pwc-archive/papers-with-abstracts huggingface dataset:
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples
Authors: Hangfan Liu, Ruiqin Xiong, Jian Zhang, Wen Gao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Denoising, Image Denoising

This paper proposes a new image denoising approach using adaptive signal modeling and adaptive soft-thresholding. It improves the image quality by regularizing all the patches in image based on distribution modeling in transform domain. Instead of using a global model for all patches, it employs content adaptive models to address the non-stationarity of image signals. The distribution model of each patch is estimated individually and can vary for different transform bands and for different patch locations. In particular, we allow the distribution model for each individual patch to have non-zero expectation. To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution. Irrelevant patches are excluded so that this non-local based modeling is more accurate than global modeling. Adaptive soft-thresholding is employed since we observed that the distribution of non-local samples can be approximated by Laplacian distribution. Experimental results show that the proposed scheme outperforms the state-of-the-art denoising methods such as BM3D and CSR in both the PSNR and the perceptual quality.
3D Scanning Deformable Objects With a Single RGBD Sensor
Authors: Mingsong Dou, Jonathan Taylor, Henry Fuchs, Andrew Fitzgibbon, Shahram Izadi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously. We demonstrate high quality scanning results in some challenging sequences, comparing with state of art nonrigid techniques, as well as ground truth data.
Nested Motion Descriptors
Authors: Jeffrey Byrne
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Activity Recognition, Optical Flow Estimation, Translation

A nested motion descriptor is a spatiotemporal representation of motion that is invariant to global camera translation, without requiring an explicit estimate of optical flow or camera stabilization.  This descriptor is a natural spatiotemporal extension of the nested shape descriptor to the representation of motion.  We demonstrate that the quadrature steerable pyramid can be used to pool phase, and that pooling phase rather than magnitude provides an estimate of camera motion.  This motion can be removed using the log-spiral normalization as introduced in the nested shape descriptor.  Furthermore, this structure enables an elegant visualization of salient motion using the reconstruction properties of the steerable pyramid.  We compare our descriptor to local motion descriptors, HOG-3D and HOG-HOF, and show improvements on three activity recognition datasets.
Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo
Authors: Gottfried Graber, Jonathan Balzer, Stefano Soatto, Thomas Pock
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Surface Reconstruction

We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects the geometry of the surface, and the strength of depth-map-based stereo, by avoiding costly computation of surface topology. The result is a near real-time variational reconstruction algorithm free of the staircasing artifacts that affect depth-map and plane-sweeping approaches. This is made possible by exploiting the gauge ambiguity to design a novel representation of the regularizer that is linear in the parameters and hence amenable to be optimized with state-of-the-art primal-dual numerical schemes.
Layered RGBD Scene Flow Estimation
Authors: Deqing Sun, Erik B. Sudderth, Hanspeter Pfister
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation, Scene Flow Estimation, Scene Segmentation

As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention. Although the depth information allows the recovery of 3D motion from a single view, it poses new challenges. In particular, depth boundaries are not well-aligned with RGB image edges and therefore not reliable cues to localize 2D motion boundaries. In addition,  methods that extend the 2D optical flow formulation to 3D still produce large errors in occlusion regions. To better use depth for occlusion reasoning, we propose a layered RGBD scene flow method that jointly solves for the scene segmentation and the motion. Our key observation is that the noisy depth is sufficient to decide the depth ordering of layers, thereby avoiding a computational bottleneck for RGB layered methods. Furthermore, the depth  enables us to estimate a per-layer 3D rigid motion to constrain the motion of each layer. Experimental results on both the Middlebury and real-world sequences demonstrate the effectiveness of the layered approach for RGBD scene flow estimation.
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite
Authors: Shuran Song, Samuel P. Lichtenberg, Jianxiong Xiao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Reconstruction, Scene Understanding

Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
Diversity-Induced Multi-View Subspace Clustering
Authors: Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Diversity, Face Clustering, Multi-view Subspace Clustering

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view features, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods.
Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection
Authors: Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We introduce tools and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen scientists -- crowd annotators who are passionate and knowledgeable about specific domains such as birds or airplanes.  We worked with citizen scientists and domain experts to collect NABirds, a new high quality dataset containing 48,562 images of North American birds with 555 categories, part annotations and bounding boxes.  We find that citizen scientists are significantly more accurate than Mechanical Turkers at zero cost.  We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.  Nevertheless, we found that learning algorithms are surprisingly robust to annotation errors and this level of training data corruption can lead to an acceptably small increase in test error if the training set has sufficient size.  At the same time, we found that an expert-curated high quality test set like NABirds is necessary to accurately measure the performance of fine-grained computer vision systems. We used NABirds to train a publicly available bird recognition service deployed on the web site of the Cornell Lab of Ornithology.
Early Burst Detection for Memory-Efficient Image Retrieval
Authors: Miaojing Shi, Yannis Avrithis, Herve Jegou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Retrieval

Recent works show that image comparison based on local descriptors is corrupted by visual bursts, which tend to dominate the image similarity. The existing strategies, like power-law normalization, improve the results by discounting the contribution of visual bursts to the image similarity.  In this paper, we propose to explicitly detect the visual bursts in an image at an early stage. We compare several detection strategies jointly taking into account feature similarity and geometrical quantities. The bursty groups are merged into meta-features, which are used as input to state-of-the-art image search systems such as VLAD or the selective match kernel.  Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query.  Extensive experiments performed on public benchmarks for visual retrieval show the benefits of our method, which achieves performance on par with the state of the art but with a significantly reduced complexity, thanks to the lower number of features fed to the indexing system.
Indoor Scene Structure Analysis for Single Image Depth Estimation
Authors: Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation

We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the scene, which models local depth jointly with mid-level and global scene structures. We formulate single image depth estimation as inference in a graphical model whose edges let us encode the interactions within and across the different layers of our hierarchy. Our method therefore still produces detailed depth estimates, but also leverages higher-level information about the scene. We demonstrate the benefits of our approach over local depth estimation methods on standard indoor datasets.
Light Field Layer Matting
Authors: Juliet Fiss, Brian Curless, Rick Szeliski
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Matting

In this paper, we use matting to separate foreground layers from light fields captured with a plenoptic camera. We represent the input 4D light field as a 4D background light field, plus a 2D spatially varying foreground color layer with alpha. Our method can be used to both pull a fore- ground matte and estimate an occluded background light field. Our method assumes that the foreground layer is thin and fronto-parallel, and is composed of a limited set of col- ors that are distinct from the background layer colors. Our method works well for thin, translucent, and blurred foreground occluders. Our representation can be used to render the light field from novel views, handling disocclusions while avoiding common artifacts.
Depth Camera Tracking With Contour Cues
Authors: Qian-Yi Zhou, Vladlen Koltun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Camera Pose Estimation, Pose Estimation

We present an approach for tracking camera pose in real time given a stream of depth images. Existing algorithms are prone to drift in the presence of smooth surfaces that destabilize geometric alignment. We show that useful contour cues can be extracted from noisy and incomplete depth input. These cues are used to establish correspondence constraints that carry information about scene geometry and constrain pose estimation. Despite ambiguities in the input, the presented contour constraints reliably improve tracking accuracy. Results on benchmark sequences and on additional challenging examples demonstrate the utility of contour cues for real-time camera pose estimation.
Radial Distortion Homography
Authors: Zuzana Kukelova, Jan Heller, Martin Bujnak, Tomas Pajdla
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Homography Estimation, Image Stitching

The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more.  Ignoring the radial distortion component in homography estimation---even for classical perspective cameras---may lead to significant errors or totally wrong estimates. In this paper, we fill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The first algorithm uses the minimal number of five image point correspondences and solves a nonlinear system of polynomial equations using the Groebner basis method. The second algorithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be efficiently used inside a RANSAC loop.
Just Noticeable Defocus Blur Detection and Estimation
Authors: Jianping Shi, Li Xu, Jiaya Jia
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Defocus Blur Detection

We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur descriptors based on local information cannot distinguish this type of small blur reliably from unblurred structures. We propose a simple yet effective blur feature via sparse representation and image decomposition. It directly establishes correspondence between sparse edge representation and blur strength estimation. Extensive experiments manifest the generality and robustness of this feature.
How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps
Authors: De-An Huang, Minghuang Ma, Wei-Chiu Ma, Kris M. Kitani
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Online Clustering

Our aim is to show how state-of-the-art computer vision techniques can be used to advance prehensile analysis (i.e., understanding the functionality of human hands). Prehensile analysis is a broad field of multi-disciplinary interest, where researchers painstakingly manually analyze hours of hand-object interaction videos to understand the mechanics of hand manipulation. In this work, we present promising empirical results indicating that wearable cameras and unsupervised clustering techniques can be used to automatically discover common modes of human hand use. In particular, we use a first-person point-of-view camera to record common manipulation tasks and leverage its strengths for reliably observing human hand use. To learn a diverse set of hand-object interactions, we propose a fast online clustering algorithm based on the Determinantal Point Process (DPP). Furthermore, we develop a hierarchical extension to the DPP clustering algorithm and show that it can be used to discover appearance-based grasp taxonomies. Using a purely data-driven approach, our proposed algorithm is able to obtain hand grasp taxonomies that roughly correspond to the classic Cutkosky grasp taxonomy. We validate our approach on over 10 hours of first-person point-of-view videos in both choreographed and real-life scenarios.
Rotating Your Face Using Multi-Task Deep Neural Network
Authors: Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, Junmo Kim
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition

Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature. In this scheme, preserving identity while rotating pose image is a crucial issue. This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity. The target pose can be controlled by the user's intention. This novel type of multi-task model significantly improves identity preservation over the single task model. By using all the synthesized controlled pose images, called Controlled Pose Image (CPI), for the pose- illumination- invariant feature and voting among the multiple face recognition results, we clearly outperform the state-of-the-art algorithms by more than 4~6% on the MultiPIE dataset.
Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks
Authors: Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Object, Object Localization, Object Recognition, Weakly-supervised Learning

Successful visual object recognition methods typically rely on training datasets containing lots of richly annotated images. Annotating object bounding boxes is both expensive and subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs similar or better compared to its fully-supervised counterparts using object bounding box annotation for training.
Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning
Authors: Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, Baowen Xu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dictionary Learning, Person Re-Identification, Super-Resolution

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD^2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD^2L.
Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution
Authors: Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, Zhongbo Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Image Deconvolution

Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fine scale texture structures while removing blur and noise. Various methods have been implemented in both spatial and transform domains, such as gradient based methods, nonlocal self-similarity methods, sparsity based methods.  However, each domain has its advantages and shortcomings, which can be complemented by each other. In this work we propose a new approach for efficient image deconvolution based on dual domain filters. In the deblurring process, we offer a hybrid method that a novel rolling guidance filter is used to ensure proper texture/structure separation, and then in the transform domain, we use the short-time Fourier transform to recover the textures while removing noise with energy shrinkage. Our hybrid algorithm that is surprisingly easy to implement, and experimental results clearly show that the proposed algorithm outperforms many state-of-the-art deconvolution algorithms in terms of both quantitative measure and visual perception quality.
Region-Based Temporally Consistent Video Post-Processing
Authors: Xuan Dong, Boyan Bonev, Yu Zhu, Alan L. Yuille
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Video Enhancement

We study the problem of temporally consistent video post-processing. Previous post-processing algorithms usually either fail to keep high fidelity or fail to keep temporal consistency of output videos. In this paper, we observe experimentally that many image/video enhancement algorithms enforce a spatially consistent prior on the enhancement. More precisely, within a local region, the enhancement is consistent, i.e., pixels with the same RGB values will get the same enhancement values. Using this prior, we segment each frame into several regions and temporally-spatially adjust the enhancement of regions of different frames, taking into account fidelity, temporal consistency and spatial consistency. User study, objective measurement and visual quality comparisons are conducted. The experimental results demonstrate that our output videos can keep high fidelity and temporal consistency at the same time.