiao Liu;Dacheng Tao;Mingli Song;Ying Ruan;Chun Chen;Jiajun Bu
Publication Year: 2014,Page(s):57 - 64
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 p...Show More
Video Motion Segmentation Using New Adaptive Manifold Denoising Model
Publication Year: 2014,Page(s):65 - 72
Cited by: Papers (5)
Video motion segmentation techniques automatically segment and track objects and regions from videos or image sequences as a primary processing step for many computer vision applications. We propose a novel motion segmentation approach for both rigid and non-rigid objects using adaptive manifold denoising. We first introduce an adaptive kernel space in which two feature trajectories are mapped int...Show More
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning
Thorsten Beier;Thorben Kroeger;Jörg H. Kappes;Ullrich Köthe;Fred A. Hamprecht
Publication Year: 2014,Page(s):73 - 80
Cited by: Papers (26)
Recently, unsupervised image segmentation has become increasingly popular. Starting from a superpixel segmentation, an edge-weighted region adjacency graph is constructed. Amongst all segmentations of the graph, the one which best conforms to the given image evidence, as measured by the sum of cut edge weights, is chosen. Since this problem is NP-hard, we propose a new approximate solver based on ...Show More
In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier -- An idea that is intuitively supported by the hierarchical nature of the decision forest model where th...Show More
The limitations of current state-of-the-art methods for single-view depth estimation and semantic segmentations are closely tied to the property of perspective geometry, that the perceived size of the objects scales inversely with the distance. In this paper, we show that we can use this property to reduce the learning of a pixel-wise depth classifier to a much simpler classifier predicting only t...Show More
Event Detection Using Multi-level Relevance Labels and Multiple Features
Zhongwen Xu;Ivor W. Tsang;Yi Yang;Zhigang Ma;Alexander G. Hauptmann
Publication Year: 2014,Page(s):97 - 104
Cited by: Papers (15)
We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In th...Show More
Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
Stephan Liwicki;Minh-Tri Pham;Stefanos Zafeiriou;Maja Pantic;Björn Stenger
Publication Year: 2014,Page(s):105 - 112
Cited by: Papers (3)
In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale an...Show More
Human vs. Computer in Scene and Object Recognition
Publication Year: 2014,Page(s):113 - 120
Cited by: Papers (26)
Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimension...Show More
Semi-supervised Spectral Clustering for Image Set Classification
Publication Year: 2014,Page(s):121 - 128
Cited by: Papers (30)
We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a p...Show More
Look at the Driver, Look at the Road: No Distraction! No Accident!
Publication Year: 2014,Page(s):129 - 136
Cited by: Papers (68)
The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptatio...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
- Print on DemandPurchase at Partner
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