OpenRaiser/Intern-Atlas huggingface dataset:
Locally Linear Models on Face Appearance Manifolds with Application to Dual-Subspace Based Classification
Authors: Wei Fan, Dit-Yan Yeung
Venue: CVPR
Year: 2006

Recently, there has been a flurry of research on face recognition based on multiple images or shots from either a video sequence or an image set. This paper is also such an attempt in multiple-shot face recognition. Specifically, we propose a novel nonparametric method that first extracts discriminating local models via clustering. We apply a hierarchical distance-based clustering procedure according to some distance measure on the appearance manifold to cluster similar face images together. Based on the local models extracted, we then construct the intrapersonal and extrapersonal subspaces. Given a new test image, the angle between the projections of the image onto the two subspaces is used as a distance measure for classification. Since a test example contains multiple face images in multiple-shot face recognition, the final classification combines the classification decisions of all individual test images via a majority voting scheme. We compare our method empirically with some previous methods based on a database of video sequences of human faces, showing that out method significantly outperforms other methods.
Reconstruction with Interval Constraints Propagation
Authors: Michela Farenzena, Andrea Fusiello, Agostino Dovier
Venue: CVPR
Year: 2006

In this paper we demonstrate how Interval Analysis and Constraint Logic Programming can be used to obtain an accurate geometric model of a scene that rigorously takes into account the propagation of data errors and roundoff. Image points are represented as small rectangles: As a result, the output of the n-views triangulation is not a single point in space, but a polyhedron that contains all the possible solutions. Interval Analysis is used to bound this polyhedron with a box. Geometrical constraints such as orthogonality, parallelism, and coplanarity are subsequently enforced in order to reduce the size of those boxes, using Constraint Logic Programming. Experiments with real calibrated images illustrate the approach.
Hierarchical Statistical Learning of Generic Parts of Object Structure
Authors: Sanja Fidler, Gregor Berginc, Ales Leonardis
Venue: CVPR
Year: 2006

With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of object classes. In pursuit of a more general categorization system this paper proposes a way to overcome the computational complexity encompassing the enormous number of different object categories by exploiting the statistical properties of the highly structured visual world. Our approach proposes a hierarchical acquisition of generic parts of object structure, varying from simple to more complex ones, which stem from the favorable statistics of natural images. The parts recovered in the individual layers of the hierarchy can be used in a top-down manner resulting in a robust statistical engine that could be efficiently used within many of the current categorization systems. The proposed approach has been applied to large image datasets yielding important statistical insights into the generic parts of object structure.
A Mean Field EM-algorithm for Coherent Occlusion Handling in MAP-Estimation Prob
Authors: Rik Fransens, Christoph Strecha, Luc Van Gool
Venue: CVPR
Year: 2006

This paper presents a generative model based approach to deal with occlusions in vision problems which can be formulated as MAP-estimation problems. The approach is generic and targets applications in diverse domains like model-based object recognition, depth-from-stereo and image registration. It relies on a probabilistic imaging model, in which visible regions and occlusions are generated by two separate processes. The partitioning into visible and occluded regions is made explicit by the introduction of an hidden binary visibility map, which, to account for the coherent nature of occlusions, is modelled as a Markov Random Field. Inference is made tractable by a mean field EMalgorithm, which alternates between estimation of visibility and optimisation of model parameters. We demonstrate the effectiveness of the approach with two examples. First, in a N-view stereo experiment, we compute a dense depth map of a scene which is contaminated by multiple occluding objects. Finally, in a 2D-face recognition experiment, we try to identify people from partially occluded facial images.
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
Authors: Feng Ge, Song Wang 0002, Tiecheng Liu
Venue: CVPR
Year: 2006

Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extended to other applications. We present in this paper a new benchmark for evaluating image segmentation. Specifically, we formulate image segmentation as identifying the single most perceptually salient structure from an image. We collect a large variety of test images that conforms to this specific formulation, construct unambiguous ground truth for each image, and define a reliable way to measure the segmentation accuracy. We then present two special strategies to further address two important issues: (a) the most salient structures in some real images may not be unique or unambiguously defined, and (b) many available image-segmentation methods are not developed to directly extract a single salient structure. Finally, we apply this benchmark to evaluate and compare the performance of several state-of-the-art image-segmentation methods, including the normalized-cut method, the level-set method, the efficient graph-based method, the mean-shift method, and the ratio-contour method.
Scale Variant Image Pyramids
Authors: Joshua Gluckman
Venue: CVPR
Year: 2006

Multi-scale representations are motivated by the scale invariant properties of natural images. While many low level statistical measures, such as the local mean and variance of intensity, behave in a scale invariant manner, there are many higher order deviations from scale invariance where zero-crossings merge and disappear. Such scale variant behavior is important information to represent because it is not easily predicted from lower resolution data. A scale variant image pyramid is a representation that separates this information from the more redundant and predictable scale invariant information.
A New Deformable Model for Boundary Tracking in Cardiac MRI and Its Application to the Detection of Intra-Ventricular Dyssynchrony
Authors: Paulo F. U. Gotardo, Kim L. Boyer, Joel H. Saltz, Subha V. Raman
Venue: CVPR
Year: 2006

We present a new deformable model technique following a snake-like approach and using a complex Fourier shape descriptors parameterization to efficiently formulate the forces that constrain contour deformation. The method was successfully applied to track the left ventricle’s (LV) endocardial and epicardial boundaries in sequences of shortaxis magnetic resonance images depicting complete cardiac cycles. The extracted shapes show the method’s robustness to weak contrast, noisy edge maps and to papillary muscle anatomy. Our second contribution is a statistical pattern recognition approach for the detection of asynchronous activation of the LV walls. We applied our deformable model method to provide spatio-temporal characterizations of complete cardiac cycles and then designed a linear classifier using the popular combination of Principal Component Analysis and Linear Discriminant Analysis. From a database comprising 33 patients, our approach provided a correct classification performance of 90.9% showing its potential in providing improved dyssynchrony characterization as an adjunct to current criteria for selecting patients for therapy, which provided a classification accuracy of just 62.5% on the same database.
On-line Boosting and Vision
Authors: Helmut Grabner, Horst Bischof
Venue: CVPR
Year: 2006

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.
Are two rotational flows sufficient to calibrate a smooth non-parametric sensor?
Authors: Etienne Grossmann, Eun-Joo Lee, Peter Hislop, David Nistér, Henrik Stewénius
Venue: CVPR
Year: 2006

We present an attempt to determine whether the shape of a generic central-projection camera, such as the eye of an insect or a log-polar camera, can be determined from two motion flows resulting from purely rotational motions with non-collinear axes. Our first contribution is to write the smooth non-parametric calibration problem as a differential equation. It is unclear at present whether this problem has unique solution, up to an orthogonal transformation. Our second contribution is a discretized version of this smooth problem, for which we give a calibration algorithm - a third contribution. Using this algorithm, we explore numerically the properties of the discrete self-calibration problem, giving some insight on the nature of the problem. We show examples of successful self-calibration, but cannot give a definite affirmative answer to the question in the title.
3D Alignment of Face in a Single Image
Authors: Lie Gu, Takeo Kanade
Venue: CVPR
Year: 2006

We present an approach for aligning a 3D deformable model to a single face image. The model consists of a set of sparse 3D points and the view-based patches associated with every point. Assuming a weak perspective projection model, our algorithm iteratively deforms the model and ad- justs the 3D pose to fit the image. As opposed to previous approaches, our algorithm starts the fitting without resort- ing to manual labeling of key facial points. And it makes no assumptions about global illumination or surface prop- erties, so it can be applied to a wide range of imaging con- ditions. Experiments demonstrate that our approach can effectively handle unseen faces with a variety of pose and illumination variations.
Euclidean Structure from Confocal Conics: Theory and Application to Camera Calibration
Authors: Pierre Gurdjos, Jun-Sik Kim 0001, In-So Kweon
Venue: CVPR
Year: 2006

Plane-based calibration is now a very popular procedure because of its flexibility. One key step consists in detecting a set of coplanar features, from which the Euclidean structure of the corresponding 3D plane has to be computed. We suggest to use confocal conics as calibration targets, as they offer undeniable advantages over other ones (e.g., points or lines) in terms of detection and estimation, especially in the presence of partial occlusion. We introduce important projective and Euclidean properties of the linear family of conics (i.e., the confocal conic range), spanned by two confocal conics. In particular, we rely on the fact that the circular point-envelope - a rank-2 conic that encodes the 2D Euclidean structure - is a degenerate member of any confocal conic range. This allows us to give closed-form solutions in three cases: one conic with known foci, two confocal conics with known product of ratios of semi axes, and two unknown confocal conics. The performances of the proposed algorithms (consisting of a few lines of Matlab-like code) show up high accuracies for both intrinsic and extrinsic camera parameters. In addition to experiments with synthetic data, a video sequence is processed, showing off the interest of using confocal conics as calibration targets, for augmented reality purposes.
Kernel-based Template Alignment
Authors: Igor Guskov
Venue: CVPR
Year: 2006

This paper introduces a novel kernel-based method for template tracking in video sequences. The method is derived for a general warping transformation, and its application to affine motion tracking is further explored. Our approach is based on maximization of the multi-kernel Bhattacharyya coefficient with respect to the warp parameters. We explicitly compute the gradient of the similarity functional, and use a quasi-Newton procedure for optimization. Additionally, we consider a simple extension of the method that employs an illumination model correction to allow tracking under varying lighting conditions. The resulting tracking procedure is evaluated on a number of examples including large templates tracking non-rigidly moving textured areas.
Learning a manifold-constrained map between image sets: applications to matching and pose estimation
Authors: Jihun Ham, Ikkjin Ahn, Daniel D. Lee
Venue: CVPR
Year: 2006

This paper proposes a method for matching two sets of images given a small number of training examples by exploiting the underlying structure of the image manifolds. A nonlinear map from one manifold to another is constructed by combining linear maps locally defined on the tangent spaces of the manifolds. This construction imposes strong constraints on the choice of the maps, and makes possible good generalization of correspondences between all of the image sets. This map is flexible enough to approximate an arbitrary diffeomorphism between manifolds and can serve many purposes for applications. The underlying algorithm is a non-iterative efficient procedure whose complexity mainly depends on the number of matched training examples and the dimensionality of the manifold, and not on the number of samples nor on the dimensionality of the images. Several experiments were performed to demonstrate the potential of our method in image analysis and pose estimation. The first example demonstrates how images from a rotating camera can be mapped to the underlying pose manifold. Second, computer generated images from articulating toy figures are matched using the underlying 4 dimensional manifold to generate image-driven animations. Finally, two sets of actual lip images during speech are matched by their appearance manifold. In all these cases, our algorithm is able to obtain reasonable matches between thousands of large-dimensional images, with a minimum of computation.
Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking
Authors: Tony X. Han, Huazhong Ning, Thomas S. Huang
Venue: CVPR
Year: 2006

An efficient Nonparametric Belief Propagation (NBP) algorithm is developed in this paper. While the recently proposed nonparametric belief propagation algorithm has wide applications such as articulated tracking [22, 19], superresolution [6], stereo vision and sensor calibration [10], the hardcore of the algorithm requires repeatedly sampling from products of mixture of Gaussians, which makes the algorithm computationally very expensive. To avoid the slow sampling process, we applied mixture Gaussian density approximation by mode propagation and kernel fitting [2, 7]. The products of mixture of Gaussians are approximated accurately by just a few mode propagation and kernel fitting steps, while the sampling method (e.g. Gibbs sampler) needs many samples to achieve similar approximation results. The proposed algorithm is then applied to articulated body tracking for several scenarios. The experimental results show the robustness and the efficiency of the proposed algorithm. The proposed efficient NBP algorithm also has potentials in other applications mentioned above.
Accurate Tracking of Monotonically Advancing Fronts
Authors: M. Sabry Hassouna, Aly A. Farag
Venue: CVPR
Year: 2006

A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.
A Detection Technique for Degraded Face Images
Authors: Shinji Hayashi, Osamu Hasegawa
Venue: CVPR
Year: 2006

This paper describes a face detection technique that enables detection of extremely small faces such as 6 × 6 pixel. This is the first approach to detect very low-resolution faces in the field of face detection. First, we use a conventional AdaBoost-based face detector to show that the face detection rate falls to 39% from 88% as face resolution decreases from 24 × 24 pixels to 6 × 6 pixels. Second, we propose a new face detection method comprising four techniques. It improves the face detection rate from 39% to 73% for 6 × 6 pixel faces of the MIT+CMU frontal face test set. Finally, we applied our method to real world data. By merging results of some frames and judging using a threshold, we show that our method is effective for processing real world data.
Putting Objects in Perspective
Authors: Derek Hoiem, Alexei A. Efros, Martial Hebert
Venue: CVPR
Year: 2006

Image understanding requires not only individually estimating elements of the visual world but also capturing the interplay among them. In this paper, we provide a framework for placing local object detection in the context of the overall 3D scene by modeling the interdependence of objects, surface orientations, and camera viewpoint. Most object detection methods consider all scales and locations in the image as equally likely. We show that with probabilistic estimates of 3D geometry, both in terms of surfaces and world coordinates, we can put objects into perspective and model the scale and location variance in the image. Our approach reflects the cyclical nature of the problem by allowing probabilistic object hypotheses to refine geometry and vice-versa. Our framework allows painless substitution of almost any object detector and is easily extended to include other aspects of image understanding. Our results confirm the benefits of our integrated approach.
Hierarchical Volumetric Multi-view Stereo Reconstruction of Manifold Surfaces based on Dual Graph Embedding
Authors: Alexander Hornung, Leif Kobbelt
Venue: CVPR
Year: 2006

This paper presents a new volumetric stereo algorithm to reconstruct the 3D shape of an arbitrary object. Our method is based on finding the minimum cut in an octahedral graph structure embedded into the volumetric grid, which establishes a well defined relationship between the integrated photo-consistency function of a region in space and the corresponding edge weights of the embedded graph. This new graph structure allows for a highly efficient hierarchical implementation supporting high volumetric resolutions and large numbers of input images. Furthermore we will show how the resulting cut surface can be directly converted into a consistent, closed and manifold mesh. Hence this work provides a complete multi-view stereo reconstruction pipeline. We demonstrate the robustness and efficiency of our technique by a number of high quality reconstructions of real objects.
Contour-Based Structure from Reflection
Authors: Po-Hao Huang, Shang-Hong Lai
Venue: CVPR
Year: 2006

In this paper, we propose a novel contour-based algorithm for 3D object reconstruction from a single uncalibrated image acquired under the setting of two plane mirrors. With the epipolar geometry recovered from the image and the properties of mirror reflection, metric reconstruction of an arbitrary rigid object is accomplished without knowing the camera parameters and the mirror poses. For this mirror setup, the epipoles can be estimated from the correspondences between the object and its reflection, which can be established automatically from the tangent lines of their contours. By using the property of mirror reflection as well as the relationship between the mirror plane normal with the epipole and camera intrinsic, we can estimate the camera intrinsic, plane normals and the orientation of virtual cameras. The positions of the virtual cameras are determined by minimizing the distance between the object contours and the projected visual cone for a reference view. After the camera parameters are determined, the 3D object model is constructed via the image-based visual hulls (IBVH) technique. The 3D model can be refined by integrating the multiple models reconstructed from different views. The main advantage of the proposed contour-based Structure from Reflection (SfR) algorithm is that it can achieve metric reconstruction from an uncalibrated image without feature point correspondences. Experimental results on synthetic and real images are presented to show its performance.
Multiple Face Model of Hybrid Fourier Feature for Large Face Image Set
Authors: Wonjun Hwang, Gyu-tae Park, Jongha Lee, Seok-Cheol Kee
Venue: CVPR
Year: 2006

The face recognition system based on the only single classifier considering the restricted information can not guarantee the generality and superiority of performances in a real situation. To challenge such problems, we propose the hybrid Fourier features extracted from different frequency bands and multiple face models. The hybrid Fourier feature comprises three different Fourier domains; merged real and imaginary components, Fourier spectrum and phase angle. When deriving Fourier features from three Fourier domains, we define three different frequency bandwidths, so that additional complementary features can be obtained. After this, they are individually classified by Linear Discriminant Analysis. This approach makes possible analyzing a face image from the various viewpoints to recognize identities. Moreover, we propose multiple face models based on different eye positions with a same image size, and it contributes to increasing the performance of the proposed system. We evaluated this proposed system using the Face Recognition Grand Challenge (FRGC) experimental protocols known as the largest data sets available. Experimental results on FRGC version 2.0 data sets has proven that the proposed method shows better verification rates than the baseline of FRGC on 2D frontal face images under various situations such as illumination changes, expression changes, and time elapses.