OpenRaiser/Intern-Atlas huggingface dataset:
StaRSaC: Stable random sample consensus for parameter estimation
Authors: Jongmoo Choi, Gérard G. Medioni
Venue: CVPR
Year: 2009

We address the problem of parameter estimation in presence of both uncertainty and outlier noise. This is a common occurrence in computer vision: feature localization is performed with an inherent uncertainty which can be described as Gaussian, with unknown variance; feature matching in multiple images produces incorrect data points. RANSAC is the preferred method to reject outliers if the variance of the uncertainty noise is known, but fails otherwise, by producing either a tight fit to an incorrect solution, or by computing a solution which includes outliers. We thus propose a new estimator which enforces stability of the solution with respect to the uncertainty bound. We show that the variance of the estimated parameters (VoP) exhibits ranges of stability with respect to this bound. Within this range of stability, we can accurately segment the inliers, and estimate the parameters, the variance of the Gaussian noise. We show how to compute this stable range using RANSAC and a search. We validate our results by extensive tests and comparison with state of the art estimators on both synthetic and real data sets. These include line fitting, homography estimation, and fundamental matrix estimation. The proposed method outperforms all others.
Increased discrimination in level set methods with embedded conditional random fields
Authors: Dana Cobzas, Mark Schmidt 0001
Venue: CVPR
Year: 2009

We propose a novel approach for improving level set segmentation methods by embedding the potential functions from a discriminatively trained conditional random field (CRF) into a level set energy function. The CRF terms can be efficiently estimated and lead to both discriminative local potentials and edge regularizers that take into account interactions among the labels. Unlike discrete CRFs, the use of a continuous level set framework allows the natural use of flexible continuous regularizers such as shape priors. We show promising experimental results for the method on two difficult medical image segmentation tasks.
Learning signs from subtitles: A weakly supervised approach to sign language recognition
Authors: Helen Cooper, Richard Bowden
Venue: CVPR
Year: 2009

This paper introduces a fully automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Based on head and hand tracking, a novel temporally constrained adaptation of a priori mining is used to extract similar regions of video, with the aid of a proposed contextual negative selection method. These regions are refined in the temporal domain to isolate the occurrences of similar signs in each example. The system is shown to automatically identify and segment signs from standard news broadcasts containing a variety of topics.
Learning from ambiguously labeled images
Authors: Timothée Cour, Benjamin Sapp, Chris Jordan, Benjamin Taskar
Venue: CVPR
Year: 2009

In many image and video collections, we have access only to partially labeled data. For example, personal photo collections often contain several faces per image and a caption that only specifies who is in the picture, but not which name matches which face. Similarly, movie screenplays can tell us who is in the scene, but not when and where they are on the screen. We formulate the learning problem in this setting as partially-supervised multiclass classification where each instance is labeled ambiguously with more than one label. We show theoretically that effective learning is possible under reasonable assumptions even when all the data is weakly labeled. Motivated by the analysis, we propose a general convex learning formulation based on minimization of a surrogate loss appropriate for the ambiguous label setting. We apply our framework to identifying faces culled from Web news sources and to naming characters in TV series and movies. We experiment on a very large dataset consisting of 100 hours of video, and in particular achieve 6% error for character naming on 16 episodes of LOST.
Capturing multiple illumination conditions using time and color multiplexing
Authors: Bert de Decker, Jan Kautz, Tom Mertens, Philippe Bekaert
Venue: CVPR
Year: 2009

Many vision and graphics problems such as relighting, structured light scanning and photometric stereo, need images of a scene under a number of different illumination conditions. It is typically assumed that the scene is static. To extend such methods to dynamic scenes, dense optical flow can be used to register adjacent frames. This registration becomes inaccurate if the frame rate is too low with respect to the degree of movement in the scenes. We present a general method that extends time multiplexing with color multiplexing in order to better handle dynamic scenes. Our method allows for packing more illumination information into a single frame, thereby reducing the number of required frames over which optical flow must be computed. Moreover, color-multiplexed frames lend themselves better to reliably computing optical flow. We show that our method produces better results compared to time-multiplexing alone. We demonstrate its application to relighting, structured light scanning and photometric stereo in dynamic scenes.
Catadioptric projectors
Authors: Yuanyuan Ding, Jing Xiao 0006, Kar-Han Tan, Jingyi Yu
Venue: CVPR
Year: 2009

We present a catadioptric projector analogous to a catadioptric camera by combining a commodity digital projector with additional optical units. We show that, by using specially shaped reflectors/refractors, catadioptric projectors can offer an unprecedented level of flexibility in aspect ratio, size, and field of view. We also present efficient algorithms to reduce projection artifacts in catadioptric projectors, such as distortions, scattering, and defocusing. Instead of recovering the reflector/refractor geometry, our approach directly models the light transport between the projector and the viewpoint using the light transport matrix (LTM). We show how to efficiently approximate the pseudo inverse of the LTM and apply it to find the optimal input image that produces least projection distortions. Furthermore, we present a projection defocus analysis for reflector and thin refractor based catadioptric projectors. We show that defocus blur can be interpreted as spatially-varying Gaussian blurs on the input image. We then measure the kernels directly from the LTM and apply deconvolution to optimize the input image. We demonstrate the practical uses of catadioptric projectors in panoramic and omni-directional projections. Our new system achieves much wider field-of-view projection while maintaining sharpness and low geometric and photometric distortions.
An empirical study of context in object detection
Authors: Santosh Kumar Divvala, Derek Hoiem, James Hays, Alexei A. Efros, Martial Hebert
Venue: CVPR
Year: 2009

This paper presents an empirical evaluation of the role of context in a contemporary, challenging object detection task - the PASCAL VOC 2008. Previous experiments with context have mostly been done on home-grown datasets, often with non-standard baselines, making it difficult to isolate the contribution of contextual information. In this work, we present our analysis on a standard dataset, using top-performing local appearance detectors as baseline. We evaluate several different sources of context and ways to utilize it. While we employ many contextual cues that have been used before, we also propose a few novel ones including the use of geographic context and a new approach for using object spatial support.
Bias reduction for stereo based motion estimation with applications to large scale visual odometry
Authors: Gijs Dubbelman, Frans C. A. Groen
Venue: CVPR
Year: 2009

This contribution addresses the problem of bias in stereo based motion estimation. Using a biased estimator within a visual-odometry system will cause significant drift on large trajectories. This drift is often minimized by exploiting auxiliary sensors, (semi-)global optimization or loop-closing. In this paper it is shown that bias in the motion estimates can be caused by incorrect modeling of the uncertainties in landmark locations. Furthermore, there exists a relation between the bias, the true motion and the distribution of landmarks in space. Guided by these observations, a novel bias reduction technique has been developed. The core of the proposed method is computing the difference between motion estimates obtained using dissimilar heteroscedastic landmark uncertainty models. This approach is accurate, efficient and does not rely on auxiliary sensors, (semi-)global optimization or loop-closing. To show the real-world applicability of the proposed method, it has been tested on several data-sets including a challenging 5 km urban trajectory. The gain in performance is clearly noticeable.
Interval HSV: Extracting ink annotations
Authors: John Femiani 0001, Anshuman Razdan
Venue: CVPR
Year: 2009

The HSV color space is an intuitive way to reason about color, but the nonlinear relationship to RGB coordinates complicates histogram analysis of colors in HSV. We present novel Interval-HSV formulas to identify a range in HSV for each RGB interval. We show the usefulness by introducing a parameter-free and completely automatic technique to extract both colored and black ink annotations from faded backgrounds such as digitized aerial photographs, maps, or printed-text documents. We discuss the characteristics of ink mixing in the HSV color space and discover a single feature, the upper limit of the saturation-interval, to extract ink even when it is achromatic. We form robust Interval-HV histograms in order to identify the number and colors of inks in the image.
Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information
Authors: Shaolei Feng 0001, Shaohua Kevin Zhou, Sara Good, Dorin Comaniciu
Venue: CVPR
Year: 2009

3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, manually searching for the optimal view of the fetal face in 3D ultrasound volumes is cumbersome and time-consuming even for expert physicians and sonographers. In this paper we propose a learning-based approach which combines both 3D and 2D information for automatic and fast fetal face detection from 3D ultrasound volumes. Our approach applies a new technique - constrained marginal space learning - for 3D face mesh detection, and combines a boosting-based 2D profile detection to refine 3D face pose. To enhance the rendering of the fetal face, an automatic carving algorithm is proposed to remove all obstructions in front of the face based on the detected face mesh. Experiments are performed on a challenging 3D ultrasound data set containing 1010 fetal volumes. The results show that our system not only achieves excellent detection accuracy but also runs very fast - it can detect the fetal face from the 3D data in 1 second on a dual-core 2.0 GHz computer.
Observable subspaces for 3D human motion recovery
Authors: Andrea Fossati, Mathieu Salzmann, Pascal Fua
Venue: CVPR
Year: 2009

The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. However, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. In this paper, we show that by directly using observable quantities as our latent variables, we eliminate this problem and achieve full automation given only modest amounts of training data. More specifically, we exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables. We treat these as our latent variables and learn a mapping between them and sequences of body poses. In this manner, by simply tracking the feet or the hands, we can reliably guess initial poses over whole sequences and, then, refine them.
An instance selection approach to Multiple instance Learning
Authors: Zhouyu Fu, Antonio Robles-Kelly
Venue: CVPR
Year: 2009

Multiple-instance learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from which features are extracted. In MIL, we have usually confronted with a large instance space for even moderately sized data sets since each bag may contain many instances. Hence it is important to design efficient instance pruning and selection techniques to speed up the learning process without compromising on the performance. In this paper, we address the issue of instance selection in multiple instance learning and propose the IS-MIL, an instance selection framework for MIL, to tackle large-scale MIL problems. IS-MIL is based on an alternative optimisation framework by iteratively repeating the steps of instance selection/updating and classifier learning, which is guaranteed to converge. Experimental results demonstrate the utility and efficiency of the proposed approach compared to the alternatives.
Fourier analysis and Gabor filtering for texture analysis and local reconstruction of general shapes
Authors: Fabio Galasso, Joan Lasenby
Venue: CVPR
Year: 2009

Since the pioneering work of Gibson in 1950, Shape-From-Texture has been considered by researchers as a hard problem, mainly due to restrictive assumptions which often limit its applicability. We assume a very general stochastic homogeneity and perspective camera model, for both deterministic and stochastic textures. A multi-scale distortion is efficiently estimated with a previously presented method based on Fourier analysis and Gabor filters. The novel 3D reconstruction method that we propose applies to general shapes, and includes non-developable and extensive surfaces. Our algorithm is accurate, robust and compares favorably to the present state of the art of Shape-From-Texture. Results show its application to non-invasively study shape changes with laid-on textures, while rendering and re-texturing of cloth is suggested for future work.
Class-specific Hough forests for object detection
Authors: Juergen Gall, Victor S. Lempitsky
Venue: CVPR
Year: 2009

We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas the previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets.
Adaptive Contour Features in oriented granular space for human detection and segmentation
Authors: Wei Gao, Haizhou Ai, Shihong Lao
Venue: CVPR
Year: 2009

In this paper, a novel feature named adaptive contour feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in oriented granular space (OGS) that is learnt via the AdaBoost algorithm. Three operations are defined on the OGS to mine object contour feature and feature co-occurrences automatically. A heuristic learning algorithm is proposed to generate an ACF that at the same time define a weak classifier for human detection or segmentation. Experiments on two open datasets show that the ACF outperform several well-known existing features due to its stronger discriminative power rooted in the nature of its flexibility and adaptability to describe an object contour element.
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers
Authors: Peter V. Gehler, Sebastian Nowozin
Venue: CVPR
Year: 2009

Most modern computer vision systems for high-level tasks, such as image classification, object recognition and segmentation, are based on learning algorithms that are able to separate discriminative information from noise. In practice, however, the typical system consists of a long pipeline of pre-processing steps, such as extraction of different kinds of features, various kinds of normalizations, feature selection, and quantization into aggregated representations such as histograms. Along this pipeline, there are many parameters to set and choices to make, and their effect on the overall system performance is a-priori unclear. In this work, we shorten the pipeline in a principled way. We move pre-processing steps into the learning system by means of kernel parameters, letting the learning algorithm decide upon suitable parameter values. Learning to optimize the pre-processing choices becomes learning the kernel parameters. We realize this paradigm by extending the recent Multiple Kernel Learning formulation from the finite case of having a fixed number of kernels which can be combined to the general infinite case where each possible parameter setting induces an associated kernel. We evaluate the new paradigm extensively on image classification and object classification tasks. We show that it is possible to learn optimal discriminative codebooks and optimal spatial pyramid schemes, consistently outperforming all previous state-of-the-art approaches.
Ensemble manifold regularization
Authors: Bo Geng, Chao Xu 0006, Dacheng Tao, Linjun Yang, Xian-Sheng Hua 0001
Venue: CVPR
Year: 2009

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.
Physics-based edge evaluation for improved color constancy
Authors: Arjan Gijsenij, Theo Gevers, Joost van de Weijer 0001
Venue: CVPR
Year: 2009

Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation.
A nonparametric Riemannian framework for processing high angular resolution diffusion images (HARDI)
Authors: Alvina Goh, Christophe Lenglet, Paul M. Thompson, René Vidal
Venue: CVPR
Year: 2009

High angular resolution diffusion imaging has become an important magnetic resonance technique for in vivo imaging. Most current research in this field focuses on developing methods for computing the orientation distribution function (ODF), which is the probability distribution function of water molecule diffusion along any angle on the sphere. In this paper, we present a Riemannian framework to carry out computations on an ODF field. The proposed framework does not require that the ODFs be represented by any fixed parameterization, such as a mixture of von Mises-Fisher distributions or a spherical harmonic expansion. Instead, we use a non-parametric representation of the ODF, and exploit the fact that under the square-root re-parameterization, the space of ODFs forms a Riemannian manifold, namely the unit Hilbert sphere. Specifically, we use Riemannian operations to perform various geometric data processing algorithms, such as interpolation, convolution and linear and nonlinear filtering. We illustrate these concepts with numerical experiments on synthetic and real datasets.
Random walks on graphs to model saliency in images
Authors: Viswanath Gopalakrishnan, Yiqun Hu, Deepu Rajan
Venue: CVPR
Year: 2009

We formulate the problem of salient region detection in images as Markov random walks performed on images represented as graphs. While the global properties of the image are extracted from the random walk on a complete graph, the local properties are extracted from a k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a compact object. The equilibrium hitting times of the ergodic Markov chain holds the key for identifying the most salient node. The background nodes which are farthest from the most salient node are also identified based on the hitting times calculated from the random walk. Finally, a seeded salient region identification mechanism is developed to identify the salient parts of the image. The robustness of the proposed algorithm is objectively demonstrated with experiments carried out on a large image database annotated with “ground-truth” salient regions.
Disambiguating the recognition of 3D objects
Authors: Gutemberg Guerra-Filho
Venue: CVPR
Year: 2009

We propose novel algorithms for the detection, segmentation, recognition, and pose estimation of three-dimensional objects. Our approach initially infers geometric primitives to describe the set of 3D objects. A hierarchical structure is constructed to organize the objects in terms of shared primitives and relations between different primitives in the same object. This structure is shown to disambiguate the object models and to improve recognition rates. The primitives are obtained through our new Invariant Hough Transform. This algorithm uses geometric invariants to compute relations for subsets of points in a specific object. Each relation is stored in a hash table according to the invariant value. The hash table is used to find potential corresponding points between objects. With point matches, pose estimation is achieved by building a probability distribution of transformations. We evaluate our methods with experiments using synthetic and real 3D objects.