OpenRaiser/Intern-Atlas huggingface dataset:
An analysis of using high-frequency sinusoidal illumination to measure the 3D shape of translucent objects
Authors: Michael Holroyd, Jason Lawrence
Venue: CVPR
Year: 2011

Using optical triangulation methods to measure the shape of translucent objects is difficult because subsurface scattering contaminates measurements of the “direct” reflection at the surface. A number of recent papers have shown that high-frequency sinusoidal illumination patterns allow isolating this direct component, which in turn enables accurate estimation of the shape of translucent objects. Despite these encouraging results, there is currently no rigorous mathematical analysis of the expected error in the measured surface as it relates to the parameters of these systems: the frequency of the projected sinusoid, the geometric configuration of the source and camera, and the optical properties of the target object. We present such an analysis, which confirms earlier empirical results and provides a much needed tool for designing 3D scanners for translucent objects.
Global temporal registration of multiple non-rigid surface sequences
Authors: Peng Huang 0001, Chris Budd, Adrian Hilton 0001
Venue: CVPR
Year: 2011

In this paper we consider the problem of aligning multiple non-rigid surface mesh sequences into a single temporally consistent representation of the shape and motion. A global alignment graph structure is introduced which uses shape similarity to identify frames for inter-sequence registration. Graph optimisation is performed to minimise the total non-rigid deformation required to register the input sequences into a common structure. The resulting global alignment ensures that all input sequences are resampled with a common mesh structure which preserves the shape and temporal correspondence. Results demonstrate temporally consistent representation of several public databases of mesh sequences for multiple people performing a variety of motions with loose clothing and hair.
Exploring relations of visual codes for image classification
Authors: Yongzhen Huang, Kaiqi Huang, Chong Wang, Tieniu Tan
Venue: CVPR
Year: 2011

The classic Bag-of-Features (BOF) model and its extensional work use a single value to represent a visual code. This strategy ignores the relation of visual codes. In this paper, we explore this relation and propose a new algorithm for image classification. It consists of two main parts: 1) construct the codebook graph wherein a visual code is linked with other codes; 2) describe each local feature using a pair of related codes, corresponding to an edge of the graph. Our approach contains richer information than previous BOF models. Moreover, we demonstrate that these models are special cases of ours. Various coding and pooling algorithms can be embedded into our framework to obtain better performance. Experiments on different kinds of image classification databases demonstrate that our approach can stably achieve excellent performance compared with various BOF models.
Salient coding for image classification
Authors: Yongzhen Huang, Kaiqi Huang, Yinan Yu, Tieniu Tan
Venue: CVPR
Year: 2011

The codebook based (bag-of-words) model is a widely applied model for image classification. We analyze recent coding strategies in this model, and find that saliency is the fundamental characteristic of coding. The saliency in coding means that if a visual code is much closer to a descriptor than other codes, it will obtain a very strong response. The salient representation under maximum pooling operation leads to the state-of-the-art performance on many databases and competitions. However, most current coding schemes do not recognize the role of salient representation, so that they may lead to large deviations in representing local descriptors. In this paper, we propose “salient coding”, which employs the ratio between descriptors' nearest code and other codes to describe descriptors. This approach can guarantee salient representation without deviations. We study salient coding on two sets of image classification databases (15-Scenes and PASCAL VOC2007). The experimental results demonstrate that our approach outperforms all other coding methods in image classification.
Supervised local subspace learning for continuous head pose estimation
Authors: Dong Huang 0007, Markus Storer, Fernando De la Torre, Horst Bischof
Venue: CVPR
Year: 2011

Head pose estimation from images has recently attracted much attention in computer vision due to its diverse applications in face recognition, driver monitoring and human computer interaction. Most successful approaches to head pose estimation formulate the problem as a nonlinear regression between image features and continuous 3D angles (i.e. yaw, pitch and roll). However, regression-like methods suffer from three main drawbacks: (1) They typically lack generalization and overfit when trained using a few samples. (2) They fail to get reliable estimates over some regions of the output space (angles) when the training set is not uniformly sampled. For instance, if the training data contains under-sampled areas for some angles. (3) They are not robust to image noise or occlusion. To address these problems, this paper presents Supervised Local Subspace Learning (SL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting. Moreover, because SL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is a generative model, it can deal with image noise. Experimental results on the CMU Multi-PIE and BU-3DFE database show the effectiveness of our approach in terms of accuracy and computational complexity.
Local isomorphism to solve the pre-image problem in kernel methods
Authors: Dong Huang 0007, Yuandong Tian, Fernando De la Torre
Venue: CVPR
Year: 2011

Kernel methods have been popular over the last decade to solve many computer vision, statistics and machine learning problems. An important, both theoretically and practically, open problem in kernel methods is the pre-image problem. The pre-image problem consists of finding a vector in the input space whose mapping is known in the feature space induced by a kernel. To solve the pre-image problem, this paper proposes a framework that computes an isomorphism between local Gram matrices in the input and feature space. Unlike existing methods that rely on analytic properties of kernels, our framework derives closed-form solutions to the pre-image problem in the case of non-differentiable and application-specific kernels. Experiments on the pre-image problem for visualizing cluster centers computed by kernel k-means and denoising high-dimensional images show that our algorithm outperforms state-of-the-art methods.
CrossTrack: Robust 3D tracking from two cross-sectional views
Authors: Mohamed E. Hussein 0001, Fatih Porikli, Rui Li 0053, Suayb S. Arslan
Venue: CVPR
Year: 2011

One of the challenges in radiotherapy of moving tumors is to determine the location of the tumor accurately. Existing solutions to the problem are either invasive or inaccurate. We introduce a non-invasive solution to the problem by tracking the tumor in 3D using bi-plane ultrasound image sequences. We present CrossTrack, a novel tracking algorithm in this framework. We pose the problem as recursive inference of 3D location and tumor boundary segmentation in the two ultrasound views using the tumor 3D model as a prior. For the segmentation task, a robust graph-based approach is deployed as follows: First, robust segmentation priors are obtained through the tumor 3D model. Second, a unified graph combining information across time and multiple views is constructed with a robust weighting function. For the tracking task, an effective mechanism for recovery from respiration-induced occlusion is introduced. Our experiments show the robustness of CrossTrack in handling challenging tumor shapes and disappearance scenarios, with sub-voxel accuracy, and almost 100% precision and recall, significantly outperforming baseline solutions.
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
Authors: Zhuolin Jiang, Zhe Lin 0001, Larry S. Davis
Venue: CVPR
Year: 2011

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistent constraint called `discriminative sparse-code error' and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse coding techniques for face and object category recognition under the same learning conditions.
Tracking low resolution objects by metric preservation
Authors: Nan Jiang 0016, Wenyu Liu 0001, Heng Su, Ying Wu 0001
Venue: CVPR
Year: 2011

Tracking low resolution (LR) targets is a practical yet quite challenging problem in real applications. The loss of discriminative details in the visual appearance of the L-R targets confronts most existing visual tracking methods. Although the resolution of the LR video inputs may be enhanced by super resolution (SR) techniques, the large computational cost for high-quality SR does not make it an attractive option. This paper presents a novel solution to track LR targets without performing explicit SR. This new approach is based on discriminative metric preservation that preserves the structure in the high resolution feature space for LR matching. In addition, we integrate metric preservation with differential tracking to derive a closed-form solution to motion estimation for LR video. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed approach.
Adaptive and discriminative metric differential tracking
Authors: Nan Jiang 0016, Wenyu Liu 0001, Ying Wu 0001
Venue: CVPR
Year: 2011

Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.
Scale and rotation invariant matching using linearly augmented trees
Authors: Hao Jiang 0007, Tai-Peng Tian, Stan Sclaroff
Venue: CVPR
Year: 2011

We propose a novel linearly augmented tree method for efficient scale and rotation invariant object matching. The proposed method enforces pairwise matching consistency defined on trees, and high-order constraints on all the sites of a template. The pairwise constraints admit arbitrary metrics while the high-order constraints use L1 norms and therefore can be linearized. Such a linearly augmented tree formulation introduces hyperedges and loops into the basic tree structure, but different from a general loopy graph, its special structure allows us to relax and decompose the optimization into a sequence of tree matching problems efficiently solvable by dynamic programming. The proposed method also works on continuous scale and rotation parameters; we can match with a scale up to any large number with the same efficiency. Our experiments on ground truth data and a variety of real images and videos show that the proposed method is efficient, accurate and reliable.
Learning effective human pose estimation from inaccurate annotation
Authors: Sam Johnson, Mark Everingham
Venue: CVPR
Year: 2011

The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses.
A deformation and lighting insensitive metric for face recognition based on dense correspondences
Authors: Anne Jorstad, David Jacobs 0001, Alain Trouvé
Venue: CVPR
Year: 2011

Face recognition is a challenging problem, complicated by variations in pose, expression, lighting, and the passage of time. Significant work has been done to solve each of these problems separately. We consider the problems of lighting and expression variation together, proposing a method that accounts for both variabilities within a single model. We present a novel deformation and lighting insensitive metric to compare images, and we present a novel framework to optimize over this metric to calculate dense correspondences between images. Typical correspondence cost patterns are learned between face image pairs and a Naïve Bayes classifier is applied to improve recognition accuracy. Very promising results are presented on the AR Face Database, and we note that our method can be extended to a broad set of applications.
Matching 2D image lines to 3D models: Two improvements and a new algorithm
Authors: Behzad Kamgar-Parsi, Behrooz Kamgar-Parsi
Venue: CVPR
Year: 2011

We revisit the problem of matching a set of lines in the 2D image to a set of corresponding lines in the 3D model for the following reasons. (a) Existing algorithms that treat lines as infinitely long contain a flaw, namely, the solutions found are not invariant with respect to the choice of the coordinate frame. The source of this flaw is in the way lines are represented. We propose a frame-independent representation for sets of infinite lines that removes the non-invariance flaw. (b) Algorithms for finding the best rigid transform are nonlinear optimizations that are sensitive to initialization and may result in unreliable and expensive solutions. We present a new recipe for initialization that exploits the 3D geometry of the problem and is applicable to all algorithms that perform the matching in the 3D scene. Experiments show that with this initialization all algorithms find the best transform. (c) We present a new efficient matching algorithm that is significantly faster than existing alternatives, since it does not require explicit evaluation of the cost function and its derivatives.
High-resolution hyperspectral imaging via matrix factorization
Authors: Rei Kawakami, Yasuyuki Matsushita, John Wright 0001, Moshe Ben-Ezra, Yu-Wing Tai, Katsushi Ikeuchi
Venue: CVPR
Year: 2011

Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras are severely limited in spatial resolution. In this paper, we introduce a simple new technique for reconstructing a very high-resolution hyperspectral image from two readily obtained measurements: A lower-resolution hyper-spectral image and a high-resolution RGB image. Our approach is divided into two stages: We first apply an unmixing algorithm to the hyperspectral input, to estimate a basis representing reflectance spectra. We then use this representation in conjunction with the RGB input to produce the desired result. Our approach to unmixing is motivated by the spatial sparsity of the hyperspectral input, and casts the unmixing problem as the search for a factorization of the input into a basis and a set of maximally sparse coefficients. Experiments show that this simple approach performs reasonably well on both simulations and real data examples.
Boundary preserving dense local regions
Authors: Jaechul Kim, Kristen Grauman
Venue: CVPR
Year: 2011

We propose a dense local region detector to extract features suitable for image matching and object recognition tasks. Whereas traditional local interest operators rely on repeatable structures that often cross object boundaries (e.g., corners, scale-space blobs), our sampling strategy is driven by segmentation, and thus preserves object boundaries and shape. At the same time, whereas existing region-based representations are sensitive to segmentation parameters and object deformations, our novel approach to robustly sample dense sites and determine their connectivity offers better repeatability. In extensive experiments, we find that the proposed region detector provides significantly better repeatability and localization accuracy for object matching compared to an array of existing detectors. In addition, we show our regions lead to excellent results on two benchmark tasks that require good feature matching: weakly supervised foreground discovery, and nearest neighbor-based object recognition.
Variable grouping for energy minimization
Authors: Taesup Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo
Venue: CVPR
Year: 2011

This paper addresses the problem of efficiently solving large-scale energy minimization problems encountered in computer vision. We propose an energy-aware method for merging random variables to reduce the size of the energy to be minimized. The method examines the energy function to find groups of variables which are likely to take the same label in the minimum energy state and thus can be represented by a single random variable. We propose and evaluate a number of extremely efficient variable grouping strategies. Experimental results show that our methods result in a dramatic reduction in the computational cost and memory requirements (in some cases by a factor of one hundred) with almost no drop in the accuracy of the final result. Comparative evaluation with efficient super-pixel generation methods, which are commonly used in variable grouping, reveals that our methods are far superior both in terms of accuracy and running time.
Fast unsupervised ego-action learning for first-person sports videos
Authors: Kris Makoto Kitani, Takahiro Okabe, Yoichi Sato 0001, Akihiro Sugimoto
Venue: CVPR
Year: 2011

Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering first-person action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster video content. Our approach assumes a completely unsupervised scenario, where labeled training videos are not available, videos are not pre-segmented and the number of ego-action categories are unknown. In our proposed framework we show that a stacked Dirichlet process mixture model can be used to automatically learn a motion histogram codebook and the set of ego-action categories. We quantitatively evaluate our approach on both in-house and public YouTube videos and demonstrate robust ego-action categorization across several sports genres. Comparative analysis shows that our approach outperforms other state-of-the-art topic models with respect to both classification accuracy and computational speed. Preliminary results indicate that on average, the categorical content of a 10 minute video sequence can be indexed in under 5 seconds.
Optimal similarity registration of volumetric images
Authors: Effrosini Kokiopoulou, Daniel Kressner, Michail Zervos, Nikos Paragios
Venue: CVPR
Year: 2011

This paper proposes a novel approach to optimally solve volumetric registration problems. The proposed framework exploits parametric dictionaries for sparse volumetric representations, ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> dissimilarities and DC (Difference of Convex functions) decomposition. The SAD (sum of absolute differences) criterion is applied to the sparse representation of the reference volume and a DC decomposition of this criterion with respect to the transformation parameters is derived. This permits to employ a cutting plane algorithm for determining the optimal relative transformation parameters of the query volume. It further provides a guarantee for the global optimality of the obtained solution, which–to the best of our knowledge–is not offered by any other existing approach. A numerical validation demonstrates the effectiveness and the large potential of the proposed method.
Reconstruction of relief objects from line drawings
Authors: Michael Kolomenkin, George Leifman, Ilan Shimshoni, Ayellet Tal
Venue: CVPR
Year: 2011

This paper addresses the problem of automatic reconstruction of a 3D relief from a line drawing on top of a given base object. Reconstruction is challenging due to four reasons-the sparsity of the strokes, their ambiguity, their large number, and their inter-relations. Our approach is able to reconstruct a model from a complex drawing that consists of many inter-related strokes. Rather than viewing the inter-dependencies as a problem, we show how they can be exploited to automatically generate a good initial interpretation of the line drawing. Then, given a base and an interpretation, we propose an algorithm for reconstructing a consistent surface. The strength of our approach is demonstrated in the reconstruction of archaeological artifacts from drawings. These drawings are highly challenging, since artists created very complex and detailed descriptions of artifacts regardless of any considerations concerning their future use for shape reconstruction.
Efficient training for pairwise or higher order CRFs via dual decomposition
Authors: Nikos Komodakis
Venue: CVPR
Year: 2011

We present a very general algorithmic framework for structured prediction learning that is able to efficiently handle both pairwise and higher-order discrete MRFs/CRFs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . It relies on a dual decomposition approach that has been recently proposed for MRF optimization. By properly combining this approach with a max-margin method, our framework manages to reduce the training of a complex high-order MRF to the parallel training of a series of simple slave MRFs that are much easier to handle. This leads to an extremely efficient and general learning scheme. Furthermore, the proposed framework can yield learning algorithms of increasing accuracy since it naturally allows a hierarchy of convex relaxations to be used for MRF inference within a max-margin learning approach. It also offers extreme flexibility and can be easily adapted to take advantage of any special structure of a given class of MRFs. Experimental results demonstrate the great effectiveness of our method.