OpenRaiser/Intern-Atlas huggingface dataset:
ANSIG - An analytic signature for permutation-invariant two-dimensional shape representation
Authors: José Jeronimo Rodrigues, Pedro M. Q. Aguiar, João M. F. Xavier
Venue: CVPR
Year: 2008

Many applications require a computer representation of 2D shape, usually described by a set of 2D points. The challenge of this representation is that it must not only capture the characteristics of the shape but also be invariant to relevant transformations. Invariance to geometric transformations, such as translation, rotation and scale, has received attention in the past, usually under the assumption that the points are previously labeled, i.e., that the shape is characterized by an ordered set of landmarks. However, in many practical scenarios the landmarks are obtained from an automatic process, e.g., edge/corner detection, thus without natural ordering. In this paper, we represent 2D shapes in a way that is invariant to the permutation of the landmarks. Within our framework, a shape is mapped to an analytic function on the complex plane, leading to what we call its analytic signature (ANSIG). We show that different shapes lead to different ANSIGs but that shapes that differ by a permutation of the landmarks lead to the same ANSIG, i.e., that our representation is a maximal invariant with respect to the permutation group. To store an ANSIG, it suffices to sample it along a closed contour in the complex plane. We further show how easy it is to factor out geometric transformations when comparing shapes using the ANSIG representation. We illustrate the ANSIG capabilities in shape-based image classification.
Context-dependent kernel design for object matching and recognition
Authors: Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven
Venue: CVPR
Year: 2008

The success of kernel methods including support vector networks (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as ldquocontext-dependentrdquo. Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criteria which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a ldquocontext-dependentrdquo kernel (ldquoCDKrdquo) which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with ldquocontext-freerdquo kernels.
Manifold learning using robust Graph Laplacian for interactive image search
Authors: Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert, Renaud Keriven
Venue: CVPR
Year: 2008

Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a very small fraction of an image database and iteratively refining a decision rule using both the labeled and unlabeled data. Training of this decision rule is referred to as transductive learning. Our work is an original approach for relevance feedback based on Graph Laplacian. We introduce a new Graph Laplacian which makes it possible to robustly learn the embedding, of the manifold enclosing the dataset, via a diffusion map. Our approach is three-folds: it allows us (i) to integrate all the unlabeled images in the decision process (ii) to robustly capture the topology of the image set and (iii) to perform the search process inside the manifold. Relevance feedback experiments were conducted on simple databases including Olivetti and Swedish as well as challenging and large scale databases including Corel. Comparisons show clear and consistent gain, of our graph Laplacian method, with respect to state-of-the art relevance feedback approaches.
Particle filtering for registration of 2D and 3D point sets with stochastic dynamics
Authors: Romeil Sandhu, Samuel Dambreville, Allen R. Tannenbaum
Venue: CVPR
Year: 2008

In this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in pose parameters obtained from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. Thus, the novelty of our method is twofold: Firstly, we employ a particle filtering scheme to drive the point set registration process. Secondly, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. 1.
Unsupervised estimation of segmentation quality using nonnegative factorization
Authors: Roman Sandler, Michael Lindenbaum
Venue: CVPR
Year: 2008

We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the measure they provide is not explicitly related to segmentation errors. The proposed approach differs from these methods on several important points and has several advantages over them. First, it provides a meaningful, quantitative assessment of segmentation quality, in precision/recall terms, which were applicable so far only for supervised evaluation. Second, it builds on a new image model, which characterizes the segments as a mixture of basic feature distributions. The precision/recall estimates are then obtained by a nonnegative matrix factorization (NMF) process. A third important advantage is that the estimates, which are based on intrinsic properties of the specific image being evaluated and not on a comparison to typical images (learning), are relatively robust to context factors such as image quality or the presence of texture. Experimental results demonstrate the accuracy of the precision/recall estimates in comparison to ground truth based on human judgment. Moreover, it is shown that tuning a segmentation algorithm using the unsupervised measure improves the algorithm's quality (as measured by a supervised method).
High resolution motion layer decomposition using dual-space graph cuts
Authors: Thomas Schoenemann, Daniel Cremers
Venue: CVPR
Year: 2008

We introduce a novel energy minimization method to decompose a video into a set of super-resolved moving layers. The proposed energy corresponds to the cost of coding the sequence. It consists of a data term and two terms imposing regularity of the geometry and the intensity of each layer. In contrast to existing motion layer methods, we perform graph cut optimization in the (dual) layer space to determine which layer is visible at which video position. In particular, we show how arising higher-order terms can be accounted for by a generalization of alpha expansions. Moreover, our model accurately captures long-term temporal consistency. To the best of our knowledge, this is the first work which aims at modeling details of the image formation process (such as camera blur and downsampling) in the context of motion layer decomposition. The experimental results demonstrate that energy minimization leads to a reconstruction of a video in terms of a superposition of multiple high-resolution motion layers.
Globally optimal shape-based tracking in real-time
Authors: Thomas Schoenemann, Daniel Cremers
Venue: CVPR
Year: 2008

Most algorithms for real-time tracking of deformable shapes provide sub-optimal solutions for a suitable energy minimization task: The search space is typically considered too large to allow for globally optimal solutions. In this paper we show that - under reasonable constraints on the object motion - one can guarantee global optimality while maintaining real-time requirements. The problem is cast as finding the optimal cycle in a graph spanned by the prior template and the image. The underlying combinatorial algorithm is implemented on state-of- the-art graphics hardware. Solutions on FPGAs are conceivable. Experimental results demonstrate long-term tracking of cars in real-time, while coping with challenging weather conditions. In particular, we show that the proposed tracking algorithm is highly robust to illumination changes and that it outperforms local tracking methods such as the level set method.
Matching non-rigidly deformable shapes across images: A globally optimal solution
Authors: Thomas Schoenemann, Daniel Cremers
Venue: CVPR
Year: 2008

While global methods for matching shapes to images have recently been proposed, so far research has focused on small deformations of a fixed template. In this paper we present the first global method able to pixel-accurately match non-rigidly deformable shapes across images at amenable run-times. By finding cycles of optimal ratio in a four-dimensional graph - spanned by the image, the prior shape and a set of rotation angles - we simultaneously compute a segmentation of the image plane, a matching of points on the template to points on the segmenting boundary, and a decomposition of the template into a set of deformable parts. In particular, the interpretation of the shape template as a collection of an a priori unknown number of deformable parts - an important aspect of higher-level shape representations - emerges as a byproduct of our matching algorithm. On real-world data of running people and walking animals, we demonstrate that the proposed method can match strongly deformed shapes, even in cases where simple shape measures and optic flow methods fail.
Edge preserving spatially varying mixtures for image segmentation
Authors: Giorgos Sfikas, Christophoros Nikou, Nikolas P. Galatsanos
Venue: CVPR
Year: 2008

A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Studentpsilas t-distributed. The generative properties of the Student's t-pdf allow this prior to impose smoothness and simultaneously model the edges between the segments of the image. A maximum a posteriori (MAP) expectation-maximization (EM) based algorithm is used for Bayesian inference. An important feature of this algorithm is that all the parameters are automatically estimated from the data in closed form. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation as compared to standard GMM-based approaches and to GMM segmentation techniques with ldquostandardrdquo spatial smoothness constraints.
A rank constrained continuous formulation of multi-frame multi-target tracking problem
Authors: Khurram Shafique, Mun Wai Lee, Niels Haering
Venue: CVPR
Year: 2008

This paper presents a multi-frame data association algorithm for tracking multiple targets in video sequences. Multi-frame data association involves finding the most probable correspondences between target tracks and measurements (collected over multiple time instances) as well as handling the common tracking problems such as, track initiations and terminations, occlusions, and noisy detections. The problem is known to be NP-Hard for more than two frames. A rank constrained continuous formulation of the problem is presented that can be efficiently solved using nonlinear optimization methods. It is shown that the global and local extrema of the continuous problem respectively coincide with the maximum and the maximal solutions of the discrete counterpart. A scanning window based tracking algorithm is developed using the formulation that performs well under noisy conditions with frequent occlusions and multiple track initiations and terminations. The above claims are supported by experiments and quantitative evaluations using both synthetic and real data under different operating conditions.
Visual tracking with histograms and articulating blocks
Authors: S. M. Nejhum Shahed, Jeffrey Ho, Ming-Hsuan Yang 0001
Venue: CVPR
Year: 2008

We propose an algorithm for accurate tracking of (articulated) objects using online update of appearance and shape. The challenge here is to model foreground appearance with histograms in a way that is both efficient and accurate. In this algorithm, the constantly changing foreground shape is modeled as a small number of rectangular blocks, whose positions within the tracking window are adaptively determined. Under the general assumption of stationary foreground appearance, we show that robust object tracking is possible by adaptively adjusting the locations of these blocks. Implemented in MATLAB without substantial optimization, our tracker runs already at 3.7 frames per second on a 3GHz machine. Experimental results have demonstrated that the algorithm is able to efficiently track articulated objects undergoing large variation in appearance and shape.
Unified Principal Component Analysis with generalized Covariance Matrix for face recognition
Authors: Shiguang Shan, Bo Cao, Yu Su 0010, Laiyun Qing, Xilin Chen 0001, Wen Gao 0001
Venue: CVPR
Year: 2008

Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these methods, and a novel framework called Unified Principal Component Analysis (UPCA) is proposed. First, we introduce a novel concept, named Generalized Covariance Matrix (GCM), which is naturally derived from the traditional Covariance Matrix (CM). Each element of GCM is a generalized covariance of two random vectors rather than two scalar variables in CM. Based on GCM, the UPCA framework is proposed, from which the traditional PCA and its 2D counterparts can be deduced as special cases. Furthermore, under the UPCA framework, we not only revisit the existing 2D PCA methods and their limitations, but also propose two new methods: the grid-sampling method (GridPCA) and the intra-group correlation reduction method. Extensive experimental results on the FERET face database support the theoretical analysis and validate the feasibility of the proposed methods.
Private Content Based Image Retrieval
Authors: Jagarlamudi Shashank, Palivela Kowshik, Kannan Srinathan, C. V. Jawahar
Venue: CVPR
Year: 2008

For content level access, very often database needs the query as a sample image. However, the image may contain private information and hence the user does not wish to reveal the image to the database. Private Content Based Image Retrieval (PCBIR) deals with retrieving similar images from an image database without revealing the content of the query image — not even to the database server. We propose algorithms for PCBIR, when the database is indexed using hierarchical index structure or hash based indexing scheme. Experiments are conducted on real datasets with popular features and state of the art data structures. It is observed that specialty and subjectivity of image retrieval (unlike SQL queries to a relational database) enables in computationally efficient yet private solutions.
View-invariant recognition of body pose from space-time templates
Authors: Yuping Shen, Hassan Foroosh
Venue: CVPR
Year: 2008

We propose a new template-based approach for view invariant recognition of body poses, based on geometric constraints derived from the motion of body point triplets. In addition to spatial information our templates encode temporal information of body pose transitions. Unlike existing methods that study a body pose as a whole, we decompose it into a number of body point triplets, and compare their motions to our templates. Using the fact that the homography induced by the motion of a triplet of body points in two identical body pose transitions reduces to the special case of a homology, we exploit the equality of two of its eigenvalues to impose constraints on the similarity of the pose transitions between two subjects, observed by different perspective cameras and from different viewpoints. Extensive experimental results show that our method can accurately identify human poses from video sequences when they are observed from totally different viewpoints with different camera parameters.
Video falsifying by motion interpolation and inpainting
Authors: Timothy K. Shih, Nick C. Tang, Joseph C. Tsai, Hsing-Ying Zhong
Venue: CVPR
Year: 2008

We change the behavior of actors in a video. For instance, the outcome of a 100-meter race in the Olympic game can be falsified. We track objects and segment motions using a modified mean shift mechanism. The resulting video layers can be played in different speeds and at different reference points with respect to the original video. In order to obtain a smooth movement of target objects, a motion interpolation mechanism is proposed based on continuous stick figures (i.e., a video of human skeleton) and video inpainting. The video inpainting mechanism is performed in a quasi-3D space via guided 3D patch matching for filling. Interpolated target objects and background layers are fused by using graph cut. It is hard to tell whether a falsified video is the original. We demonstrate the original and the falsified videos in our website at http://www.mine.tku.edu.tw/video_demo/). The proposed technique can be used to create special effects in movie industry.
Calibration and rectification for reflection stereo
Authors: Masao Shimizu, Masatoshi Okutomi
Venue: CVPR
Year: 2008

This paper presents a calibration and rectification method for single-camera range estimation using a single complex image with a transparent plate or a double-sided half-mirror plate, which are collectively called a reflection stereo. The range to an object is obtainable by finding the correspondence on a constraint line in the complex image, which consists of a surface and a rear-surface reflected image in the transparent plate, or also includes a transmitted and internal reflected image through a double-sided half-mirror plate. The range estimation requires extrinsic parameters of the reflection stereo that include the shape and position of the plate and its refraction index. The proposed method assumes that the plate is non-parallel but planar for a local region around the point of interest in the complex image. The method estimates the extrinsic parameters from a set of displacements in the complex images. Experiments using real images demonstrate the effectiveness of the proposed calibration and rectification method along with fine range estimation results.
Approximate earth mover's distance in linear time
Authors: Sameer Shirdhonkar, David W. Jacobs
Venue: CVPR
Year: 2008

The earth moverpsilas distance (EMD) is an important perceptually meaningful metric for comparing histograms, but it suffers from high (O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> logN)) computational complexity. We present a novel linear time algorithm for approximating the EMD for low dimensional histograms using the sum of absolute values of the weighted wavelet coefficients of the difference histogram. EMD computation is a special case of the Kantorovich-Rubinstein transshipment problem, and we exploit the Holder continuity constraint in its dual form to convert it into a simple optimization problem with an explicit solution in the wavelet domain. We prove that the resulting wavelet EMD metric is equivalent to EMD, i.e. the ratio of the two is bounded. We also provide estimates for the bounds. The weighted wavelet transform can be computed in time linear in the number of histogram bins, while the comparison is about as fast as for normal Euclidean distance or chi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> statistic. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation.
Subspace segmentation with outliers: A grassmannian approach to the maximum consensus subspace
Authors: Nuno Pinho da Silva, João Paulo Costeira
Venue: CVPR
Year: 2008

Segmenting arbitrary unions of linear subspaces is an important tool for computer vision tasks such as motion and image segmentation, SfM or object recognition. We segment subspaces by searching for the orthogonal complement of the subspace supported by the majority of the observations, i.e., the maximum consensus subspace. It is formulated as a Grassmannian optimization problem: a smooth, constrained but nonconvex program is immersed into the Grassmann manifold, resulting in a low dimensional and unconstrained program solved with an efficient optimization algorithm. Nonconvexity implies that global optimality depends on the initialization. However, by finding the maximum consensus subspace, outlier rejection becomes an inherent property of the method. Besides robustness, it does not rely on prior global detection procedures (e.g., rank of data matrices), which is the case of most current works. We test our algorithm in both synthetic and real data, where no outlier was ever classified as inlier.
Interactive image segmentation via minimization of quadratic energies on directed graphs
Authors: Dheeraj Singaraju, Leo J. Grady, René Vidal
Venue: CVPR
Year: 2008

We propose a scheme to introduce directionality in the random walker algorithm for image segmentation. In particular, we extend the optimization framework of this algorithm to combinatorial graphs with directed edges. Our scheme is interactive and requires the user to label a few pixels that are representative of a foreground object and of the background. These labeled pixels are used to learn intensity models for the object and the background, which allow us to automatically set the weights of the directed edges. These weights are chosen so that they bias the direction of the object boundary gradients to flow from regions that agree well with the learned object intensity model to regions that do not agree well. We use these weights to define an energy function that associates asymmetric quadratic penalties with the edges in the graph. We show that this energy function is convex, hence it has a unique minimizer. We propose a provably convergent iterative algorithm for minimizing this energy function. We also describe the construction of an equivalent electrical network with diodes and resistors that solves the same segmentation problem as our framework. Finally, our experiments on a database of 69 images show that the use of directional information does improve the segmenting power of the random Walker algorithm.