OpenRaiser/Intern-Atlas huggingface dataset:
Using Stationary-Dynamic Camera Assemblies for Wide-area Video Surveillance and Selective Attention
Authors: Ankur Jain, Dan Koppel, Kyle Kakligian, Yuan-Fang Wang
Venue: CVPR
Year: 2006

In this paper, we present a prototype video surveillance system that uses stationary-dynamic (or master-slave) camera assemblies to achieve wide-area surveillance and selective focus-of-attention. We address two critical issues in deploying such camera assemblies in real-world applications: off-line camera calibration and on-line selective focus-ofattention. Our contributions over existing techniques are twofold: (1) in terms of camera calibration, our technique calibrates all degrees-of-freedom (DOFs) of both stationary and dynamic cameras, using a closed-form solution that is both efficient and accurate, and (2) in terms of selective focus-of-attention, our technique correctly handles dynamic changes in the scene and varying object depths. This is a significant improvement over existing techniques that use an expensive and non-adaptable table-look-up process.
Multi-Resolution Patch Tensor for Facial Expression Hallucination
Authors: Kui Jia, Shaogang Gong
Venue: CVPR
Year: 2006

In this paper, we propose a sequential approach to hallucinate/ synthesize high-resolution images of multiple facial expressions. We propose an idea of multi-resolution tensor for super-resolution, and decompose facial expression images into small local patches. We build a multi-resolution patch tensor across different facial expressions. By unifying the identity parameters and learning the subspace mappings across different resolutions and expressions, we simplify the facial expression hallucination as a problem of parameter recovery in a patch tensor space. We further add a high-frequency component residue using nonparametric patch learning from high-resolution training data. We integrate the sequential statistical modelling into a Bayesian framework, so that given any low-resolution facial image of a single expression, we are able to synthesize multiple facial expression images in high-resolution. We show promising experimental results from both facial expression database and live video sequences.
Successive Convex Matching for Action Detection
Authors: Hao Jiang 0007, Mark S. Drew, Ze-Nian Li
Venue: CVPR
Year: 2006

We propose human action detection based on a successive convex matching scheme. Human actions are represented as sequences of postures and specific actions are detected in video by matching the time-coupled posture sequences to video frames. The template sequence to video registration is formulated as an optimal matching problem. Instead of directly solving the highly non-convex problem, our method convexifies the matching problem into linear programs and refines the matching result by successively shrinking the trust region. The proposed scheme represents the target point space with small sets of basis points and therefore allows efficient searching. This matching scheme is applied to robustly matching a sequence of coupled binary templates simultaneously in a video sequence with cluttered backgrounds.
Accelerated Kernel Feature Analysis
Authors: Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xingquan Zhu 0001
Venue: CVPR
Year: 2006

A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An \ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(\elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and M¨uller’s Kernel Principal Component Analysis (KPCA), and O(\ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopf’s Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem.
Panoramic 3D Reconstruction Using Rotational Stereo Camera with Simple Epipolar Constraints
Authors: Wei Jiang 0009, Masatoshi Okutomi, Shigeki Sugimoto
Venue: CVPR
Year: 2006

In this paper, we propose a novel method for panoramic 3D scene recovery using rotational stereo cameras with simple epipolar constraints. By rotating two parallel stereo cameras about a vertical axis with a constant velocity, we acquire two sampled spacio-temporal volumes which are made of the sequential images captured by a uniform angular interval. The two spacio-temporal volumes can be resampled into a set of multi-perspective panoramas. We analyze the epipolar geometry among images (panoramas and original images) of two spacio-temporal volumes. The result shows that only three types of simple epipolar constraints (epipolar line is row or column of image) exist in the two spacio-temporal volumes. Then we compute a depth map from four image pairs using a multi-baseline algorithm with the three types of epipolar constraints; that is horizontal, vertical and combination of them. Experimental results using both synthetic and real images show that our approach produces high quality panoramic 3D reconstruction.
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video
Authors: Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick
Venue: CVPR
Year: 2006

Recently, the generative modeling approach to video segmentation has been gaining popularity in the computer vision community. For example, the flexible sprites framework has been studied in, among other references, [11,13,14,24]. In general, detailed generative models are vulnerable to intractability of inference and local minima problems when approximations are made (see, e.g., [25]). Recent approaches to dealing with these problems focused on inference techniques for increasingly more expressive models. Simpler models, on the other hand, while less precise, are often not just faster, but less prone to local minima. In addition, while many different models may be based on similar hidden variables, some models may be more amenable to inference of some of the shared variables, while other models lead to efficient and accurate inference of other components of the hierarchical data description. In this paper, we empirically illustrate that forcing multiple models to share the posterior distribution leads to inference less prone to local minima. We define a set of key hidden variables that describe aspects of the data that we care about. The relationships among these key variables are defined through multiple conditional distribution models on the same pairs of variables, controlled by switch variables. The posterior distribution over the key hidden variables is shared, and inference of the switch variables serves as a mechanism for combinatorial model selection. The key observation here is that while the most expressive model often ends up a winner by the end of the iterative learning of model parameters, early iterations are dominated by simpler model components, and upon convergence, the free energy is lower than the ones reached by switching on all the most complex components from the beginning of the learning. We illustrate the performance of this approach on the unsupervised video segmentation task.
Active Graph Cuts
Authors: Olivier Juan, Yuri Boykov
Venue: CVPR
Year: 2006

This paper adds a number of novel concepts into global s/t cut methods improving their efficiency and making them relevant for a wider class of applications in vision where algorithms should ideally run in real-time. Our new Active Cuts (AC) method can effectively use a good approximate solution (initial cut) that is often available in dynamic, hierarchical, and multi-label optimization problems in vision. In many problems AC works faster than the state-of-the-art max-flow methods [2] even if initial cut is far from the optimal one. Moreover, empirical speed improves several folds when initial cut is spatially close to the optima. Before converging to a global minima, Active Cuts outputs a multitude of intermediate solutions (intermediate cuts) that, for example, can be used be accelerate iterative learning-based methods or to improve visual perception of graph cuts realtime performance when large volumetric data is segmented. Finally, it can also be combined with many previous methods for accelerating graph cuts.
A Joint Illumination and Shape Model for Visual Tracking
Authors: Amit A. Kale, Christopher O. Jaynes
Venue: CVPR
Year: 2006

Visual tracking involves generating an inference about the motion of an object from measured image locations in a video sequence. In this paper we present a unified framework that incorporates shape and illumination in the context of visual tracking. The contribution of the work is twofold. First, we introduce a a multiplicative, low dimensional model of illumination that is defined by a linear combination of a set of smoothly changing basis functions. Secondly, we show that a small number of centroids in this new space can be used to represent the illumination conditions existing in the scene. These centroids can be learned from ground truth and are shown to generalize well to other objects of the same class for the scene. Finally we show how this illumination model can be combined with shape in a probabilistic sampling framework. Results of the joint shape-illumination model are demonstrated in the context of vehicle and face tracking in challenging conditions.
Statistical Analysis of Local 3D Structure in 2D Images
Authors: Sinan Kalkan, Florentin Wörgötter, Norbert Krüger
Venue: CVPR
Year: 2006

For the analysis of images, a deeper understanding of their intrinsic structure is required. This has been obtained for 2D images by means of statistical analysis [15, 18]. Here, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texturelike structures) and the underlying local 3D structure, represented in terms of continuous surfaces and different kinds of 3D discontinuities, using 3D range data with the true color information. We find that homogeneous image patches correspond to continuous surfaces, and discontinuities are mainly formed by edge-like or corner-like structures. The results are discussed with regard to existing and potential computer vision applications and the assumptions made by these applications.
Distributed Cost Boosting and Bounds on Mis-classification Cost
Authors: John P. Kaufhold, Justin Abbott, Robert Kaucic
Venue: CVPR
Year: 2006

Many classification algorithms (such as Adaboost) aim to minimize training error, that is, the ratio of the number of mis-classified examples to the total number of examples. However, it is often desirable to minimize a more general cost metric, where distinct examples have different costs. For example, in industrial inspection, the cost of clearing a defective part is substantially greater than the cost of incorrectly alarming on a good part. Further, defects themselves vary in severity and, hence, cost. Extending boosting algorithms to account for this variable cost (cost-sensitive boosting) has been attempted by a number of investigators, resulting in a number of Adaboost variants, including AdaCost, Asymboost, CSB0, CSB1, and CSB2. These algorithms all focus only on the distribution update step in Adaboost-leaving the error computation step unchanged (or only indirectly modified). In this paper, we present a novel algorithm, termed "distributed cost boosting" (DCB), that incorporates the cost metric into the error computation step, rather than merely adjusting the distribution update step. DCB produces large empirical reductions in total cost when compared to current state-ofthe- art cost-sensitive Adaboost variants. Further, we show that distributed cost boosting provides error bounds on misclassification cost similar to AdaBoost’s bounds on training error rate.
Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification
Authors: Minyoung Kim 0001, Vladimir Pavlovic 0001
Venue: CVPR
Year: 2006

A mixture of Bayesian Network Classifiers(BNC) has a potential to yield superior classification and generative performance to a single BNC model. We introduce novel discriminative learning methods for mixtures of BNCs. Unlike a single BNC model where the discriminative learning resorts to a gradient search, we can exploit the properties of a mixture to alleviate the complex learning task. The proposed method adds mixture components recursively via functional gradient boosting while maximizing the conditional likelihood. This method is highly efficient as it reduces to generative learning of a base BNC model on weighed data. The proposed approach is particularly suited to sequence classification problems where the kernels in the base model are usually too complex for effective gradient search. We demonstrate the improved classification performance of the proposed methods in an extensive set of evaluations on time-series sequence data, including human motion classification problems.
Image Matching Using Photometric Information
Authors: Michael Kolomenkin, Ilan Shimshoni
Venue: CVPR
Year: 2006

Image matching is an essential task in many computer vision applications. It is obvious that thorough utilization of all available information is critical for the success of matching algorithms. However most popular matching methods do not incorporate effectively photometric data. Some algorithms are based on geometric, color invariant features, thus completely neglecting available photometric information. Others assume that color does not differ significantly in the two images; that assumption may be wrong when the images are not taken at the same time, for example when a recently taken image is compared with a database. This paper introduces a method for using color information in image matching tasks. Initially the images are segmented using an off-the-shelf segmentation process (EDISON). No assumptions are made on the quality of the segmentation. Then the algorithm employs a model for natural illumination change to define the probability of two segments to originate from the same surface. When additional information is supplied (for example suspected corresponding point features in both images), the probabilities are updated. We show that the probabilities can easily be utilized in any existing image matching system. We propose a technique to make use of them in a SIFT-based algorithm. The technique’s capabilities are demonstrated on real images, where it causes a significant improvement in comparison with the original SIFT results in the percentage of correct matches found.
Feature Selection for Evaluating Fluorescence Microscopy Images in Genome-Wide Cell Screens
Authors: Vassili Kovalev, Nathalie Harder, Beate Neumann, Michael Held, Urban Liebel, Holger Erfle, Jan Ellenberg, Roland Eils, Karl Rohr
Venue: CVPR
Year: 2006

We investigate different approaches for efficient feature space reduction and compare different methods for cell classification. The application context is the development of automatic methods for analysing fluorescence microscopy images with the goal to identify those genes that are involved in the mitosis of human cells (cell division). We distinguish four cell classes comprising interphase cells, mitotic cells, apoptotic cells, and cells with clustered nuclei. Feature space reduction was performed using the Principal Component Analysis and Independent Component Analysis methods. Six classification methods were examined including unsupervised clustering algorithms such as K-means, Hard Competitive Learning, and Neural Gas as well as Hierarchical Clustering, Support Vector Machines, and Random Forests classifiers. Detailed results on the cell image classification accuracy and computational efficiency achieved using different feature sets and different classification methods are reported.
Solving Markov Random Fields using Second Order Cone Programming Relaxations
Authors: M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
Venue: CVPR
Year: 2006

This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding
Authors: Sang-Mook Lee, A. Lynn Abbott, Neil A. Clark, Philip A. Araman
Venue: CVPR
Year: 2006

This paper introduces a new representation for planar curves. From the well-known Dirichlet problem for a disk, the harmonic function embedded in a circular disk is solely dependent on specified boundary values and can be obtained from Poisson’s integral formula. We derive a discrete version of Poisson’s formula and assess its harmonic properties. Various shape signatures can be used as boundary values, whereas only the corresponding Fourier descriptors are needed for the framework. The proposed approach is similar to a scale space representation but exhibits greater generality by accommodating using any type of shape signature. In addition, it is robust to noise and computationally efficient, and it is guaranteed to have a unique solution. In this paper, we demonstrate that the approach has strong potential for shape representation and matching applications.
Region-Tree Based Stereo Using Dynamic Programming Optimization
Authors: Cheng Lei, Jason M. Selzer, Yee-Hong Yang
Venue: CVPR
Year: 2006

In this paper, we present a novel stereo algorithm that combines the strengths of region-based stereo and dynamic programming on a tree approaches. Instead of formulating an image as individual scan-lines or as a pixel tree, a new region tree structure, which is built as a minimum spanning tree on the adjacency-graph of an over-segmented image, is used for the global dynamic programming optimization. The resulting disparity maps do not contain any streaking problem as is common in scanline-based algorithms because of the tree structure. The performance evaluation using the Middlebury benchmark datasets shows that the performance of our algorithm is comparable in accuracy and efficiency with top ranking algorithms.
Surface Geometric Constraints for Stereo in Belief Propagation
Authors: Gang Li, Steven W. Zucker
Venue: CVPR
Year: 2006

Belief propagation has been shown to be a powerful inference mechanism for stereo correspondence. However the classical formulation of belief propagation implicitly imposes the frontal parallel plane assumption in the compatibility matrix for exploiting contextual information, since the priors perfer no depth (disparity) change in surrounding neighborhoods. This results in systematic errors for slanted or curved surfaces. To eliminate these errors we propose to use contextual information geometrically, and show how to encode surface differential geometric properties in the compatibility matrix for stereo correspondence. This enforces consistency for both depth and surface normal, extending the traditional formulation beyond consistency for (constant) depth. With such geometric contextual information, the belief propagation algorithm shows dramatic improvement on generic non-frontal parallel scenes. Several such examples are provided.
Accurate Face Alignment using Shape Constrained Markov Network
Authors: Lin Liang, Fang Wen 0001, Ying-Qing Xu, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2006

In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.
Robust AAM Fitting by Fusion of Images and Disparity Data
Authors: Joerg Liebelt, Jing Xiao 0006, Jie Yang 0001
Venue: CVPR
Year: 2006

Active Appearance Models (AAMs) have been popularly used to represent the appearance and shape variations of human faces. Fitting an AAM to images recovers the face pose as well as its deformable shape and varying appearance. Successful fitting requires that the AAM is sufficiently generic such that it covers all possible facial appearances and shapes in the images. Such a generic AAM is often difficult to be obtained in practice, especially when the image quality is low or when occlusion occurs. To achieve robust AAM fitting under such circumstances, this paper proposes to incorporate the disparity data obtained from a stereo camera with the image fitting process. We develop an iterative multi-level algorithm that combines efficient AAM fitting to 2D images and robust 3D shape alignment to disparity data. Experiments on tracking faces in low-resolution images captured from meeting scenarios show that the proposed method achieves better performance than the original 2D AAM fitting algorithm. We also demonstrate an application of the proposed method to a facial expression recognition task.
Dynamic Appearance Modeling for Human Tracking
Authors: Hwasup Lim, Octavia I. Camps, Mario Sznaier, Vlad I. Morariu
Venue: CVPR
Year: 2006

Dynamic appearance is one of the most important cues for tracking and identifying moving people. However, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities. In this paper we present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter.