OpenRaiser/Intern-Atlas huggingface dataset:
Illumination and spatially varying specular reflectance from a single view
Authors: Kenji Hara, Ko Nishino
Venue: CVPR
Year: 2009

Estimating the illumination and the reflectance properties of an object surface from a sparse set of images is an important but inherently ill-posed problem. The problem becomes even harder if we wish to account for the spatial variation of material properties on the surface. In this paper, we derive a novel method for estimating the spatially varying specular reflectance properties, of a surface of known geometry, as well as the illumination distribution from a specular-only image, for instance, captured using polarization to separate reflection components. Unlike previous work, we do not assume the illumination to be a single point light source. We model specular reflection with a spherical statistical distribution and encode the spatial variation with radial basis functions of its parameters. This allows us to formulate the simultaneous estimation of spatially varying specular reflectance and illumination as a sound probabilistic inference problem, in particular, using Csiszar's I-divergence measure. To solve it, we derive an iterative algorithm similar to expectation maximization. We demonstrate the effectiveness of the method on synthetic and real-world scenes.
A unified active and semi-supervised learning framework for image compression
Authors: Xiaofei He 0001, Ming Ji, Hujun Bao
Venue: CVPR
Year: 2009

We consider the problem of lossy image compression from machine learning perspective. Typical image compression algorithms first transform the image from its spatial domain representation to frequency domain representation using some transform technique, such as discrete cosine transform and discrete wavelet transform, and then code the transformed values. Recently, instead of performing a frequency transformation, machine learning based approach has been proposed which uses the color information from a few representative pixels to learn a model which predicts color on the rest of the pixels. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose a novel active learning algorithm, called graph regularized experimental design (GRED), which shares the same principle of the semi-supervised learning algorithm used for colorization. This way, active and semi-supervised learning is unified into a single framework for pixel selection and colorization. Our experimental results suggest that the proposed approach achieves higher compression ratio and image quality, while the compression time is significantly reduced.
Single image haze removal using dark channel prior
Authors: Kaiming He, Jian Sun 0001, Xiaoou Tang
Venue: CVPR
Year: 2009

In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.
Discrete tracking of parametrized curves
Authors: Tim Hauke Heibel, Ben Glocker, Martin Groher, Nikos Paragios, Nikos Komodakis, Nassir Navab
Venue: CVPR
Year: 2009

A novel scheme for deformable tracking of curvilinear structures in image sequences is presented. The approach is based on B-spline snakes defined by a set of control points whose optimal configuration is determined through efficient discrete optimization. Each control point is associated with a discrete random variable in a MAP-MRF formulation where a set of labels captures the deformation space. In such a context, generic terms are encoded within this MRF in the form of pairwise potentials. The use of pairwise potentials along with the B-spline representation offers nearly perfect approximation of the continuous domain. Efficient linear programming is considered to recover the approximate optimal solution. The method is successfully applied to the tracking of guide-wires in fluoroscopic X-ray sequences of several hundred frames which requires extremely robust techniques.
Stereographic rectification of omnidirectional stereo pairs
Authors: Jan Heller, Tomás Pajdla
Venue: CVPR
Year: 2009

We present a general technique for rectification of a stereo pair acquired by a calibrated omnidirectional camera. Using this technique we formulate a new stereographic rectification method. Our rectification does not map epipolar curves onto lines as common rectification methods, but rather maps epipolar curves onto circles. We show that this rectification in a certain sense minimizes the distortion of the original omnidirectional images. We formulate the rectification for multiple images and show that the choice of the optimal projection center of the rectification is under certain circumstances equivalent to the classical problem of spherical minimax location. We demonstrate the behaviour and the quality of the rectification in real experiments with images from 180 degree field of view fish eye lenses.
Mutual information-based stereo matching combined with SIFT descriptor in log-chromaticity color space
Authors: Yong Seok Heo, Kyoung Mu Lee, Sang Uk Lee
Venue: CVPR
Year: 2009

Radiometric variations between input images can seriously degrade the performance of stereo matching algorithms. In this situation, mutual information is a very popular and powerful measure which can find any global relationship of intensities between two input images taken from unknown sources. The mutual information-based method, however, is still ambiguous or erroneous as regards local radiometric variations, since it only accounts for global variation between images, and does not contain spatial information properly. In this paper, we present a new method based on mutual information combined with SIFT descriptor to find correspondence for images which undergo local as well as global radiometric variations. We transform the input color images to log-chromaticity color space from which a linear relationship can be established. To incorporate spatial information in mutual information, we utilize the SIFT descriptor which includes near pixel gradient histogram to construct a joint probability in log-chromaticity color space. By combining the mutual information as an appearance measure and the SIFT descriptor as a geometric measure, we devise a robust and accurate stereo system. Experimental results show that our method is superior to the state-of-the art algorithms including conventional mutual information-based methods and window correlation methods under various radiometric changes.
Depth from sliding projections
Authors: Chris Hermans, Yannick Francken, Tom Cuypers, Philippe Bekaert
Venue: CVPR
Year: 2009

In this paper we present a novel method for 3D structure acquisition, based on structured light. Unlike classical structured light methods, in which a static projector illuminates a scene with time-varying illumination patterns, our technique makes use of a moving projector emitting a static striped illumination pattern. This projector is translated at a constant velocity, in the direction of the projector's horizontal axis. Illuminating the object in this manner allows us to perform a per pixel analysis, in which we decompose the recorded illumination sequence into a corresponding set of frequency components. The dominant frequency in this set can be directly converted into a corresponding depth value. This per pixel analysis allows us to preserve sharp edges in the depth image. Unlike classical structured light methods, the quality of our results is not limited by projector or camera resolution, but is solely dependent on the temporal sampling density of the captured image sequence. Additional benefits include a significant robustness against common problems encountered with structured light methods, such as occlusions, specular reflections, subsurface scattering, interreflections, and to a certain extent projector defocus.
Real-time learning of accurate patch rectification
Authors: Stefan Hinterstoisser, Oliver Kutter, Nassir Navab, Pascal Fua, Vincent Lepetit
Venue: CVPR
Year: 2009

Recent work showed that learning-based patch rectification methods are both faster and more reliable than affine region methods. Unfortunately, their performance improvements are founded in a computationally expensive offline learning stage, which is not possible for applications such as SLAM. In this paper we propose an approach whose training stage is fast enough to be performed at run-time without the loss of accuracy or robustness. To this end, we developed a very fast method to compute the mean appearances of the feature points over sets of small variations that span the range of possible camera viewpoints. Then, by simply matching incoming feature points against these mean appearances, we get a coarse estimate of the viewpoint that is refined afterwards. Because there is no need to compute descriptors for the input image, the method is very fast at run-time. We demonstrate our approach on tracking-by-detection for SLAM, real-time object detection and pose estimation applications.
Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation
Authors: Michael Hofmann 0005, Dariu M. Gavrila
Venue: CVPR
Year: 2009

We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single frame pose recovery, temporal integration and model adaptation. Single frame pose recovery consists of a hypothesis generation stage, where candidate 3D poses are generated based on hierarchical shape matching in the individual camera views. In the subsequent hypothesis verification stage, candidate 3D poses are reprojected to the other camera views and ranked according to a multiview matching score. Temporal integration consists of computing best trajectories combining a motion model and observations in a Viterbi style maximum likelihood approach. Poses that lie on the best trajectories are used to generate and adapt a texture model, which in turn enriches the shape component used for pose recovery. We demonstrate that our approach outperforms the state of the art in experiments with large and challenging real world data from an outdoor setting. The new data set is made public to facilitate benchmarking.
Distance transform templates for object detection and pose estimation
Authors: Stefan Holzer, Stefan Hinterstoisser, Slobodan Ilic, Nassir Navab
Venue: CVPR
Year: 2009

We propose a new approach for detecting low textured planar objects and estimating their 3D pose. Standard matching and pose estimation techniques often depend on texture and feature points. They fail when there is no or only little texture available. Edge-based approaches mostly can deal with these limitations but are slow in practice when they have to search for six degrees of freedom. We overcome these problems by introducing the distance transform templates, generated by applying the distance transform to standard edge based templates. We obtain robustness against perspective transformations by training a classifier for various template poses. In addition, spatial relations between multiple contours on the template are learnt and later used for outlier removal. At runtime, the classifier provides the identity and a rough 3D pose of the distance transform template, which is further refined by a modified template matching algorithm that is also based on the distance transform. We qualitatively and quantitatively evaluate our approach on synthetic and real-life examples and demonstrate robust real-time performance.
A collaborative benchmark for region of interest detection algorithms
Authors: Tz-Huan Huang, Kai-Yin Cheng, Yung-Yu Chuang
Venue: CVPR
Year: 2009

This paper presents a collaborative benchmark for region of interest (ROI) detection in images. ROI detection has many useful applications and many algorithms have been proposed to automatically detect ROIs. Unfortunately, due to the lack of benchmarks, these methods were often tested on small data sets that are not available to others, making fair comparisons of these methods difficult. Examples from many fields have shown that repeatable experiments using published benchmarks are crucial to the fast advancement of the fields. To fill the gap, this paper presents our design for a collaborative game, called Photoshoot, to collect human ROI annotations for constructing an ROI benchmark. Using this game, we have gathered a large number of annotations and fused them into aggregated ROI models. With these models, we are able to evaluate six ROI detection algorithms quantitatively.
Image deblurring for less intrusive iris capture
Authors: Xinyu Huang 0001, Liu Ren, Ruigang Yang
Venue: CVPR
Year: 2009

For most iris capturing scenarios, captured iris images could easily blur when the user is out of the depth of field (DOF) of the camera, or when he or she is moving. The common solution is to let the user try the capturing process again as the quality of these blurred iris images is not good enough for recognition. In this paper, we propose a novel iris deblurring algorithm that can be used to improve the robustness and nonintrusiveness for iris capture. Unlike other iris deblurring algorithms, the key feature of our algorithm is that we use the domain knowledge inherent in iris images and iris capture settings to improve the performance, which could be in the form of iris image statistics, characteristics of pupils or highlights, or even depth information from the iris capturing system itself. Our experiments on both synthetic and real data demonstrate that our deblurring algorithm can significantly restore blurred iris patterns and therefore improve the robustness of iris capture.
Active learning for large multi-class problems
Authors: Prateek Jain 0002, Ashish Kapoor
Venue: CVPR
Year: 2009

Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the K-nearest neighbor method for classification that can be seamlessly used for active learning in multi-class scenarios. Given some labeled training data, our method learns an accurate metric/kernel function over the input space that can be used for classification and similarity search. Unlike existing metric/kernel learning methods, our scheme is highly scalable for classification problems and provides a natural notion of uncertainty over class labels. Further, we use this measure of uncertainty to actively sample training examples that maximize discriminating capabilities of the model. Experiments on benchmark datasets show that the proposed method learns appropriate distance metrics that lead to state-of-the-art performance for object categorization problems. Furthermore, our active learning method effectively samples training examples, resulting in significant accuracy gains over random sampling for multi-class problems involving a large number of classes.
Vocabulary hierarchy optimization for effective and transferable retrieval
Authors: Rongrong Ji, Xing Xie 0001, Hongxun Yao, Wei-Ying Ma
Venue: CVPR
Year: 2009

Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of “visual words”; (2) the model cannot adapt to database variance. In this paper, we describe an unsupervised optimization strategy in generating the hierarchy structure of visual vocabulary, which produces a more effective and adaptive retrieval model for large-scale search. We adopt a novel Density-based Metric Learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain for efficient online search based on the vocabulary hierarchy. We also discovered that by hierarchy optimization, efficient and effective transfer of a retrieval model across different databases is feasible. We deployed a large-scale image retrieval system using a vocabulary tree model to validate our advances. Experiments on UKBench and street-side urban scene databases demonstrated the effectiveness of our hierarchy optimization approach in comparison with state-of-the-art methods.
Support Vector Machines in face recognition with occlusions
Authors: Hongjun Jia, Aleix M. Martínez
Venue: CVPR
Year: 2009

Support vector machines (SVM) are one of the most useful techniques in classification problems. One clear example is face recognition. However, SVM cannot be applied when the feature vectors defining our samples have missing entries. This is clearly the case in face recognition when occlusions are present in the training and/or testing sets. When k features are missing in a sample vector of class 1, these define an affine subspace of k dimensions. The goal of the SVM is to maximize the margin between the vectors of class 1 and class 2 on those dimensions with no missing elements and, at the same time, maximize the margin between the vectors in class 2 and the affine subspace of class 1. This second term of the SVM criterion will minimize the overlap between the classification hyperplane and the subspace of solutions in class 1, because we do not know which values in this subspace a test vector can take. The hyperplane minimizing this overlap is obviously the one parallel to the missing dimensions. However, this condition is too restrictive, because its solution will generally contradict that obtained when maximizing the margin of the visible data. To resolve this problem, we define a criterion which minimizes the probability of overlap. The resulting optimization problem can be solved efficiently and we show how the global minimum of the error term is guaranteed under mild conditions. We provide extensive experimental results, demonstrating the superiority of the proposed approach over the state of the art.
Efficient scale space auto-context for image segmentation and labeling
Authors: Jiayan Jiang, Zhuowen Tu
Venue: CVPR
Year: 2009

The Conditional Random Fields (CRF) model, using patch-based classification bound with context information, has recently been widely adopted for image segmentation/ labeling. In this paper, we propose three components for improving the speed and accuracy, and illustrate them on a recently developed auto-context algorithm [28]: (1) a new coding scheme for multiclass classification, named data-assisted output code (DAOC); (2) a scale-space approach to make it less sensitive to geometric scale change; and (3) a region-based voting scheme to make it faster and more accurate at object boundaries. The proposed multiclass classifier, DAOC, is general and particularly appealing when the number of class becomes large since it needs a minimal number of [log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> k] binary classifiers for k classes. We show advantages of the DAOC classifier over the existing algorithms on several Irvine repository datasets, as well as vision applications. Combining DAOC, the scale-space approach, and the region-based voting scheme for autocontext, the overall algorithm is significantly faster (5 ∼ 10 times) than the original auto-context, with improved accuracy over many of the existing algorithms on theMSRC [24] and VOC 2007 [7] datasets.
Linear solution to scale and rotation invariant object matching
Authors: Hao Jiang 0007, Stella X. Yu
Venue: CVPR
Year: 2009

Images of an object undergoing ego- or camera-motion often appear to be scaled, rotated, and deformed versions of each other. To detect and match such distorted patterns to a single sample view of the object requires solving a hard computational problem that has eluded most object matching methods. We propose a linear formulation that simultaneously finds feature point correspondences and global geometrical transformations in a constrained solution space. Further reducing the search space based on the lower convex hull property of the formulation, our method scales well with the number of candidate features. Our results on a variety of images and videos demonstrate that our method is accurate, efficient, and robust over local deformation, occlusion, clutter, and large geometrical transformations.
Learning a distance metric from multi-instance multi-label data
Authors: Rong Jin 0001, Shijun Wang, Zhi-Hua Zhou
Venue: CVPR
Year: 2009

Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image is represented by multiple key points (i.e., instances) and is assigned to multiple object categories. In this paper, we study the problem of learning a distance metric from multi-instance multi-label data. It is significantly more challenging than the conventional setup of distance metric learning because it is difficult to associate instances in a bag with its assigned class labels. We propose an iterative algorithm for MIML distance metric learning: it first estimates the association between instances in a bag and its assigned class labels, and learns a distance metric from the estimated association by a discriminative analysis; the learned metric will be used to update the association between instances and class labels, which is further used to improve the learning of distance metric. We evaluate the proposed algorithm by the task of automated image annotation, a well known MIML problem. Our empirical study shows an encouraging result when combining the proposed algorithm with citation-kNN, a state-of-theart algorithm for multi-instance learning.
Optimization of landmark selection for cortical surface registration
Authors: Anand A. Joshi, David W. Shattuck, Dimitrios Pantazis, Quanzheng Li, Hanna Damasio, Richard M. Leahy
Venue: CVPR
Year: 2009

Manually labeled landmark sets are often required as inputs for landmark-based image registration. Identifying an optimal subset of landmarks from a training dataset may be useful in reducing the labor intensive task of manual labeling. In this paper, we present a new problem and a method to solve it: given a set of N landmarks, find the k(< N) best landmarks such that aligning these k landmarks that produce the best overall alignment of all N landmarks. The resulting procedure allows us to select a reduced number of landmarks to be labeled as a part of the registration procedure. We apply this methodology to the problem of registering cerebral cortical surfaces extracted from MRI data. We use manually traced sulcal curves as landmarks in performing inter-subject registration of these surfaces. To minimize the error metric, we analyze the correlation structure of the sulcal errors in the landmark points by modeling them as a multivariate Gaussian process. Selection of the optimal subset of sulcal curves is performed by computing the error variance for the subset of unconstrained landmarks conditioned on the constrained set. We show that the registration error predicted by our method closely matches the actual registration error. The method determines optimal curve subsets of any given size with minimal registration error.