pwc-archive/papers-with-abstracts huggingface dataset:
Supervised Descent Method and Its Applications to Face Alignment
Authors: Xuehan Xiong, Fernando de la Torre
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Camera Calibration, Face Alignment

Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2 nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2 nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-ofthe-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.
Self-Paced Learning for Long-Term Tracking
Authors: James S. Supancic III, Deva Ramanan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection, Object Tracking

We address the problem of long-term object tracking, where the object may become occluded or leave-the-view. In this setting, we show that an accurate appearance model is considerably more effective than a strong motion model. We develop simple but effective algorithms that alternate between tracking and learning a good appearance model given a track. We show that it is crucial to learn from the "right" frames, and use the formalism of self-paced curriculum learning to automatically select such frames. We leverage techniques from object detection for learning accurate appearance-based templates, demonstrating the importance of using a large negative training set (typically not used for tracking). We describe both an offline algorithm (that processes frames in batch) and a linear-time online (i.e. causal) algorithm that approaches real-time performance. Our models significantly outperform prior art, reducing the average error on benchmark videos by a factor of 4.
A Machine Learning Approach for Non-blind Image Deconvolution
Authors: Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Scholkopf
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: BIG-bench Machine Learning, Image Deconvolution

Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-ofthe-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur.
Correlation Filters for Object Alignment
Authors: Vishnu Naresh Boddeti, Takeo Kanade, B. V. K. Vijaya Kumar
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object

Alignment of 3D objects from 2D images is one of the most important and well studied problems in computer vision. A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors. Since errors in landmark initialization from the appearance model propagate through the shape model, it is critical to have a robust landmark appearance model. While there has been much progress in designing sophisticated and robust shape models, there has been relatively less progress in designing robust landmark detection models. In this paper we present an efficient and robust landmark detection model which is designed specifically to minimize localization errors thereby leading to state-of-the-art object alignment performance. We demonstrate the efficacy and speed of the proposed approach on the challenging task of multi-view car alignment.
Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds
Authors: Jeremie Papon, Alexey Abramov, Markus Schoeler, Florentin Worgotter
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, Segmentation, Superpixels

Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpixels, is a widely used preprocessing step in segmentation algorithms. Superpixel methods reduce the number of regions that must be considered later by more computationally expensive algorithms, with a minimal loss of information. Nevertheless, as some information is inevitably lost, it is vital that superpixels not cross object boundaries, as such errors will propagate through later steps. Existing methods make use of projected color or depth information, but do not consider three dimensional geometric relationships between observed data points which can be used to prevent superpixels from crossing regions of empty space. We propose a novel over-segmentation algorithm which uses voxel relationships to produce over-segmentations which are fully consistent with the spatial geometry of the scene in three dimensional, rather than projective, space. Enforcing the constraint that segmented regions must have spatial connectivity prevents label flow across semantic object boundaries which might otherwise be violated. Additionally, as the algorithm works directly in 3D space, observations from several calibrated RGB+D cameras can be segmented jointly. Experiments on a large data set of human annotated RGB+D images demonstrate a significant reduction in occurrence of clusters crossing object boundaries, while maintaining speeds comparable to state-of-the-art 2D methods.
Adherent Raindrop Detection and Removal in Video
Authors: Shaodi You, Robby T. Tan, Rei Kawakami, Katsushi Ikeuchi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatiotemporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.
Recovering Line-Networks in Images by Junction-Point Processes
Authors: Dengfeng Chai, Wolfgang Forstner, Florent Lafarge
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Point Processes

The automatic extraction of line-networks from images is a well-known computer vision issue. Appearance and shape considerations have been deeply explored in the literature to improve accuracy in presence of occlusions, shadows, and a wide variety of irrelevant objects. However most existing works have ignored the structural aspect of the problem. We present an original method which provides structurally-coherent solutions. Contrary to the pixelbased and object-based methods, our result is a graph in which each node represents either a connection or an ending in the line-network. Based on stochastic geometry, we develop a new family of point processes consisting in sampling junction-points in the input image by using a Monte Carlo mechanism. The quality of a configuration is measured by a probability density which takes into account both image consistency and shape priors. Our experiments on a variety of problems illustrate the potential of our approach in terms of accuracy, flexibility and efficiency.
Continuous Inference in Graphical Models with Polynomial Energies
Authors: Mathieu Salzmann
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Diversity

In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, where the global problem is split into subproblems defined over the graph cliques. The optimal solution to these subproblems is obtained by making use of a polynomial system solver. Our algorithm inherits the convergence guarantees of dual decomposition. To speed up optimization, we also introduce a variant of this algorithm based on the augmented Lagrangian method. Our experiments illustrate the diversity of computer vision problems that can be expressed with polynomial energies, and demonstrate the benefits of our approach over existing continuous inference methods.
Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop
Authors: Catherine Wah, Serge Belongie
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, General Classification, Zero-Shot Learning

Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class; it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.
Locally Aligned Feature Transforms across Views
Authors: Wei Li, Xiaogang Wang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Metric Learning, Person Re-Identification

In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person reidentification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual features of an image pair from different views are first locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsityinducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach.
Sensing and Recognizing Surface Textures Using a GelSight Sensor
Authors: Rui Li, Edward H. Adelson
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Material Recognition, Texture Classification

Sensing surface textures by tou ch is a valuable was difficult to build capability for robots. Until recently it a a compliant sensor with high sennnsitivity and high resolution. The GelSight sensor is cooompliant and offers sensitivity and resolution exceeding that of the human fingertips. This opens the possibility of measuring and recognizing highly detailed surface texxxtures. The GelSight sensor, when pressed against a surfaccce, delivers a height map. This can be treated as an image, aaand processed using the tools of visual texture analysis. W WW have devised a simple yet effective texture recognitiooon system based on local binary patterns, and enhanced it by the use of a multi-scale pyramid and a Hellinger dddistance metric. We built a database with 40 classes of taaactile textures using materials such as fabric, wood, and sannndpaper. Our system can correctly categorize materials from m this database with high accuracy. This suggests that the G GelSight sensor can be useful for material recognition by rooobots.
Universality of the Local Marginal Polytope
Authors: Daniel Prusa, Tomas Werner
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We show that solving the LP relaxation of the MAP inference problem in graphical models (also known as the minsum problem, energy minimization, or weighted constraint satisfaction) is not easier than solving any LP. More precisely, any polytope is linear-time representable by a local marginal polytope and any LP can be reduced in linear time to a linear optimization (allowing infinite weights) over a local marginal polytope.
Blocks That Shout: Distinctive Parts for Scene Classification
Authors: Mayank Juneja, Andrea Vedaldi, C. V. Jawahar, Andrew Zisserman
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Classification, General Classification, Scene Classification

The automatic discovery of distinctive parts for an object or scene class is challenging since it requires simultaneously to learn the part appearance and also to identify the part occurrences in images. In this paper, we propose a simple, efficient, and effective method to do so. We address this problem by learning parts incrementally, starting from a single part occurrence with an Exemplar SVM. In this manner, additional part instances are discovered and aligned reliably before being considered as training examples. We also propose entropy-rank curves as a means of evaluating the distinctiveness of parts shareable between categories and use them to select useful parts out of a set of candidates. We apply the new representation to the task of scene categorisation on the MIT Scene 67 benchmark. We show that our method can learn parts which are significantly more informative and for a fraction of the cost, compared to previous part-learning methods such as Singh et al. [28]. We also show that a well constructed bag of words or Fisher vector model can substantially outperform the previous state-ofthe-art classification performance on this data.
Megastereo: Constructing High-Resolution Stereo Panoramas
Authors: Christian Richardt, Yael Pritch, Henning Zimmer, Alexander Sorkine-Hornung
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Vocal Bursts Intensity Prediction

We present a solution for generating high-quality stereo panoramas at megapixel resolutions. While previous approaches introduced the basic principles, we show that those techniques do not generalise well to today's high image resolutions and lead to disturbing visual artefacts. As our first contribution, we describe the necessary correction steps and a compact representation for the input images in order to achieve a highly accurate approximation to the required ray space. Our second contribution is a flow-based upsampling of the available input rays which effectively resolves known aliasing issues like stitching artefacts. The required rays are generated on the fly to perfectly match the desired output resolution, even for small numbers of input images. In addition, the upsampling is real-time and enables direct interactive control over the desired stereoscopic depth effect. In combination, our contributions allow the generation of stereoscopic panoramas at high output resolutions that are virtually free of artefacts such as seams, stereo discontinuities, vertical parallax and other mono-/stereoscopic shape distortions. Our process is robust, and other types of multiperspective panoramas, such as linear panoramas, can also benefit from our contributions. We show various comparisons and high-resolution results.
Dense 3D Reconstruction from Severely Blurred Images Using a Single Moving Camera
Authors: Hee Seok Lee, Kuoung Mu Lee
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D geometry, 3D Reconstruction, Deblurring

Motion blur frequently occurs in dense 3D reconstruction using a single moving camera, and it degrades the quality of the 3D reconstruction. To handle motion blur caused by rapid camera shakes, we propose a blur-aware depth reconstruction method, which utilizes a pixel correspondence that is obtained by considering the effect of motion blur. Motion blur is dependent on 3D geometry, thus parameterizing blurred appearance of images with scene depth given camera motion is possible and a depth map can be accurately estimated from the blur-considered pixel correspondence. The estimated depth is then converted into pixel-wise blur kernels, and non-uniform motion blur is easily removed with low computational cost. The obtained blur kernel is depth-dependent, thus it effectively addresses scene-depth variation, which is a challenging problem in conventional non-uniform deblurring methods.
A Practical Rank-Constrained Eight-Point Algorithm for Fundamental Matrix Estimation
Authors: Yinqiang Zheng, Shigeki Sugimoto, Masatoshi Okutomi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Due to its simplicity, the eight-point algorithm has been widely used in fundamental matrix estimation. Unfortunately, the rank-2 constraint of a fundamental matrix is enforced via a posterior rank correction step, thus leading to non-optimal solutions to the original problem. To address this drawback, existing algorithms need to solve either a very high order polynomial or a sequence of convex relaxation problems, both of which are computationally ineffective and numerically unstable. In this work, we present a new rank-2 constrained eight-point algorithm, which directly incorporates the rank-2 constraint in the minimization process. To avoid singularities, we propose to solve seven subproblems and retrieve their globally optimal solutions by using tailored polynomial system solvers. Our proposed method is noniterative, computationally efficient and numerically stable. Experiment results have verified its superiority over existing algebraic error based algorithms in terms of accuracy, as well as its advantages when used to initialize geometric error based algorithms.
Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses
Authors: Byung-soo Kim, Shili Xu, Silvio Savarese
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, Object Recognition

In this paper we focus on the problem of detecting objects in 3D from RGB-D images. We propose a novel framework that explores the compatibility between segmentation hypotheses of the object in the image and the corresponding 3D map. Our framework allows to discover the optimal location of the object using a generalization of the structural latent SVM formulation in 3D as well as the definition of a new loss function defined over the 3D space in training. We evaluate our method using two existing RGB-D datasets. Extensive quantitative and qualitative experimental results show that our proposed approach outperforms state-of-theart as methods well as a number of baseline approaches for both 3D and 2D object recognition tasks.
Pixel-Level Hand Detection in Ego-centric Videos
Authors: Cheng Li, Kris M. Kitani
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Hand Detection, Sign Language Recognition

We address the task of pixel-level hand detection in the context of ego-centric cameras. Extracting hand regions in ego-centric videos is a critical step for understanding handobject manipulation and analyzing hand-eye coordination. However, in contrast to traditional applications of hand detection, such as gesture interfaces or sign-language recognition, ego-centric videos present new challenges such as rapid changes in illuminations, significant camera motion and complex hand-object manipulations. To quantify the challenges and performance in this new domain, we present a fully labeled indoor/outdoor ego-centric hand detection benchmark dataset containing over 200 million labeled pixels, which contains hand images taken under various illumination conditions. Using both our dataset and a publicly available ego-centric indoors dataset, we give extensive analysis of detection performance using a wide range of local appearance features. Our analysis highlights the effectiveness of sparse features and the importance of modeling global illumination. We propose a modeling strategy based on our findings and show that our model outperforms several baseline approaches.
Exploiting the Power of Stereo Confidences
Authors: David Pfeiffer, Stefan Gehrig, Nicolai Schneider
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Applications based on stereo vision are becoming increasingly common, ranging from gaming over robotics to driver assistance. While stereo algorithms have been investigated heavily both on the pixel and the application level, far less attention has been dedicated to the use of stereo confidence cues. Mostly, a threshold is applied to the confidence values for further processing, which is essentially a sparsified disparity map. This is straightforward but it does not take full advantage of the available information. In this paper, we make full use of the stereo confidence cues by propagating all confidence values along with the measured disparities in a Bayesian manner. Before using this information, a mapping from confidence values to disparity outlier probability rate is performed based on gathered disparity statistics from labeled video data. We present an extension of the so called Stixel World, a generic 3D intermediate representation that can serve as input for many of the applications mentioned above. This scheme is modified to directly exploit stereo confidence cues in the underlying sensor model during a maximum a posteriori estimation process. The effectiveness of this step is verified in an in-depth evaluation on a large real-world traffic data base of which parts are made publicly available. We show that using stereo confidence cues allows both reducing the number of false object detections by a factor of six while keeping the detection rate at a near constant level.