pwc-archive/papers-with-abstracts huggingface dataset:
Kernel-PCA Analysis of Surface Normals for Shape-from-Shading
Authors: Patrick Snape, Stefanos Zafeiriou
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We propose a kernel-based framework for computing components from a set of surface normals. This framework allows us to easily demonstrate that component analysis can be performed directly upon normals. We link previously proposed mapping functions, the azimuthal equidistant projection (AEP) and principal geodesic analysis (PGA), to our kernel-based framework. We also propose a new mapping function based upon the cosine distance between normals. We demonstrate the robustness of our proposed kernel when trained with noisy training sets. We also compare our kernels within an existing shape-from-shading (SFS) algorithm. Our spherical representation of normals, when combined with the robust properties of cosine kernel, produces a very robust subspace analysis technique. In particular, our results within SFS show a substantial qualitative and quantitative improvement over existing techniques.
Merging SVMs with Linear Discriminant Analysis: A Combined Model
Authors: Symeon Nikitidis, Stefanos Zafeiriou, Maja Pantic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Dimensionality Reduction, Object Recognition

A key problem often encountered by many learning algorithms in computer vision dealing with high dimensional data is the so called "curse of dimensionality" which arises when the available training samples are less than the input feature space dimensionality. To remedy this problem, we propose a joint dimensionality reduction and classification framework by formulating an optimization problem within the maximum margin class separation task. The proposed optimization problem is solved using alternative optimization where we jointly compute the low dimensional maximum margin projections and the separating hyperplanes in the projection subspace. Moreover, in order to reduce the computational cost of the developed optimization algorithm we incorporate orthogonality constraints on the derived projection bases and show that the resulting combined model is an alternation between identifying the optimal separating hyperplanes and performing a linear discriminant analysis on the support vectors. Experiments on face, facial expression and object recognition validate the effectiveness of the proposed method against state-of-the-art dimensionality reduction algorithms.
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning
Authors: Bang Zhang, Yi Wang, Yang Wang, Fang Chen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Multi-class Classification, Transfer Learning

Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding. Most of these approaches focus on the first two phases and predefined distance function is used for decoding. In this paper, however, we propose to perform learning in coding space for more adaptive decoding, thereby improving overall performance. Ramp loss is exploited for measuring multi-class decoding error. The proposed algorithm has uniform stability. It is insensitive to data noises and scalable with large scale datasets. Generalization error bound and numerical results are given with promising outcomes.
Finding the Subspace Mean or Median to Fit Your Need
Authors: Tim Marrinan, J. Ross Beveridge, Bruce Draper, Michael Kirby, Chris Peterson
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Many computer vision algorithms employ subspace models to represent data. Many of these approaches benefit from the ability to create an average or prototype for a set of subspaces. The most popular method in these situations is the Karcher mean, also known as the Riemannian center of mass. The prevalence of the Karcher mean may lead some to assume that it provides the best average in all scenarios. However, other subspace averages that appear less frequently in the literature may be more appropriate for certain tasks. The extrinsic manifold mean, the L2-median, and the flag mean are alternative averages that can be substituted directly for the Karcher mean in many applications.  This paper evaluates the characteristics and performance of these four averages on synthetic and real-world data. While the Karcher mean generalizes the Euclidean mean to the Grassman manifold, we show that the extrinsic manifold mean, the L2-median, and the flag mean behave more like medians and are therefore more robust to the presence of outliers among the subspaces being averaged. We also show that while the Karcher mean and L2-median are computed using iterative algorithms, the extrinsic manifold mean and flag mean can be found analytically and are thus orders of magnitude faster in practice. Finally, we show that the flag mean is a generalization of the extrinsic manifold mean that permits subspaces with different numbers of dimensions to be averaged. The result is a "cookbook" that maps algorithm constraints and data properties to the most appropriate subspace mean for a given application.
Adaptive Color Attributes for Real-Time Visual Tracking
Authors: Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg, Joost Van de Weijer
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Image Description, Object Recognition, Real-Time Visual Tracking, Visual Tracking

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.  This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
Local Layering for Joint Motion Estimation and Occlusion Detection
Authors: Deqing Sun, Ce Liu, Hanspeter Pfister
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Motion Estimation, Optical Flow Estimation

Most motion estimation algorithms (optical flow, layered models) cannot handle large amount of occlusion in textureless regions, as motion is often initialized with no occlusion assumption despite that occlusion may be included in the final objective. To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly. In particular, the uncertainties of occlusion relationships are retained so that motion is inferred by considering all the possibilities of local occlusion relationships.  In addition, the local layering model handles articulated objects with self-occlusion. We demonstrate that the local layering model can handle motion and occlusion well for both challenging synthetic and real sequences.
Realtime and Robust Hand Tracking from Depth
Authors: Chen Qian, Xiao Sun, Yichen Wei, Xiaoou Tang, Jian Sun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: GPU, Stochastic Optimization

We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data.  Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. Those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking.
Multi-Output Learning for Camera Relocalization
Authors: Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, Camera Relocalization

We address the problem of estimating the pose of a cam- era relative to a known 3D scene from a single RGB-D frame. We formulate this problem as inversion of the generative rendering procedure, i.e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input. This is a non-convex optimization problem with many local optima. We propose a hybrid discriminative-generative learning architecture that consists of: (i) a set of M predictors which generate M camera pose hypotheses; and (ii) a 'selector' or 'aggregator' that infers the best pose from the multiple pose hypotheses based on a similarity function. We are interested in predictors that not only produce good hypotheses but also hypotheses that are different from each other. Thus, we propose and study methods for learning 'marginally relevant' predictors, and compare their performance when used with different selection procedures. We evaluate our method on a recently released 3D reconstruction dataset with challenging camera poses, and scene variability. Experiments show that our method learns to make multiple predictions that are marginally relevant and can effectively select an accurate prediction. Furthermore, our method outperforms the state-of-the-art discriminative approach for camera relocalization.
MAP Visibility Estimation for Large-Scale Dynamic 3D Reconstruction
Authors: Hanbyul Joo, Hyun Soo Park, Yaser Sheikh
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

Many traditional challenges in reconstructing 3D motion, such as matching across wide baselines and handling occlusion, reduce in significance as the number of unique viewpoints increases. However, to obtain this benefit, a new challenge arises: estimating precisely which cameras observe which points at each instant in time. We present a maximum a posteriori (MAP) estimate of the time-varying visibility of the target points to reconstruct the 3D motion of an event from a large number of cameras. Our algorithm takes, as input, camera poses and image sequences, and outputs the time-varying set of the cameras in which a target patch is visibile and its reconstructed trajectory. We model visibility estimation as a MAP estimate by incorporating various cues including photometric consistency, motion consistency, and geometric consistency, in conjunction with a prior that rewards consistent visibilities in proximal cameras. An optimal estimate of visibility is obtained by finding the minimum cut of a capacitated graph over cameras. We demonstrate that our method estimates visibility with greater accuracy, and increases tracking performance producing longer trajectories, at more locations, and at higher accuracies than methods that ignore visibility or use photometric consistency alone.
Multi-Object Tracking via Constrained Sequential Labeling
Authors: Sheng Chen, Alan Fern, Sinisa Todorovic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Multi-Object Tracking, Object, Object Tracking

This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations. In such videos, detection-based trackers give poor performance since detecting people occurrences is not reliable, and common assumptions about locally smooth trajectories do not hold. Rather, we use temporal mid-level features (e.g., supervoxels or dense point trajectories) as a more coherent spatiotemporal basis for handling occlusion and pose variations.Thus, we formulate tracking as labeling mid-level features by object identifiers, and specify a new approach, called constrained sequential labeling (CSL), for performing this labeling. CSL uses a cost function to sequentially assign labels while respecting the implications of hard constraints computed via constraint propagation. A key feature of this approach is that it allows for the use of flexible cost functions and constraints that capture complex dependencies that cannot be represented in standard network-flow formulations. To exploit this flexibility we describe how to learn constraints and give a provably correct learning algorithms for cost functions that achieves finitetime convergence at a rate that improves with the strength of the constraints. Our experimental results indicate that CSL outperforms the state-of-the-art on challenging real-world videos of volleyball, basketball, and pedestrians walking.
A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields
Authors: Alexander Fix, Chen Wang, Ramin Zabih
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising

Graph cuts method such as a-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision. Higher-order Markov Random Fields (MRF's), which are important for numerous applications, have proven to be very difficult, especially for multilabel MRF's (i.e. more than 2 labels). In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF's. Primal-dual methods provide guaranteed approximation bounds, and can exploit information in the dual variables to improve their efficiency. Our algorithm generalizes the PD3 [19] technique for first-order MRFs, and relies on a variant of max-flow that can exactly optimize certain higher-order binary MRF's [14]. We provide approximation bounds similar to PD3 [19], and the method is fast in practice. It can optimize non-submodular MRF's, and additionally can in- corporate problem-specific knowledge in the form of fusion proposals. We compare experimentally against the existing approaches that can efficiently handle these difficult energy functions [6, 10, 11]. For higher-order denoising and stereo MRF's, we produce lower energy while running significantly faster.
Energy Based Multi-model Fitting & Matching for 3D Reconstruction
Authors: Hossam Isack, Yuri Boykov
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, valid

Standard geometric model fitting methods take as an input a fixed set of feature pairs greedily matched based only on their appearances. Inadvertently, many valid matches are discarded due to repetitive texture or large baseline between view points. To address this problem, matching should consider both feature appearances and geometric fitting errors. We jointly solve feature matching and multi-model fitting problems by optimizing one energy. The formulation is based on our generalization of the assignment problem and its efficient min-cost-max-flow solver. Our approach significantly increases the number of correctly matched features, improves the accuracy of fitted models, and is robust to larger baselines.
Submodularization for Binary Pairwise Energies
Authors: Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Many computer vision problems require optimization of binary non-submodular energies. We propose a general optimization framework based on local submodular approximations (LSA). Unlike standard LP relaxation methods that linearize the whole energy globally, our approach iteratively approximates the energies locally. On the other hand, unlike standard local optimization methods (e.g. gradient descent or projection techniques) we use non-linear submodular approximations and optimize them without leaving the domain of integer solutions. We discuss two specific LSA algorithms based on  trust region and auxiliary function principles, LSA-TR and LSA-AUX. These methods obtain state-of-the-art results on a wide range of applications outperforming many standard techniques such as LBP, QPBO, and TRWS. While our paper is focused on pairwise energies, our ideas extend to higher-order problems. The code is available online
Maximum Persistency in Energy Minimization
Authors: Alexander Shekhovtsov
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We consider discrete pairwise energy minimization problem (weighted constraint satisfaction, max-sum labeling) and methods that identify a globally optimal partial assignment of variables. When finding a complete optimal assignment is intractable, determining optimal values for a part of variables is an interesting possibility. Existing methods are based on different sufficient conditions.  We propose a new sufficient condition for partial optimality which is: (1) verifiable in polynomial time (2) invariant to reparametrization of the problem and permutation of labels and (3) includes many existing sufficient conditions as special cases. We pose the problem of finding the maximum optimal partial assignment identifiable by the new sufficient condition. A polynomial method is proposed which is guaranteed to assign same or larger part of variables. The core of the method is a specially constructed linear program that identifies persistent assignments in an arbitrary multi-label setting.
Scene Labeling Using Beam Search Under Mutex Constraints
Authors: Anirban Roy, Sinisa Todorovic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Scene Labeling, Superpixels

This paper addresses the problem of assigning object class labels to image pixels. Following recent holistic formulations, we cast scene labeling as inference of a conditional random field (CRF) grounded onto superpixels. The CRF inference is specified as quadratic program (QP) with mutual exclusion (mutex) constraints on class label assignments. The QP is solved using a beam search (BS), which is well-suited for scene labeling, because it explicitly accounts for spatial extents of objects; conforms to inconsistency constraints from domain knowledge; and has low computational costs. BS gradually builds a search tree whose nodes correspond to candidate scene labelings. Successor nodes are repeatedly generated from a select set of their parent nodes until convergence. We prove that our BS efficiently maximizes the QP objective of CRF inference. Effectiveness of our BS for scene labeling is evaluated on the benchmark MSRC, Stanford Backgroud, PASCAL VOC 2009 and 2010 datasets.
Persistent Tracking for Wide Area Aerial Surveillance
Authors: Jan Prokaj, Gerard Medioni
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: object-detection, Object Detection

Persistent surveillance of large geographic areas from unmanned aerial vehicles allows us to learn much about the daily activities in the region of interest. Nearly all of the approaches addressing tracking in this imagery are detection-based and rely on background subtraction or frame differencing to provide detections. This, however, makes it difficult to track targets once they slow down or stop, which is not acceptable for persistent tracking, our goal.  We present a multiple target tracking approach that does not exclusively rely on background subtraction and is better able to track targets through stops. It accomplishes this by effectively running two trackers in parallel: one based on detections from background subtraction providing target initialization and reacquisition, and one based on a target state regressor providing frame to frame tracking. We evaluated the proposed approach on a long sequence from a wide area aerial imagery dataset, and the results show improved object detection rates and ID-switch rates with limited increases in false alarms compared to the competition.
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
Authors: Xiangyuan Lan, Andy J. Ma, Pong C. Yuen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Visual Tracking

The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multicue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.
Multi-Forest Tracker: A Chameleon in Tracking
Authors: David J. Tan, Slobodan Ilic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object Tracking

In this paper, we address the problem of object tracking in intensity images and depth data. We propose a generic framework that can be used either for tracking 2D templates in intensity images or for tracking 3D objects in depth images. To overcome problems like partial occlusions, strong illumination changes and motion blur, that notoriously make energy minimization-based tracking methods get trapped in a local minimum, we propose a learning-based method that is robust to all these problems. We use random forests to learn the relation between the parameters that defines the object's motion, and the changes they induce on the image intensities or the point cloud of the template. It follows that, to track the template when it moves, we use the changes on the image intensities or point cloud to predict the parameters of this motion. Our algorithm has an extremely fast tracking performance running at less than 2 ms per frame, and is robust to partial occlusions. Moreover, it demonstrates robustness to strong illumination changes when tracking templates using intensity images, and robustness in tracking 3D objects from arbitrary viewpoints even in the presence of motion blur that causes missing or erroneous data in depth images. Extensive experimental evaluation and comparison to the related approaches strongly demonstrates the benefits of our method.
Rigid Motion Segmentation using Randomized Voting
Authors: Heechul Jung, Jeongwoo Ju, Junmo Kim
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Motion Segmentation, Segmentation

In this paper, we propose a novel rigid motion segmentation algorithm called randomized voting (RV). This algorithm is based on epipolar geometry, and computes a score using the distance between the feature point and the corresponding epipolar line. This score is accumulated and utilized for final grouping. Our algorithm basically deals with two frames, so it is also applicable to the two-view motion segmentation problem. For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted; it consists of two and three rigid motions. Our algorithm has provided the most accurate motion segmentation results among all of the state-of-the-art algorithms. The average error rate is 0.77%. In addition, when there is measurement noise, our algorithm is comparable with other state-of-the-art algorithms.