pwc-archive/papers-with-abstracts huggingface dataset:
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction
Authors: Chen Chen, Junzhou Huang, Lei He, Hongsheng Li
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Compressive Sensing

In this paper, we propose a novel algorithm for structured sparsity reconstruction. This algorithm is based on the iterative reweighted least squares (IRLS) framework, and accelerated by the preconditioned conjugate gradient method.  The convergence rate of the proposed algorithm is almost the same as that of the traditional IRLS algorithms, that is, exponentially fast. Moreover, with the devised preconditioner, the computational cost for each iteration is significantly less than that of traditional IRLS algorithms, which makes it feasible for large scale problems. Besides the fast convergence, this algorithm can be flexibly applied to standard sparsity, group sparsity, and overlapping group sparsity problems. Experiments are conducted on a practical application compressive sensing magnetic resonance imaging. Results demonstrate that the proposed algorithm achieves superior performance over 9 state-of-the-art algorithms in terms of both accuracy and computational cost.
Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis
Authors: Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Recently, there has been a great interest in computer-aided Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) diagnosis. Previous learning based methods defined the diagnosis process as a classification task and directly used the low-level features extracted from neuroimaging data without considering relations among them. However, from a neuroscience point of view, it's well known that a human brain is a complex system that multiple brain regions are anatomically connected and functionally inter- act with each other. Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways. To this end, in this paper, we first devise a coupled feature representation by utilizing intra-coupled and inter-coupled interaction relationship. Regarding multi-modal data fusion, we propose a novel coupled boosting algorithm that analyzes the pairwise coupled-diversity correlation between modalities. Specifically, we formulate a new weight updating function, which considers both incorrectly and inconsistently classified samples. In our experiments on the ADNI dataset, the proposed method presented the best performance with accuracies of 94.7% and 80.1% for AD vs. Normal Control (NC) and MCI vs. NC classifications, respectively, outperforming the competing methods and the state-of-the-art methods.
Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields
Authors: Demian Wassermann, James Ross, George Washko, William M. Wells III, Raul San Jose-Estepar
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees.
Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning
Authors: Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames. For this challenging problem, we propose a novel model which handles occlusions, complex motions and non-rigid deformations by jointly optimizing the flows of multiple latent intensities across frames. These flows are latent variables for which the user cannot directly provide labels. Instead, we leverage a structured learning formulation that uses weak user annotations to find the best hyperparameters of this model. The approach is evaluated on a challenging dataset for the tracking of multiple Drosophila larvae which we make publicly available. Our method tracks multiple larvae in spite of their poor distinguishability and minimizes the number of identity switches during prolonged mutual occlusion.
Scale-space Processing Using Polynomial Representations
Authors: Gou Koutaki, Keiichi Uchimura
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Translation

In this study, we propose the application of principal components analysis (PCA) to scale-spaces. PCA is a standard method used in computer vision. The translation of an input image into scale-space is a continuous operation, which requires the extension of conventional finite matrix-based PCA to an infinite number of dimensions. In this study, we use spectral decomposition to resolve this infinite eigenproblem by integration and we propose an approximate solution based on polynomial equations. To clarify its eigensolutions, we apply spectral decomposition to the Gaussian scale-space and scale-normalized Laplacian of Gaussian (LoG) space. As an application of this proposed method, we introduce a method for generating Gaussian blur images and scale-normalized LoG images, where we demonstrate that the accuracy of these images can be very high when calculating an arbitrary scale using a simple linear combination. We also propose a new Scale Invariant Feature Transform (SIFT) detector as a more practical example.
Single Image Layer Separation using Relative Smoothness
Authors: Yu Li, Michael S. Brown
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Intrinsic Image Decomposition, Reflection Removal

This paper addresses extracting two layers from an image where one layer is smoother than the other.  This problem arises most notably in intrinsic image decomposition and reflection interference removal.  Layer decomposition from a single-image is inherently ill-posed and solutions require additional constraints to be enforced.  We introduce a novel strategy that regularizes the gradients of the two layers such that one has a long tail distribution and the other a short tail distribution.  While imposing the long tail distribution is a common practice, our introduction of the short tail distribution on the second layer is unique.  We formulate our problem in a probabilistic framework and describe an optimization scheme to solve this regularization with only a few iterations.  We apply our approach to the intrinsic image and reflection removal problems and demonstrate high quality layer separation on par with other techniques but being significantly faster than prevailing methods.
Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity
Authors: Chen Chen, Yeqing Li, Wei Liu, Junzhou Huang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location. Different from previous methods, we do not make any assumption about the upsampled multispectral image, but only assume that the fused image after downsampling should be close to the original multispectral image. This is a severely ill-posed problem and a dynamic gradient sparsity penalty is thus proposed for regularization. Incorporating the intra- correlations of different bands, this penalty can effectively exploit the prior information (e.g. sharp boundaries) from the panchromatic image. A new convex optimization algorithm is proposed to efficiently solve this problem. Extensive experiments on four multispectral datasets demonstrate that the proposed method significantly outperforms the state-of-the-arts in terms of both spatial and spectral qualities.
Segmentation-Free Dynamic Scene Deblurring
Authors: Tae Hyun Kim, Kyoung Mu Lee
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring, Motion Segmentation, Segmentation

Most state-of-the-art dynamic scene deblurring methods based on accurate motion segmentation assume that motion blur is small or that the specific type of motion causing the blur is known. In this paper, we study a motion segmentation-free dynamic scene deblurring method, which is unlike other conventional methods. When the motion can be approximated to linear motion that is locally (pixel-wise) varying, we can handle various types of blur caused by camera shake, including out-of-plane motion, depth variation, radial distortion, and so on. Thus, we propose a new energy model simultaneously estimating motion flow and the latent image based on robust total variation (TV)-L1 model. This approach is necessary to handle abrupt changes in motion without segmentation. Furthermore, we address the problem of the traditional coarse-to-fine deblurring framework, which gives rise to artifacts when restoring small structures with distinct motion. We thus propose a novel kernel re-initialization method which reduces the error of motion flow propagated from a coarser level. Moreover, a highly effective convex optimization-based solution mitigating the computational difficulties of the TV-L1 model is established. Comparative experimental results on challenging real blurry images demonstrate the efficiency of the proposed method.
Shrinkage Fields for Effective Image Restoration
Authors: Uwe Schmidt, Stefan Roth
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Computational Efficiency, Image Restoration

Many state-of-the-art image restoration approaches do not scale well to larger images, such as megapixel images common in the consumer segment. Computationally expensive optimization is often the culprit. While efficient alternatives exist, they have not reached the same level of image quality. The goal of this paper is to develop an effective approach to image restoration that offers both computational efficiency and high restoration quality. To that end we propose shrinkage fields, a random field-based architecture that combines the image model and the optimization algorithm in a single unit. The underlying shrinkage operation bears connections to wavelet approaches, but is used here in a random field context. Computational efficiency is achieved by construction through the use of convolution and DFT as the core components; high restoration quality is attained through loss-based training of all model parameters and the use of a cascade architecture. Unlike heavily engineered solutions, our learning approach can be adapted easily to different trade-offs between efficiency and image quality. We demonstrate state-of-the-art restoration results with high levels of computational efficiency, and significant speedup potential through inherent parallelism.
Camouflaging an Object from Many Viewpoints
Authors: Andrew Owens, Connelly Barnes, Alex Flint, Hanumant Singh, William Freeman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object

We address the problem of camouflaging a 3D object from the many viewpoints that one might see it from. Given photographs of an object's surroundings, we produce a surface texture that will make the object difficult for a human to detect. To do this, we introduce several background matching algorithms that attempt to make the object look like whatever is behind it. Of course, it is impossible to exactly match the background from every possible viewpoint. Thus our models are forced to make trade-offs between different perceptual factors, such as the conspicuousness of the occlusion boundaries and the amount of texture distortion. We use experiments with human subjects to evaluate the effectiveness of these models for the task of camouflaging a cube, finding that they significantly outperform naïve strategies.
Learning Optimal Seeds for Diffusion-based Salient Object Detection
Authors: Song Lu, Vijay Mahadevan, Nuno Vasconcelos
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Superpixels

In diffusion-based saliency detection, an image is partitioned into superpixels and mapped to a graph, with superpixels as nodes and edge strengths proportional to superpixel similarity. Saliency information is then propagated over the graph using a diffusion process, whose equilibrium state yields the object saliency map. The optimal solution is the product of a propagation matrix and a saliency seed vector that contains a prior saliency assessment. This is obtained from either a bottom-up saliency detector or some heuristics. In this work, we propose a method to learn optimal seeds for object saliency. Two types of features are computed per superpixel: the bottom-up saliency of the superpixel region and a set of mid-level vision features informative of how likely the superpixel is to belong to an object. The combination of features that best discriminates between object and background saliency is then learned, using a large-margin formulation of the discriminant saliency principle. The propagation of the resulting saliency seeds, using a diffusion process, is finally shown to outperform the state of the art on a number of salient object detection datasets.
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
Authors: Eleonora Vig, Michael Dorr, David Cox
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Hyperparameter Optimization, Saliency Prediction

Saliency prediction typically relies on hand-crafted (multiscale) features that are combined in different ways to form a "master" saliency map, which encodes local image conspicuity. Recent improvements to the state of the art on standard benchmarks such as MIT1003 have been achieved mostly by incrementally adding more and more hand-tuned features (such as car or face detectors) to existing models. In contrast, we here follow an entirely automatic data-driven approach that performs a large-scale search for optimal features. We identify those instances of a richly-parameterized bio-inspired model family (hierarchical neuromorphic networks) that successfully predict image saliency. Because of the high dimensionality of this parameter space, we use automated hyperparameter optimization to efficiently guide the search. The optimal blend of such multilayer features combined with a simple linear classifier achieves excellent performance on several image saliency benchmarks. Our models outperform the state of the art on MIT1003, on which features and classifiers are learned. Without additional training, these models generalize well to two other image saliency data sets, Toronto and NUSEF, despite their different image content. Finally, our algorithm scores best of all the 23 models evaluated to date on the MIT300 saliency challenge, which uses a hidden test set to facilitate an unbiased comparison.
Saliency Detection on Light Field
Authors: Nianyi Li, Jinwei Ye, Yu Ji, Haibin Ling, Jingyi Yu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Saliency Detection

Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions. We explore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. We show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. We further develop a new saliency detection algorithm tailored for light fields. To validate our approach, we acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that our saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions,  etc., and achieve high accuracy and robustness.
Saliency Optimization from Robust Background Detection
Authors: Wangjiang Zhu, Shuang Liang, Yichen Wei, Jian Sun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Recent progresses in salient object detection have exploited the boundary prior, or background information, to assist other saliency cues such as contrast, achieving state-of-the-art results. However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic. In this work, we present new methods to address these issues. First, we propose a robust background measure, called boundary connectivity. It characterizes the spatial layout of image regions with respect to image boundaries and is much more robust. It has an intuitive geometrical interpretation and presents unique benefits that are absent in previous saliency measures. Second, we propose a principled optimization framework to integrate multiple low level cues, including our background measure, to obtain clean and uniform saliency maps. Our formulation is intuitive, efficient and achieves state-of-the-art results on several benchmark datasets.
100+ Times Faster Weighted Median Filter (WMF)
Authors: Qi Zhang, Li Xu, Jiaya Jia
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 2D Semantic Segmentation task 1 (8 classes), Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation. But it is hard to be accelerated due to the spatially varying weight and the median property. We propose a few efficient schemes to reduce computation complexity from O(r^2) to O(r) where r is the kernel size. Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access. The effectiveness of these schemes is demonstrated on optical flow estimation, stereo matching, structure-texture separation, image filtering, to name a few. The running time is largely shortened from several minutes to less than 1 second. The source code is provided in the project website.
Edge-aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation
Authors: Miao Hua, Xiaohui Bie, Minying Zhang, Wencheng Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Colorization, Image Colorization, image smoothing

Gradient domain methods are popular for image processing. However, these methods even the edge-preserving ones cannot preserve edges well in some cases. In this paper, we present new constraints explicitly to better preserve edges for general gradient domain image filtering and theoretically analyse why these constraints are edge-aware. Our edge-aware constraints are easy to implement, fast to compute and can be seamlessly integrated into the general gradient domain optimization framework. The improved framework can better preserve edges while maintaining similar image filtering effects as the original image filters. We also demonstrate the strength of our edge-aware constraints on various applications such as image smoothing, image colorization and Poisson image cloning.
Super-Resolving Noisy Images
Authors: Abhishek Singh, Fatih Porikli, Narendra Ahuja
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Super-Resolution

Our goal is to obtain a noise-free, high resolution (HR) image, from an observed, noisy, low resolution (LR) image. The conventional approach of preprocessing the image with a denoising algorithm, followed by applying a super-resolution (SR) algorithm, has an important limitation: Along with noise, some high frequency content of the image (particularly textural detail) is invariably lost during the denoising step. This 'denoising loss' restricts the performance of the subsequent SR step, wherein the challenge is to synthesize such textural details. In this paper, we show that high frequency content in the noisy image (which is ordinarily removed by denoising algorithms) can be effectively used to obtain the missing textural details in the HR domain. To do so, we first obtain HR versions of both the noisy and the denoised images, using a patch-similarity based SR algorithm. We then show that by taking a convex combination of orientation and frequency selective bands of the noisy and the denoised HR images, we can obtain a desired HR image where (i) some of the textural signal lost in the denoising step is effectively recovered in the HR domain, and (ii) additional textures can be easily synthesized by appropriately constraining the parameters of the convex combination. We show that this part-recovery and part-synthesis of textures through our algorithm yields HR images that are visually more pleasing than those obtained using the conventional processing pipeline. Furthermore, our results show a consistent improvement in numerical metrics, further corroborating the ability of our algorithm to recover lost signal.
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images
Authors: Xiaowu Chen, Dongqing Zou, Jianwei Li, Xiaochun Cao, Qinping Zhao, Hao Zhang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Dictionary Learning, global-optimization, Vocal Bursts Intensity Prediction

We introduce a method of sparse dictionary learning for edit propagation of high-resolution images or video. Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a compact set of representative samples (or features) and perform edit propagation on the samples instead. The sparse set of samples provides an intrinsic basis for an input image, and the coding coefficients capture the linear relationship between all pixels and the samples. The representative set of samples is then optimized by a novel scheme which maximizes the KL-divergence between each sample pair to remove redundant samples. We show several applications of sparsity-based edit propagation including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. We demonstrate that with a sample-to-pixel ratio in the order of 0.01%, signifying a significant reduction on memory consumption, our method still maintains a high-degree of visual fidelity.