pwc-archive/papers-with-abstracts huggingface dataset:
Shadow Removal from Single RGB-D Images
Authors: Yao Xiao, Efstratios Tsougenis, Chi-Keung Tang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Intrinsic Image Decomposition, Shadow Removal

We present the first automatic method to remove shadows from single RGB-D images. Using normal cues directly derived from depth, we can remove hard and soft shadows while preserving surface texture and shading. Our key assumption is: pixels with similar normals, spatial locations and chromaticity should have similar colors. A modified nonlocal matching is used to compute a shadow confidence map that localizes well hard shadow boundary, thus handling hard and soft shadows within the same framework. We compare our results produced using state-of-the-art shadow removal on single RGB images, and intrinsic image decomposition on standard RGB-D datasets.
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples
Authors: Hongteng Xu, Hongyuan Zha, Mark A. Davenport
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Texture Synthesis

In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e.g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning. Decomposing a textural image as a set of patches, we achieve dynamic texture synthesis by estimating sequences of temporal patches. We select candidates for each temporal patch from spatial patches based on MRFs and regard them as samples from a low-dimensional manifold. After mapping candidates to a low-dimensional latent space, we estimate the sequence of temporal patches by finding an optimal trajectory in the latent space. Guided by some key properties of trajectories of realistic temporal patches, we derive a curvature-based trajectory selection algorithm. In contrast to the methods based on MRFs or dynamic systems that rely on a large amount of samples, our method is able to deal with the case of extremely few samples and requires no training phase. We compare our method with the state of the art and show that our method not only exhibits superior performance on synthesizing textures but it also produces results with pleasing visual effects.
The Synthesizability of Texture Examples
Authors: Dengxin Dai, Hayko Riemenschneider, Luc van Gool
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Texture Synthesis

Example-based texture synthesis (ETS) has been widely used to generate high quality textures of desired sizes from a small example. However, not all textures are equally well reproducible that way. We predict how synthesizable a particular texture is by ETS. We introduce a dataset (21,302 textures) of which all images have been annotated in terms of their synthesizability. We design a set of texture features, such as 'textureness', homogeneity, repetitiveness, and irregularity, and train a predictor using these features on the data collection. This work is the first attempt to quantify this image property, and we find that texture synthesizability can be learned and predicted. We use this insight to trim images to parts that are more synthesizable. Also we suggest which texture synthesis method is best suited to synthesise a given texture. Our approach can be seen as 'winner-uses-all': picking one method among several alternatives, ending up with an overall superior ETS method. Such strategy could also be considered for other vision tasks: rather than building an even stronger method, choose from existing methods based on some simple preprocessing.
Reconstructing Evolving Tree Structures in Time Lapse Sequences
Authors: Przemyslaw Glowacki, Miguel Amavel Pinheiro, Engin Turetken, Raphael Sznitman, Daniel Lebrecht, Jan Kybic, Anthony Holtmaat, Pascal Fua
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches. Instead of reconstructing structures in each image independently, we do so for all images simultaneously to take advantage of temporal-consistency constraints. We show that this problem can be formulated as a Quadratic Mixed Integer Program and solved efficiently. The outcome of our approach is a framework that provides substantial improvements in reconstructions over traditional single time-instance formulations. Furthermore, an added benefit of our approach is the ability to automatically detect places where significant changes have occurred over time, which is challenging when considering large amounts of data.
Total-Variation Minimization on Unstructured Volumetric Mesh: Biophysical Applications on Reconstruction of 3D Ischemic Myocardium
Authors: Jingjia Xu, Azar Rahimi Dehaghani, Fei Gao, Linwei Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

This paper describes the development and application of a new approach to total-variation (TV) minimization for reconstruction problems on geometrically-complex and unstructured volumetric mesh. The driving application of this study is the reconstruction of 3D ischemic regions in the heart from noninvasive body-surface potential data, where the use of a TV-prior can be expected to promote the reconstruction of two piecewise smooth regions of healthy and ischemic electrical properties with localized gradient in between. Compared to TV minimization on regular grids of pixels/voxels, the complex unstructured volumetric mesh of the heart poses unique challenges including the impact of mesh resolutions on the TV-prior and the difficulty of gradient calculation. In this paper, we introduce a variational TV-prior and, when combined with the iteratively re-weighted least-square concept, a new algorithm to TV minimization that is computationally efficient and robust to the discretization resolution. In a large set of simulation studies as well as two initial real-data studies, we show that the use of the proposed  TV prior outperforms L2-based penalties in reconstruct ischemic regions, and it shows higher robustness and efficiency compared to the commonly used discrete TV prior. We also investigate the performance of the proposed TV-prior in combination with a L2- versus L1-based data fidelity term. The proposed method can extend TV-minimization to a border range of applications that involves physical domains of complex shape and unstructured volumetric mesh.
Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI
Authors: Yuanxiang Wang, Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Diffusion  MRI

Tractography refers to the process of tracing out the nerve fiber bundles from diffusion Magnetic Resonance Images (dMRI) data acquired either in vivo or ex-vivo. Tractography is a mature research topic within the field of diffusion MRI analysis, nevertheless, several new methods are being proposed on a regular basis thereby justifying the need, as the problem is not fully solved. Tractography is usually applied to the model (used to represent the diffusion MR signal or a derived quantity) reconstructed from the acquired data. Separating shape and orientation of these models was previously shown to approximately preserve diffusion anisotropy (a useful bio-marker) in the ubiquitous problem of interpolation. However, no further intrinsic geometric properties of this framework were exploited to date in literature. In this paper, we propose a new intrinsic recursive filter on the product manifold of shape and orientation. The recursive filter, dubbed IUKFPro, is a generalization of the unscented Kalman filter (UKF) to this product manifold. The salient contributions of this work are: (1) A new intrinsic UKF for the product manifold of shape and orientation. (2) Derivation of the Riemannian geometry of the product manifold. (3) IUKFPro is tested on synthetic and real data sets from various tractography challenge competitions. From the experimental results, it is evident that IUKFPro performs better than several competing schemes in literature with regards to some of the error measures used in the competitions and is competitive with respect to others.
Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation
Authors: Erkang Cheng, Yu Pang, Ying Zhu, Jingyi Yu, Haibin Ling
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Motion Compensation

Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field as a result of complex 3D anatomical movements projected into 2D images. To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation. Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 l1 tensor approximation. The model prior is propagated in the course of deformable tracking.  Both the higher order information and the model prior provide powerful discriminative cues for reducing ambiguity arising from the complex background, and consequently improve the tracking robustness. To validate the proposed approach, we applied it to catheter and vascular structures tracking and tested on X-ray fluoroscopic sequences obtained from 17 clinical cases. The results show, both quantitatively and qualitatively, that our approach achieves a mean tracking error of 1.4 pixels for vascular structure and 1.3 pixels for catheter tracking.
Patch-based Evaluation of Image Segmentation
Authors: Christian Ledig, Wenzhe Shi, Wenjia Bai, Daniel Rueckert
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Hippocampus, Image Segmentation, Segmentation, Semantic Segmentation

The quantification of similarity between image segmentations is a complex yet important task. The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias. Similarity measures based on overlap, e.g. Dice score, or surface distances, e.g. Hausdorff distance, clearly do not satisfy all of these properties. To address this problem, we introduce Patch-based Evaluation of Image Segmentation (PEIS), a general method to assess segmentation quality. Our method is based on finding patch correspondences and the associated patch displacements, which allow the estimation of segmentation bias. We quantify both the agreement of the segmentation boundary and the conservation of the segmentation shape. We further assess the segmentation complexity within patches to weight the contribution of local segmentation similarity to the global score. We evaluate PEIS on both synthetic data and two medical imaging datasets. On synthetic segmentations of different shapes, we provide evidence that PEIS, in comparison to the Dice score, produces more comparable scores, has increased sensitivity and estimates segmentation bias accurately. On cardiac magnetic resonance (MR) images, we demonstrate that PEIS can evaluate the performance of a segmentation method independent of the size or complexity of the segmentation under consideration. On brain MR images, we compare five different automatic hippocampus segmentation techniques using PEIS. Finally, we visualise the segmentation bias on a selection of the cases.
Evaluation of Scan-Line Optimization for 3D Medical Image Registration
Authors: Simon Hermann
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Computed Tomography (CT), Image Registration, Medical Image Registration

Scan-line optimization via cost accumulation has become very popular for stereo estimation in computer vision applications and is often combined with a semi-global cost integration strategy, known as SGM.  This paper introduces this combination as a general and effective optimization technique. It is the first time that this concept is applied to 3D medical image registration.  The presented algorithm, SGM-3D, employs a coarse-to-fine strategy and reduces the search space dimension for consecutive pyramid levels by a fixed linear rate. This allows it to handle large displacements to an extent that is required for clinical applications in high dimensional data.  SGM-3D is evaluated in context of pulmonary motion analysis on the recently extended DIR-lab benchmark that provides ten 4D computed tomography (CT) image data sets, as well as ten challenging 3D CT scan pairs from the COPDgene study archive. Results show that both registration errors as well as run-time performance are very competitive with current state-of-the-art methods.
Classification of Histology Sections via Multispectral Convolutional Sparse Coding
Authors: Yin Zhou, Hang Chang, Kenneth Barner, Paul Spellman, Bahram Parvin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification

Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSDSPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]).
Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis
Authors: Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: feature selection

Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis  presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification
Authors: Luping Zhou, Lei Wang, Philip Ogunbona
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Functional Connectivity, General Classification

Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative method that may not necessarily perform well on classifying neuroimaging data. In this paper, we propose a learning framework to effectively improve the discriminative power of SICEs by taking advantage of the samples in the opposite class. We formulate our objective as convex optimization problems for both one-class and two-class classifications. By analyzing these optimization problems, we not only solve them efficiently in their dual form, but also gain insights into this new learning framework. The proposed framework is applied to analyzing the brain metabolic covariant networks built upon FDG-PET images for the prediction of the Alzheimer's disease, and shows significant improvement of classification performance for both one-class and two-class scenarios. Moreover, as SICE is a general method for learning undirected Gaussian graphical models, this paper has broader meanings beyond the scope of brain research.
A Bayesian Framework For the Local Configuration of Retinal Junctions
Authors: Touseef Ahmad Qureshi, Andrew Hunter, Bashir Al-Diri
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Segmentation

Retinal images contain forests of mutually intersecting and overlapping venous and arterial vascular trees. The geometry of these trees shows adaptation to vascular diseases including diabetes, stroke and hypertension. Segmentation of the retinal vascular network is complicated by inconsistent vessel contrast, fuzzy edges, variable image quality, media opacities, complex intersections and overlaps. This paper presents a Bayesian approach to resolving the configuration of vascular junctions to correctly construct the vascular trees. A probabilistic model of vascular joints (terminals, bridges and bifurcations) and their configuration in junctions is built, and Maximum A Posteriori (MAP) estimation used to select most likely configurations. The model is built using a reference set of 3010 joints extracted from the DRIVE public domain vascular segmentation dataset, and evaluated on 3435 joints from the DRIVE test set, demonstrating an accuracy of 95.2%.
Learning-Based Atlas Selection for Multiple-Atlas Segmentation
Authors: Gerard Sanroma, Guorong Wu, Yaozong Gao, Dinggang Shen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Recently, multi-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption of MAS is that multiple atlases encompass richer anatomical variability than a single atlas.  Therefore, we can label the target image more accurately by mapping the label information from the appropriate atlas images that have the most similar structures. The problem of atlas selection, however, still remains unexplored. Current state-of-the-art MAS methods rely on image similarity to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to segmentation performance and, thus may undermine segmentation results. To solve this simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would eventually lead to more accurate image segmentation. Our idea is to learn the relationship between the pairwise appearance of observed instances (a pair of atlas and target images) and their final labeling performance (in terms of Dice ratio). In this way, we can select the best atlases according to their expected labeling accuracy. It is worth noting that our atlas selection method is general enough to be integrated with existing MAS methods. As is shown in the experiments, we achieve significant improvement after we integrate our method with 3 widely used MAS methods on ADNI and LONI LPBA40 datasets.
Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-structured Inference
Authors: Tuan Anh Ngo, Gustavo Carneiro
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Left Ventricle Segmentation, Segmentation

We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks. This recently proposed level-set formulation achieves reasonably accurate results in several segmentation problems, and has the advantage of eliminating periodic re-initializations during the optimization process, and as a result it avoids numerical errors. Nevertheless, when applied to challenging problems, such as the left ventricle segmentation from short axis cine magnetic ressonance (MR) images, the accuracy obtained by this distance regularized level set is lower than the state of the art. The main reasons behind this lower accuracy are the dependence on good initial guess for the level set optimization and on reliable appearance models. We address these two issues with an innovative structured inference using deep belief networks that produces reliable initial guess and appearance model. The effectiveness of our method is demonstrated on the MICCAI 2009 left ventricle segmentation challenge, where we show that our approach achieves one of the most competitive results (in terms of segmentation accuracy) in the field.
FAST LABEL: Easy and Efficient Solution of Joint Multi-Label and Estimation Problems
Authors: Ganesh Sundaramoorthi, Byung-Woo Hong
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition. These problems jointly determine a parameter for each of the regions in the partition. Given an estimate of the parameters, a fast approximate solution to the multi-label sub-problem is derived by a global update that uses smoothing and thresholding. The method is empirically validated to be robust to fine details of the image that plague local solutions. Further, in comparison to global methods for the multi-label problem, the method is more efficient and it is easy for a non-specialist to implement. We give sample Matlab code for the multi-label Chan-Vese problem in this paper!  Experimental comparison to the state-of-the-art in multi-label solutions to Region Competition shows that our method achieves equal or better accuracy, with the main advantage being speed and ease of implementation.