OpenRaiser/Intern-Atlas huggingface dataset:
MRF Augmented Particle Filter Tracker
Authors: Hee Lin Wang, Loong Fah Cheong
Venue: CVPR
Year: 2005

In particle filter trackers, the object a posteriori distribution is severely distorted under more challenging situations like occlusion. To overcome the problem, this paper proposes a principled manner of augmenting the particle filter algorithm with an MRF based representation of the tracked object within a dynamic Bayesian framework, where the object is transformed into a composite of multiple MRF regions. This results in more accurate modeling, thus improving the tracking performance. Additionally, Metropolis based sampling of the regions enhances the tracker with an adaptive ability. Finally, the resultant generative model provides a natural framework to integrate multiple cues. Experiments show good tracking results for challenging situations.
Online Selecting Discriminative Tracking Features Using Particle Filter
Authors: Jianyu Wang, Xilin Chen 0001, Wen Gao 0001
Venue: CVPR
Year: 2005

The paper proposes a method to keep the tracker robust to background clutters by online selecting discriminative features from a large feature space. Furthermore, the feature selection procedure is embedded into the particle filtering process with the aid of existed "background" particles. Feature values from background patches and object observations are sampled during tracking and Fisher discriminant is employed to rank the classification capacity of each feature based on sampled values. Top-ranked discriminative features are selected into the appearance model and simultaneously invalid features are removed out to adjust the object representation adaptively. The implemented tracker with online discriminative feature selection module embedded shows promising results on experimental video sequences.
MosaicShape: Stochastic Region Grouping with Shape Prior
Authors: Jingbin Wang, Erdan Gu, Margrit Betke
Venue: CVPR
Year: 2005

A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework, and object segmentation and recognition are accomplished simultaneously by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized cluster sampling algorithm is presented in A. Barbu and S. Zhu (2003), combined with a large sampling jump and other implementation improvements, and greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences
Authors: Yang Wang 0002, Qiang Ji
Venue: CVPR
Year: 2005

This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of video frames. The segmentation method employs both intensity and motion cues, and it combines dynamic information and spatial interaction of the observed data. Experimental results show that the proposed approach effectively fuses contextual constraints in video sequences and improves the accuracy of object segmentation.
Learn Discriminant Features for Multi-View Face and Eye Detection
Authors: Peng Wang 0005, Qiang Ji
Venue: CVPR
Year: 2005

In current face detection, mostly often used features are selected from a large set (e.g. Haar wavelets). Generally Haar wavelets only represent the local geometric feature. When applying those features to profile faces and eyes with irregular geometric patterns, the classifier accuracy is low in the later training stages, only near 50%. In this paper, instead of brute-force searching the large feature set, we propose to statistically learn the discriminant features for object detection. Besides applying Fisher discriminant analysis(FDA) in AdaBoost, we further propose the recursive nonparametric discriminant analysis (RNDA) to handle more general cases. Those discriminant analysis features are not constrained with geometric shape and can provide better accuracy. The compact size of feature set allows to select a near optimal subset of features and construct the probabilistic classifiers by greedy searching. The proposed methods are applied to multi-view face and eye detection and achieve good accuracy.
Subspace Analysis Using Random Mixture Models
Authors: Xiaogang Wang 0001, Xiaoou Tang
Venue: CVPR
Year: 2005

In a work by Wang and Tang (2004), three popular subspace face recognition methods, PCA, Bayes, and LDA were analyzed under the same framework and an unified subspace analysis was proposed. However, since they are all based on a single Gaussian model, a global linear subspace often fails to deliver good performance on the data set with complex intrapersonal variation. They also have to face the problem caused by high dimensional face feature vector and the difficulty in finding optimal parameters for subspace analysis. In this paper, we develop a random mixture model to improve Bayes and LDA subspace analysis. By clustering the intrapersonal difference, the complex intrapersonal variation manifold is learned by a set of local linear intrapersonal subspaces. To boost the system performance, we construct multiple low dimensional subspaces by randomly sampling on the high dimensional feature vector and randomly selecting the parameters for subspace analysis. The effectiveness of our method is demonstrated by experiments on the AR face database containing 2340 face images.
Videoshop: A New Framework for Spatio-Temporal Video Editing in Gradient Domain
Authors: Hongcheng Wang, Ning Xu 0005, Ramesh Raskar, Narendra Ahuja
Venue: CVPR
Year: 2005

Our goal is to develop tools that go beyond frame-constrained manipulation such as resizing, color correction, and simple transitions, and provide object-level operations within frames. Some of our targeted video editing tasks includes transferring a motion picture to a new still picture, importing a moving object into a new background, and compositing two video sequences. The challenges behind this kind of complex video editing tasks lie in two constraints: 1) Spatial consistency: imported objects should blend with the background seamlessly. Hence pixel replacement, which creates noticeable seams, is problematic. 2) Temporal coherency: successive frames should display smooth transitions. Hence frame-by-frame editing, which results in visual flicker, is inappropriate. Our work is aimed at providing an easy-to-use video editing tool that maximally satisfies the spatial and temporal constraints mentioned above and requires minimum user interaction. We propose a new framework for video editing in gradient domain. The spatio-temporal gradient fields of target videos are modified and/or mixed to generate a new gradient field which is usually not integrable. We propose a 3D video integration algorithm, which uses the variational method, to find the potential function whose gradient field is closest to the mixed gradient field in the sense of least squares. The video is reconstructed by solving a 3D Poisson equation. We derive an extension of current 2D gradient technique to 3D space, yielding in a novel video editing framework, which is very different from all current video editing software.
Estimating Disparity and Occlusions in Stereo Video Sequences
Authors: Oliver Williams, Michael Isard, John MacCormick
Venue: CVPR
Year: 2005

We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly models half-occlusions - scene points visible in one camera but not the other - and (ii) enforces the so-called "monotonicity constraint" on the boundary of half-occluded regions. The algorithm is also able to exploit temporal coherence more appropriately than many previous approaches to temporal stereo, by employing additional states in the Markov random field. These additional states permit rudimentary motion estimation to be performed as part of the belief propagation, thus improving the quality of temporal inference. Parameters of the algorithm are learned from the ground truth disparities of a real stereo sequence. Qualitative results are shown on real sequences, including comparisons with competing approaches, and the performance of the algorithm is assessed quantitatively using the ground truth data.
Combining Variable Selection with Dimensionality Reduction
Authors: Lior Wolf, Stanley M. Bileschi
Venue: CVPR
Year: 2005

This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reduction algorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data, since many features are similar in quality. Dimensionality reduction algorithms tend to combine all variables and cannot select a subset of significant variables. Our approach combines both methodologies by applying variable selection followed by dimensionality reduction. This combination makes sense only when using the same utility function in both stages, which we do. The resulting algorithm benefits from complex features as variable selection algorithms do, and at the same time enjoys the benefits of dimensionality reduction.
Robust Boosting for Learning from Few Examples
Authors: Lior Wolf, Ian Martin
Venue: CVPR
Year: 2005

We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to make more robust classification functions. Using this framework, we propose a simple addition to the gentle boosting algorithm which enables it to work with only a few examples. We test this new algorithm on a variety of datasets and show convincing results.
Personal Identification Utilizing Finger Surface Features
Authors: Damon L. Woodard, Patrick J. Flynn
Venue: CVPR
Year: 2005

In this paper we present a novel approach for personal identification, which utilizes finger surface features as a biometric identifier. Using dense range data images of the hand, we calculate the curvature-based surface representation, shape index, for the index, middle, and ring fingers. This representation is used for comparisons to determine subject similarity. Our experiments involve the use of a large data set of range images collected over time. We examine the performance of individual finger surfaces as a biometric identifier as well as the performance when using the three finger surfaces in conjunction. The results of our experiments are presented, which indicate that this approach performs well for a first-of-its-kind biometric technique.
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
Authors: Tai-Pang Wu, Chi-Keung Tang
Venue: CVPR
Year: 2005

We present a surprisingly simple system that performs robust normal reconstruction by dense photometric stereo, in the presence of large shadows, highlight, transparencies, complex geometry, variable attenuation in light intensity and inaccurate light directions. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, we infer a dense set of unbiased but noisy photometric data uniformly distributed on the light direction sphere. We use this dense set to derive a very robust matching cost for our MRF photometric stereo model, where the maximum a posteriori (MAP) solution is estimated. To aggregate support for candidate normals in the normal refinement process, we introduce a compatibility function that is translated into a discontinuity-preserving metric, thus speeding up the MAP estimation by energy minimization using graph cut. No reference object of similar material is used. We perform detailed comparison on our approach with conventional convex minimization. We show very good normals estimated from very noisy data on a wide range of difficult objects to show the robustness and usefulness of our method.
Program Committee
Venue: CVPR
Year: 2005

Today's wireless sensor networks (WSN) focus on energy-efficiency as the main metric to optimize. However, an increasing number of scenarios where sensor networks are considered for time-critical purposes in application scenarios like intrusion detection, industrial monitoring, or health care systems demands for an explicit support of performance guarantees in WSNs and, thus, in turn for a respective mathematical framework. In (J. Schmitt and U. Roedig, 2005) , a sensor network calculus was introduced in order to accommodate a worst-case analysis of WSNs. This sensor network calculus focused on the communication aspect in WSNs, but had not yet a possibility to treat in-network processing in WSNs. In this work, we now incorporate in-network processing features as they are typical for WSNs by taking into account computational resources on the sensor nodes. Furthermore, we propose a simple, yet effective priority queue management discipline which achieves a good balance of response times across sensor nodes in the field.
Additional Reviewers
Venue: CVPR
Year: 2005

Structured (a.k.a. symbolic) representation techniques of Markov models have, to a large extent, been used effectively for representing very large transition matrices and their associated state spaces. However, their success means that the largest space requirement encountered when analyzing these models is often the representation of their iteration and solution vectors. In this paper, we present a new approach for computing bounds on solutions of transient measures in large continuous-time Markov chains (CTMCs). The approach extends existing path- and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship allows us to explore multiple paths at the same time, thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow us to analyze very large models using a very small amount of storage. In addition to presenting the method itself we illustrate its use to compute the reliability and the availability of a large distributed information service system in which faults may propagate across subsystems.
Accurate Motion Layer Segmentation and Matting
Authors: Jiangjian Xiao, Mubarak Shah
Venue: CVPR
Year: 2005

Given a video sequence, obtaining accurate layer segmentation and alpha matting is very important for various applications. However, when a non-textured or smooth area is present in the scene, the segmentation based on only single motion cue usually cannot provide satisfactory results. Conversely, the most matting approaches require a smooth assumption on foreground and background to obtain a good result. In this paper, we combine the merits of motion segmentation and alpha matting technique together to simultaneously achieve high-quality layer segmentation and alpha mattes. First, we explore a general occlusion constraint and design a novel graph cuts framework to solve the layer-based motion segmentation problem for the textured regions using multiple frames. Then, an alpha matting technique is further used to refine the segmentation and resolve the non-textured ambiguities by determining proper alpha values for the foreground and background respectively.
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
Authors: Rong Yan, Milind R. Naphade
Venue: CVPR
Year: 2005

For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose a novel multi-view semi-supervised learning algorithm called semi-supervised cross feature learning (SCFL). The proposed algorithm has two advantages over co-training. First, SCFL can theoretically guarantee its performance not being significantly degraded even when the assumption of view sufficiency fails. Also, SCFL can also handle additional views of unlabeled data even when these views are absent from the training data. As demonstrated in the TRECVID '03 semantic concept extraction task, the proposed SCFL algorithm not only significantly outperforms the conventional co-training algorithms, but also comes close to achieving the performance when the unlabeled set were to be manually annotated and used for training along with the labeled data set.
A Factorization-Based Approach to Articulated Motion Recovery
Authors: Jingyu Yan 0002, Marc Pollefeys
Venue: CVPR
Year: 2005

This paper addresses the subspace properties and the recovery of articulated motion. We point out that the global motion subspace of an articulated object is a combination of a number of intersecting rigid motion subspaces of the parts. Depending on whether a link of two parts is a joint or an axis, the global motion subspace loses one or two in rank for each link. The rank loss results from the intersection between the rigid motion subspaces of linked parts. Furthermore, the intersection is, in fact, the motion subspace of the link. From these observations, we describe the rank constraint of the global motion subspace of an articulated object; we give an algorithm to recover the image motion of a link, either a joint or an axis; and we propose a novel but simple approach, which is based on subspace clustering, to recover articulated shape and motion from a single-view image sequence.
A Factorization-Based Approach to Articulated Motion Recovery
Authors: Jingyu Yan 0002, Marc Pollefeys
Venue: CVPR
Year: 2005

In this paper, we demonstrate our factorization-based approach to articulated motion recovery by applying it to two image sequences: one is a sequence of an articulated object with a rotation axis and the other, a rotation joint. We show that the articulated shape and motion as well as the rotation axis or joint can be recovered using our approach.
Graph Embedding: A General Framework for Dimensionality Reduction
Authors: Shuicheng Yan, Dong Xu 0001, Benyu Zhang, HongJiang Zhang
Venue: CVPR
Year: 2005

In the last decades, a large family of algorithms - supervised or unsupervised; stemming from statistic or geometry theory - have been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a general framework, graph embedding along with its linearization and kernelization, which in theory reveals the underlying objective shared by most previous algorithms. It presents a unified perspective to understand these algorithms; that is, each algorithm can be considered as the direct graph embedding or its linear/kernel extension of some specific graph characterizing certain statistic or geometry property of a data set. Furthermore, this framework is a general platform to develop new algorithm for dimensionality reduction. To this end, we propose a new supervised algorithm, Marginal Fisher Analysis (MFA), for dimensionality reduction by designing two graphs that characterize the intra-class compactness and inter-class separability, respectively. MFA measures the intra-class compactness with the distance between each data point and its neighboring points of the same class, and measures the inter-class separability with the class margins; thus it overcomes the limitations of traditional Linear Discriminant Analysis algorithm in terms of data distribution assumptions and available projection directions. The toy problem on artificial data and the real face recognition experiments both show the superiority of our proposed MFA in comparison to LDA.
Coupled Kernel-Based Subspace Learning
Authors: Shuicheng Yan, Dong Xu 0001, Lei Zhang 0001, Benyu Zhang, HongJiang Zhang
Venue: CVPR
Year: 2005

It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the kernel discriminant analysis (KDA) algorithm as an example to perform kernel analysis on 2D image matrices directly. First, each image matrix is decomposed as the product of two orthogonal matrices and a diagonal one by using singular value decomposition; then an image matrix is expanded to be of higher or even infinite dimensions by applying the kernel trick on the column vectors of the two orthogonal matrices; finally, two coupled discriminative kernel subspaces are iteratively learned for dimensionality reduction by optimizing the Fisher criterion measured by Frobenius norm. The derived algorithm, called coupled kernel discriminant analysis (CKDA), effectively utilizes the underlying spatial structure of objects and the discriminating information is encoded in two coupled kernel subspaces respectively. The experiments on real face databases compared with KDA and Fisherface validate the effectiveness of CKDA.
Efficient Mean-Shift Tracking via a New Similarity Measure
Authors: Changjiang Yang, Ramani Duraiswami, Larry S. Davis
Venue: CVPR
Year: 2005

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.