pwc-archive/papers-with-abstracts huggingface dataset:
Remote Heart Rate Measurement From Face Videos Under Realistic Situations
Authors: Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.
GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor
Authors: Amir Roshan Zamir, Shervin Ardeshir, Mubarak Shah
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: TAG

The number of GPS-tagged images available on the web is increasing at a rapid rate. The majority of such location tags are specified by the users, either through manual tagging or localization-chips embedded in the cameras. However, a known issue with user shared images is the unreliability of such GPS-tags. In this paper, we propose a method for addressing this problem. We assume a large dataset of GPS-tagged images which includes an unknown subset with contaminated tags is available. We develop a robust method for identification and refinement of this subset using the rest of the images in the dataset. In the proposed method, we form a large number of triplets of matching images and use them for estimating the location of the query image utilizing structure from motion. Some of the generated estimations may be inaccurate due to the noisy GPS-tags in the dataset. Therefore, we perform Random Walks on the estimations in order to identify the subset with the maximal agreement. Finally, we estimate the GPS-tag of the query utilizing the identified consistent subset using a weighted mean. We propose a new damping factor for Random Walks which conforms to the level of noise in the input, and consequently, robustifies Random Walks. We evaluated the proposed framework on a dataset of over 18k user-shared images; the experiments show our method robustly improves the accuracy of GPS-tags under diverse scenarios.
Blur Kernel Estimation Using Normalized Color-Line Prior
Authors: Wei-Sheng Lai, Jian-Jiun Ding, Yen-Yu Lin, Yung-Yu Chuang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Image Deblurring

This paper proposes a single-image blur kernel estimation algorithm that utilizes the normalized color-line prior to restore sharp edges without altering edge structures or enhancing noise. The proposed prior is derived from the color-line model, which has been successfully applied to non-blind deconvolution and many computer vision problems. In this paper, we show that the original color-line prior is not effective for blur kernel estimation and propose a normalized color-line prior which can better enhance edge contrasts. By optimizing the proposed prior, our method gradually enhances the sharpness of the intermediate patches without using heuristic filters or external patch priors. The intermediate patches can then guide the estimation of the blur kernel. A comprehensive evaluation on a large image deblurring dataset shows that our algorithm achieves the state-of-the-art results.
A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors
Authors: Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI). Specifically, MPI occurs when multiple optical reflections return to a single spatial location on the imaging sensor. Many prior approaches to rectifying MPI rely on sparsity in optical reflections, which is an extreme simplification. In this paper, we correct MPI by combining the standard measurements from a TOF camera with information from direct and global light transport. We report results on both simulated experiments and physical experiments (using the Kinect sensor). Our results, evaluated against ground truth, demonstrate a quantitative improvement in depth accuracy.
Traditional Saliency Reloaded: A Good Old Model in New Shape
Authors: Simone Frintrop, Thomas Werner, German Martin Garcia
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Proposal Generation, Segmentation, Semantic Segmentation

 In this paper, we show that the seminal, biologically-inspired   saliency model by Itti et al. is still competitive with current state-of-the-art methods for salient object   segmentation if some important adaptions are made.  We show which   changes are necessary to achieve high performance, with special   emphasis on the scale-space: we introduce a twin pyramid for   computing Difference-of-Gaussians, which enables a flexible   center-surround ratio.  The resulting system, called VOCUS2,   is elegant and coherent in structure, fast, and computes saliency at   the pixel level.  It is not only suitable for images with few objects, but also for   complex scenes as captured by mobile devices.  Furthermore, we   integrate the saliency system into an object proposal generation   framework to obtain segment-based saliency maps and boost the   results for salient object segmentation. We show that our system   achieves state-of-the-art performance on a large collection of   benchmark data.
Automatic Construction Of Robust Spherical Harmonic Subspaces
Authors: Patrick Snape, Yannis Panagakis, Stefanos Zafeiriou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined model of shape and identity. We build this basis without aid from a 3D model and show how it can be combined with recent efficient sparse facial feature localisation techniques to recover dense 3D facial shape. Unlike previous works in the area, our method is very efficient and is an order of magnitude faster to train, taking only a few minutes to build a model with over 2000 images. Furthermore, it can be used for real-time recovery of facial shape.
Leveraging Stereo Matching With Learning-Based Confidence Measures
Authors: Min-Gyu Park, Kuk-Jin Yoon
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: regression, Stereo Matching, Stereo Matching Hand

We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then train regression forests again to predict the correctness (confidence) of a match by using selected confidence measures. In addition, we present a confidence-based matching cost modulation scheme based on the predicted correctness for improving the robustness and accuracy of various stereo matching algorithms. We apply the proposed scheme to the semi-global matching algorithm to make it robust under unexpected difficulties that can occur in outdoor environments. We verify the proposed confidence measure selection and cost modulation methods through extensive experimentation with various aspects using KITTI and challenging outdoor datasets.
Saliency Detection via Cellular Automata
Authors: Yao Qin, Huchuan Lu, Yiqun Xu, He Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Saliency Detection

In this paper, we introduce Cellular Automata--a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors. Impact factor matrix and coherence matrix are constructed to balance the influential power towards each cell's next state. The saliency values of all cells will be renovated simultaneously according to the proposed updating rule. It's surprising to find out that parallel evolution can improve all the existing methods to a similar level regardless of their original results. Finally, we present an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps. Extensive experiments on six public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.
Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers
Authors: Jonas Wulff, Michael J. Black
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: CPU, Optical Flow Estimation

We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed from four Hollywood movies. Optical flow fields are then compactly approximated as a weighted sum of the basis flow fields. Our new PCA-Flow algorithm robustly estimates these weights from sparse feature matches. The method runs in under 200ms/frame on the MPI-Sintel dataset using a single CPU and is more accurate and significantly faster than popular methods such as LDOF and Classic+NL. For some applications, however, the results are too smooth. Consequently, we develop a novel sparse layered flow method in which each layer is represented by PCA-Flow. Unlike existing layered methods, estimation is fast because it uses only sparse matches. We combine information from different layers into a dense flow field using an image-aware MRF. The resulting PCA-Layers method runs in 3.2s/frame, is significantly more accurate than PCA-Flow, and achieves state-of-the-art performance in occluded regions on MPI-Sintel.
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition
Authors: Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dimensionality Reduction, Face Recognition, Metric Learning

 In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. From the perspective of manifold learning, our method can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifold to a lower-dimensional, more discriminative Grassmann manifold  where more favorable classification can be achieved. Experiments on several real-world video face datasets demonstrate that the proposed method yields competitive performance compared with the state-of-the-art algorithms.
Structural Sparse Tracking
Authors: Tianzhu Zhang, Si Liu, Changsheng Xu, Shuicheng Yan, Bernard Ghanem, Narendra Ahuja, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Visual Tracking

Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.
Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation
Authors: Hyeokhyen Kwon, Yu-Wing Tai, Stephen Lin
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dictionary Learning, Missing Values, Quantization

Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse representation, our method learns a dictionary of geometric primitives which captures the correlation between high-quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by employing a corresponding training set. In our experiments, we show that this leads to additional improvements in recovering depth maps of human faces.
Uncalibrated Photometric Stereo Based on Elevation Angle Recovery From BRDF Symmetry of Isotropic Materials
Authors: Feng Lu, Imari Sato, Yoichi Sato
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper addresses the problem of uncalibrated photometric stereo with isotropic reflectances. Existing methods face difficulty in solving for the elevation angles of surface normals when the light sources only cover the visible hemisphere. Here, we introduce the notion of "constrained half-vector symmetry" for general isotropic BRDFs and show its capability of elevation angle recovery. This sort of symmetry can be observed in a 1D BRDF slice from a subset of surface normals with the same azimuth angle, and we use it to devise an efficient modeling and solution method to constrain and recover the elevation angles of surface normals accurately. To enable our method to work in an uncalibrated manner, we further solve for light sources in the case of general isotropic BRDFs. By combining this method with the existing ones for azimuth angle estimation, we can get state-of-the-art results for uncalibrated photometric stereo with general isotropic reflectances.
Attributes and Categories for Generic Instance Search From One Example
Authors: Ran Tao, Arnold W. M. Smeulders, Shih-Fu Chang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Instance Search

This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses its generality on shoes. Secondly, we propose to use automatically learned category-specific attributes to address the large appearance variations present in generic instance search. On the problem of searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin. On a shoe dataset containing 6624 shoe images recorded from all viewing angles, we improve the performance from 36.73 to 56.56 using category-specific attributes. Thirdly, we extend our methods to search objects without restricting to the specifically known category. We show the combination of category-level information and the category-specific attributes is superior to combining category-level information with low-level features such as Fisher vector.
Heat Diffusion Over Weighted Manifolds: A New Descriptor for Textured 3D Non-Rigid Shapes
Authors: Mostafa Abdelrahman, Aly Farag, David Swanson, Moumen T. El-Melegy
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval

This paper propose an approach for modeling textured 3D non-rigid models based on Weighted Heat Kernel Signature(W-HKS). As a first contribution, we show how to include photometric information as a weight over the shape manifold, we also propose a novel formulation for heat diffusion over weighted manifolds. As a second contribution we present a new discretization method for the proposed equation using finite element approximation. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. We also propose a new method to introduce the scale invariance for the weighted heat kernel signature. The performance is tested on two benchmark datasets. The results have indeed confirmed the high performance of the proposed approach on the textured shape retrieval problem, and show that the proposed method is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.
A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images
Authors: Christopher Zach, Adrian Penate-Sanchez, Minh-Tri Pham
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object Recognition, Pose Estimation

Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem, and a standard random sampling based approach is prohibitively time-consuming. We propose to address this difficult problem by generating promising inlier sets for pose estimation by early rejection of clear outliers with the help of local belief propagation (or dynamic programming). By exploiting data-parallelism our method is fast, and we also do not rely on a computationally expensive training phase. We demonstrate state-of-the art performance on a standard dataset and illustrate our approach on challenging real sequences.
A Geodesic-Preserving Method for Image Warping
Authors: Dongping Li, Kaiming He, Jian Sun, Kun Zhou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Manipulation

The manipulation of panoramic/wide-angle images is usually achieved via image warping. Though various techniques have been developed for preserving shapes and straight lines for warping, these are not sufficient for panoramic/wide-angle images. The image projections will turn the straight lines into curved "geodesic lines", and it is fundamentally impossible to keep all these lines straight. In this work, we propose a geodesic-preserving method for content-aware image warping. An energy term is introduced to preserve the geodesic appearance of the geodesic lines, and can be used with shape-preserving terms. Our method is demonstrated in various applications, including rectangling panoramas, resizing panoramic/wide-angle images, and wide-angle image manipulation. An extension to ellipse preservation for general images is also presented.
Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition
Authors: Shaoxin Li, Junliang Xing, Zhiheng Niu, Shiguang Shan, Shuicheng Yan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Age And Gender Classification, Gender Classification, MORPH

One key challenge of facial traits recognition is the large non-rigid appearance variations due to irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangling such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.
From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement
Authors: Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc van Gool
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification, image-classification, Image Classification

The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover   finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to account for that semantic refinement. Image classification in general has improved significantly over  the last few years, but it still requires a massive amount of manually annotated data. Subdividing categories into subcategories multiples the number of labels, aggravating the annotation problem. Hence, we    can expect the annotations to be refined only for a subset of the already labeled data, and exploit coarser labeled data to improve classification. In this work, we investigate how coarse category labels can   be used to improve the classification of subcategories. To this end, we adopt the framework of Random Forests and propose a regularized objective function that takes into account relations between categories   and subcategories. Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image            classification experiments.