OpenRaiser/Intern-Atlas huggingface dataset:
Selection and Fusion of Color Models for Feature Detection
Authors: Harro M. G. Stokman, Theo Gevers
Venue: CVPR
Year: 2005

The choice of a color space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. However, the problem is how to automatically select the color space that produces the best result for a particular task. The subsequent difficulty then is how to obtain a proper weighting scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color space selection and fusion of feature detectors, in this paper, we propose a method that exploits non-perfect correlation between the color models derived from the principles of diversification. As a consequence, the weighting scheme yields maximal color discrimination. The method is verified experimentally for two different feature detectors. The experimental results show that the model provides feature detection results having a discriminative power of 30 percent higher than the standard weighting scheme.
Imaging the Cardiovascular Pulse
Authors: Nanfei Sun, Marc Garbey, Arcangelo Merla, Ioannis T. Pavlidis
Venue: CVPR
Year: 2005

We have developed a novel method to measure human cardiac pulse at a distance. It is based on the information contained in the thermal signal emitted from major superficial vessels. This signal is acquired through a highly sensitive thermal imaging system. Temperature on the vessel is modulated by pulsative blood flow. To compute the frequency of modulation (pulse), we extract a line-based region along the vessel. Then, we apply Fast Fourier Transform (FFT) to individual points along this line of interest to capitalize on the pulse propagation effect. Finally, we use an adaptive estimation function on the average FFT outcome to quantify the pulse. We have tested the accuracy of our method on 5 subjects with highly successful results. The technology is expected to find applications among others in sustained physiological monitoring of cardiopulmonary diseases, sport training, sleep studies, and psychophysiology (polygraph).
Symmetric Stereo Matching for Occlusion Handling
Authors: Jian Sun 0001, Yin Li 0003, Sing Bing Kang
Venue: CVPR
Year: 2005

In this paper, we propose a symmetric stereo model to handle occlusion in dense two-frame stereo. Our occlusion reasoning is directly based on the visibility constraint that is more general than both ordering and uniqueness constraints used in previous work. The visibility constraint requires occlusion in one image and disparity in the other to be consistent. We embed the visibility constraint within an energy minimization framework, resulting in a symmetric stereo model that treats left and right images equally. An iterative optimization algorithm is used to approximate the minimum of the energy using belief propagation. Our stereo model can also incorporate segmentation as a soft constraint. Experimental results on the Middlebury stereo images show that our algorithm is state-of-the-art.
Classification of Contour Shapes Using Class Segment Sets
Authors: Kang B. Sun, Boaz J. Super
Venue: CVPR
Year: 2005

Both example-based and model-based approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. This paper proposes a part-based approach to address this problem. Bayesian classification is performed within a three-level framework, which consists of models for contour segments, for classes, and for the entire database of training examples. The class model enables different parts of different exemplars of a class to contribute to the recognition of an input shape. The method is robust to occlusion and is invariant to planar rotation, translation, and scaling. Furthermore, the method is completely automated. It achieves 98% classification accuracy on a large database with many classes.
Ordinal Palmprint Represention for Personal Identification
Authors: Zhenan Sun, Tieniu Tan, Yunhong Wang 0001, Stan Z. Li
Venue: CVPR
Year: 2005

Palmprint-based personal identification, as a new member in the biometrics family, has become an active research topic in recent years. Although great progress has been made, how to represent palmprint for effective classification is still an open problem. In this paper, we present a novel palmprint representation - ordinal measure, which unifies several major existing palmprint algorithms into a general framework. In this framework, a novel palmprint representation method, namely orthogonal line ordinal features, is proposed. The basic idea of this method is to qualitatively compare two elongated, line-like image regions, which are orthogonal in orientation and generate one bit feature code. A palmprint pattern is represented by thousands of ordinal feature codes. In contrast to the state-of-the-art algorithm reported in the literature, our method achieves higher accuracy, with the equal error rate reduced by 42% for a difficult set, while the complexity of feature extraction is halved.
Reflection Components Decomposition of Textured Surfaces Using Linear Basis Functions
Authors: Robby T. Tan, Katsushi Ikeuchi
Venue: CVPR
Year: 2005

Most existing methods of reflection components decomposition using a single color image require color segmentation. Few methods that employ local operations are able to avoid the requirement; however, they usually suffer from color discontinuity problems. In this paper, we introduce a decomposition method using a single color image that does not require (global) color segmentation or (local) color discontinuity detection. The method principally utilizes the coefficients of the reflectance basis functions of input image and its specular-free image. Combining those coefficients enables us to find the diffuse coefficients of the specular pixels for every surface color. As a result, the decomposition becomes a well-posed problem and able to be solved in closed-form equations. Our experimental results on real complex textured images show the effectiveness of our proposed method.
Theoretical Analysis on Reconstruction-Based Super-Resolution for an Arbitrary PSF
Authors: Masayuki Tanaka 0001, Masatoshi Okutomi
Venue: CVPR
Year: 2005

This study presents and proves a condition number theorem for super-resolution (SR). The SR condition number theorem provides the condition number for an arbitrary space-invariant point spread function (PSF) when using an infinite number of low resolution images. A gradient restriction is also derived for maximum likelihood (ML) method. The gradient restriction is presented as an inequality which shows that the power spectrum of the PSF suppresses the spatial frequency component of the gradient of ML cost function. A Box PSF and a Gaussian PSF are analyzed with the SR condition number theorem. Effects of the gradient restriction on super-resolution results are shown using synthetic images.
Dense Photometric Stereo Using Tensorial Belief Propagation
Authors: Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong
Venue: CVPR
Year: 2005

We address the normal reconstruction problem by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint. Our method is robust to spurious noises caused by highlight and shadows and non-Lambertian reflections. To simultaneously recover normal orientations and preserve discontinuities, we model the dense photometric stereo problem into two coupled Markov random fields (MRFs): a smooth field for normal orientations, and a spatial line process for normal orientation discontinuities. We propose a very fast tensorial belief propagation method to approximate the maximum a posteriori (MAP) solution of the Markov network. Our tensor-based message passing scheme not only improves the normal orientation estimation from one of discrete to continuous, but also reduces storage and running time drastically. A convenient handheld device was built to collect a scattered set of photometric samples, from which a dense and uniform set on the lighting direction sphere is obtained. We present very encouraging results on a wide range of difficult objects to show the efficacy of our approach.
A Markov Random Field Approach for Dense Photometric Stereo
Authors: Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong
Venue: CVPR
Year: 2005

We present a surprisingly simple system that allows for robust normal reconstruction by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint, in the presense of spurious noises caused by highlight, shadows and non-Lambertian reflections. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, a dense set of unbiased but noisy photometric data that roughly distributed uniformly on the light direction sphere is produced. To simultaneously recover normal orientations and preserve discontinuities, we model the dense photometric stereo problem into two coupled Markov random fields (MRFs): a smooth field for normal orientations, and a spatial line process for normal orientation discontinuities. A very fast tensorial belief propagation method is used to approximate the maximum a posteriori (MAP) solution of the Markov network. We present very encouraging results on a wide range of difficult objects to show the efficacy of our approach.
Combining Object and Feature Dynamics in Probabilistic Tracking
Authors: Leonid Taycher, John W. Fisher III, Trevor Darrell
Venue: CVPR
Year: 2005

Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as input to a system for tracking the entire object using a global dynamic model. Approximate local dynamics may be brittle - point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary - but constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating global dynamics knowledge into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values that are incorporated into an observation process of the feature extractor. We combine such models in a multichain graphical model framework. We show the utility of our framework for improving feature tracking and thus shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications, and demonstrate its application to background subtraction.
Region Competition via Local Watershed Operators
Authors: Hüseyin Tek, Ferit Akova, Alper Ayvaci
Venue: CVPR
Year: 2005

In this paper, we propose a segmentation algorithm which combines the ideas from local watershed transforms and the region based deformable models. Traditionally, watersheds are computed in the whole image and then some region merging techniques are applied on them to reach the segmentation of structures. We propose that watershed regions can be used as operators in region-based deformable models. These regions are computed only when the deformable models reach them. Then, they are added to (or subtracted from) the deformable models via a measure computed from two terms: (i) statistical fit of regions to the models, region competition; (ii) smoothness of such fits, smoothness constraint. The proposed algorithm is computationally efficient because it operates on regions instead of pixels. In addition, this algorithm allows better boundary localization due to the edge information brought by watersheds. Moreover, the proposed algorithm can handle topological changes, e.g., split or merge, during the evolutions without an additional embedded surface as in the case of level set formulation. Furthermore, structure-based smoothness of segmented objects is obtained by using the smoothness term computed from the alignment of regions. We illustrate the efficiency and accuracy of the proposed technique on several medical data such as MRA and CTA data.
The Radial Trifocal Tensor: A Tool for Calibrating the Radial Distortion of Wide-Angle Cameras
Authors: SriRam Thirthala, Marc Pollefeys
Venue: CVPR
Year: 2005

We present a technique to linearly estimate the radial distortion of a wide-angle lens given three views of a real-world plane. The approach can also be used with pure rotation as in this case all points appear as lying on a plane. The three views can even be recorded using three different cameras as long as the deviation from the pin-hole model for each camera is distortion along radial lines. We introduce the 1D radial camera which projects scene points onto radial lines and the radial trifocal tensor which encodes the multi-view relations between radial lines. Given at least seven triplets of corresponding points the radial trifocal tensor can be computed linearly. This allows recovery of the radial cameras and the projective reconstruction of the plane up to a two fold ambiguity. This 2D reconstruction is unaffected by radial distortion and can be used in different ways to compute the radial distortion parameters. We propose to use the division model as in this case we obtain a linear algorithm that computes the radial distortion coefficients and the 3 remaining degrees of freedom of the homography relating the reconstructed 2D plane to the undistorted image. Each feature point that has at least one corresponding point yields one linear constraint on those unknowns. Our method is validated on real-world images. We successfully calibrate several wide-angle cameras.
Calibration of Pan-Tilt-Zoom (PTZ) Cameras and Omni-Directional Cameras
Authors: SriRam Thirthala, Sudipta N. Sinha, Marc Pollefeys
Venue: CVPR
Year: 2005

In the first part we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera's lowest zoom setting very accurately by capturing an extended panorama. The camera intrinsics are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the cameras. Both steps are fully automatic and do not assume any knowledge of the scene structure. We validate our approach by calibrating two different types of pan tilt zoom cameras placed in an outdoor environment. We also show the high-resolution panoramic mosaics built from the images captured during this process. The second section deals with the calibration of omnidirectional cameras. A broad class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. We study the multi-view geometry of 1D radial cameras. For cameras in general configuration, we introduce a quadrifocal tensor. From this tensor a metric reconstruction of the 1D cameras as well as the observed features can be obtained. In a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. We study some degenerate cases, including pure rotation. In the case of a purely rotating camera we obtain a trifocal tensor. This allows us to obtain a metric reconstruction of the plane at infinity. Next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion.
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
Authors: Ying-li Tian, Max Lu, Arun Hampapur
Venue: CVPR
Year: 2005

We present a new method to robustly and efficiently analyze foreground when we detect background for a fixed camera view by using mixture of Gaussians models and multiple cues. The background is modeled by three Gaussian mixtures as in the work of Stauffer and Grimson (1999). Then the intensity and texture information are integrated to remove shadows and to enable the algorithm working for quick lighting changes. For foreground analysis, the same Gaussian mixture model is employed to detect the static foreground regions without using any tracking or motion information. Then the whole static regions are pushed back to the background model to avoid a common problem in background subtraction /spl times/ fragmentation (one object becomes multiple parts). The method was tested on our real time video surveillance system. It is robust and run about 130 fpsfor color images and 150 fps for grayscale images at size 160/spl times/120 on a 2GB Pentium IV machine with MMX optimization.
A Unified Optimization Based Learning Method for Image Retrieval
Authors: Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying Ma, Changshui Zhang, HongJiang Zhang
Venue: CVPR
Year: 2005

In this paper, an optimization based learning method is proposed for image retrieval from graph model point of view. Firstly, image retrieval is formulated as a regularized optimization problem, which simultaneously considers the constraints from low-level feature, online relevance feedback and offline semantic information. Then, the global optimal solution is developed in both closed form and iterative form, providing that the latter converges to the former. The proposed method is unified in the senses that 1) it makes use of the information from various aspects in a global optimization manner so that the retrieval performance might be maximally improved; 2) it provides a natural way to support two typical query scenarios in image retrieval. The proposed method has a solid mathematical ground. Systematic experimental results on a general-purpose image database demonstrate that it achieves significant improvements over existing methods.
Articulated Structure from Motion by Factorization
Authors: Philip A. Tresadern, Ian D. Reid 0001
Venue: CVPR
Year: 2005

Multibody affine structure from motion (SFM) methods commonly assume independent motion between objects such that the 'measurement matrix' has rank 4k. When multiple views are available, each object is then independently calibrated to a metric co-ordinate frame. However, articulated motion results in a further decrease in rank - a fact that we exploit to detect articulated objects and determine their degrees of freedom using simple linear methods. Furthermore, these objects cannot be recovered and calibrated independently since this violates articulation constraints. We show that articulation constraints can be imposed during factorization and self-calibration to recover consistent 3D structure and motion, from which link lengths and joint angles can be computed. The stability of the method is evaluated using synthetic data for comparison with ground truth and results are also presented for real image sequences.
Monocular 3D Tracking of the Golf Swing
Authors: Raquel Urtasun, David J. Fleet, Pascal Fua
Venue: CVPR
Year: 2005

This paper showcases our tracking algorithm described in (R. Urtasun et al., 2005). The proposed approach incorporates dynamic motion models into the human body tracking process yielding full 3D reconstruction from monocular sequences. The tracking is formulated in terms of minimizing a differentiable criterion whose differential structure is rich enough for successful optimization using a single hypothesis. In other words we avoid the computational complexity of multihypotheses algorithms while obtaining excellent results under challenging conditions.
A Weighted Nearest Mean Classifier for Sparse Subspaces
Authors: Cor J. Veenman, David M. J. Tax
Venue: CVPR
Year: 2005

In this paper we focus on high dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. First, in any subspace with as many dimensions as objects the data set can be separated with an almost arbitrary linear hyperplane. Second, another important issue is to determine which features are responsible for the phenomenon under consideration. This problem comes down to finding as few features as possible that still can discriminate the classes involved. To attack these problems, we propose the LESS (lowest error in a sparse subspace) classifier. The LESS classifier is a weighted nearest mean classifier that efficiently finds linear discriminants in sparse subspaces, where the subspace is found automatically. In the experiments we compare LESS to related state-of-the-art classifiers like among others linear ridge regression with the LASSO and the support vector machine. It turns out that LESS performs competitively while it uses the fewest features.
A Closed Form Solution to Direct Motion Segmentation
Authors: René Vidal, Dheeraj Singaraju
Venue: CVPR
Year: 2005

We present a closed form solution to the problem of segmenting multiple 2D motion models of the same type directly from the partial derivatives of an image sequence. We introduce the multibody brightness constancy constraint (MBCC), a polynomial equation relating motion models, image derivatives and pixel coordinates that is independent of the segmentation of the image measurements. We first show that the optical flow at a pixel can be obtained analytically as the derivative of the MBCC at the corresponding image measurement, without knowing the motion model associated with that pixel. We then show that the parameters of the multiple motion models can be obtained from the cross products of the derivatives of the MBCC at a set of image measurements that minimize a suitable distance function. Our approach requires no feature tracking, point correspondences or optical flow, and provides a global non-iterative solution that can be used to initialize more expensive iterative approaches to motion segmentation. Experiments on real and synthetic sequences are also presented.
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
Authors: Hongcheng Wang, Narendra Ahuja
Venue: CVPR
Year: 2005

We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in traditional dimensionality reduction techniques like PCA, and higher-dimensional data as a tensor. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. The challenges lie in the computational and memory requirements for large ensembles. Currently, there exists a rank-R approximation algorithm which, although applicable to any number of dimensions, is efficient for only low-rank approximations. For larger dimensionality reductions, the memory and time costs of this algorithm become prohibitive. We propose a novel algorithm, for rank-R approximations of third-order tensors, which is efficient for arbitrary R but for the important special case of 2D image ensembles, e.g. video. Both of these algorithms reduce redundancies present in all dimensions. Rank-R tensor approximation yields the most compact data representation among all known image-as-matrix methods. We evaluated the performance of our algorithm vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification.