OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic gradient kernel density mode-seeking
Authors: Xiaotong Yuan, Stan Z. Li
Venue: CVPR
Year: 2009

As a well known fixed-point iteration algorithm for kernel density mode-seeking, mean-shift has attracted wide attention in pattern recognition field. To date, mean-shift algorithm is typically implemented in a batch way with the entire data set known at once. In this paper, based on stochastic gradient optimization technique, we present the stochastic gradient mean-shift (SG-MS) along with its approximation performance analysis. We apply SG-MS to the speedup of Gaussian blurring mean-shift (GBMS) clustering. Experiments in toy problems and image segmentation show that, while the clustering accuracy is comparable between SG-GBMS and Naive-GBMS, the former significantly outperforms the latter in running time.
Discriminative subvolume search for efficient action detection
Authors: Junsong Yuan 0001, Zicheng Liu 0001, Ying Wu 0001
Venue: CVPR
Year: 2009

Actions are spatio-temporal patterns which can be characterized by collections of spatio-temporal invariant features. Detection of actions is to find the re-occurrences (e.g. through pattern matching) of such spatio-temporal patterns. This paper addresses two critical issues in pattern matching-based action detection: (1) efficiency of pattern search in 3D videos and (2) tolerance of intra-pattern variations of actions. Our contributions are two-fold. First, we propose a discriminative pattern matching called naive-Bayes based mutual information maximization (NBMIM) for multi-class action categorization. It improves the state-of-the-art results on standard KTH dataset. Second, a novel search algorithm is proposed to locate the optimal subvolume in the 3D video space for efficient action detection. Our method is purely data-driven and does not rely on object detection, tracking or background subtraction. It can well handle the intra-pattern variations of actions such as scale and speed variations, and is insensitive to dynamic and clutter backgrounds and even partial occlusions. The experiments on versatile datasets including KTH and CMU action datasets demonstrate the effectiveness and efficiency of our method.
Nonlinear Nonnegative Component Analysis
Authors: Stefanos Zafeiriou, Maria Petrou
Venue: CVPR
Year: 2009

In this paper general solutions for nonlinear nonnegative component analysis for data representation and recognition are proposed. That is, motivated by a combination of the Nonnegative Matrix Factorization (NMF) algorithm and kernel theory, which has lead to an NMF algorithm in a polynomial feature space, we propose a general framework where one can build a nonlinear nonnegative component analysis using kernels, the so-called projected gradient Kernel Nonnegative Matrix Factorization (PGKNMF). In the proposed approach, arbitrary positive kernels can be adopted while at the same time it is ensured that the limit point of the procedure is a stationary point of the optimization problem. Moreover, we propose fixed point algorithms for the special case of Radial Basis Function (RBF) kernels. We demonstrate the power of the proposed methods in face and facial expression recognition applications.
Surface feature detection and description with applications to mesh matching
Authors: Andrei Zaharescu, Edmond Boyer, Kiran Varanasi, Radu Horaud
Venue: CVPR
Year: 2009

In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, such as photometric information. This is motivated by the growing need for matching and tracking photometric surfaces over temporal sequences, due to recent advancements in multiple camera 3D reconstruction. We propose a 3D feature detector (MeshDOG) and a 3D feature descriptor (MeshHOG) for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale. The descriptor is able to capture the local geometric and/or photometric properties in a succinct fashion. Moreover, the method is defined generically for any scalar function, e.g., local curvature. Results with matching rigid and non-rigid meshes demonstrate the interest of the proposed framework.
Efficient Kernels for identifying unbounded-order spatial features
Authors: Yimeng Zhang, Tsuhan Chen
Venue: CVPR
Year: 2009

Higher order spatial features, such as doublets or triplets have been used to incorporate spatial information into the bag-of-local-features model. Due to computational limits, researchers have only been using features up to the 3rd order, i.e., triplets, since the number of features increases exponentially with the order. We propose an algorithm for identifying high-order spatial features efficiently. The algorithm directly evaluates the inner product of the feature vectors from two images to be compared, identifying all high-order features automatically. The algorithm hence serves as a kernel for any kernel-based learning algorithms. The algorithm is based on the idea that if a high-order spatial feature co-occurs in both images, the occurrence of the feature in one image would be a translation from the occurrence of the same feature in the other image. This enables us to compute the kernel in time that is linear to the number of local features in an image (same as the bag of local features approach), regardless of the order. Therefore, our algorithm does not limit the upper bound of the order as in previous work. The experiment results on the object categorization task show that high order features can be calculated efficiently and provide significant improvement in object categorization performance.
A multiscale hybrid model exploiting heterogeneous contextual relationships for image segmentation
Authors: Lei Zhang 0011, Qiang Ji
Venue: CVPR
Year: 2009

We propose a framework that can conveniently capture heterogeneous relationships among multiple random variables. The framework is formulated based on a hybrid probabilistic graphical model. It allows using both directed links and undirected links to capture various types of relationships. Based on this framework, we develop a multiscale hybrid model for image segmentation. The multiscale model systematically captures the spatial relationships and causal relationships among such image entities as regions, edges, and vertices at different scales. We further show how to parameterize such a hybrid model and how to factorize its joint probability distribution according to the global Markov properties. Based on this factorization, we exploit the Factor Graph theory to perform joint probabilistic inference and solve for the image segmentation problem.
Learning semantic scene models by object classification and trajectory clustering
Authors: Tianzhu Zhang 0001, Hanqing Lu, Stan Z. Li
Venue: CVPR
Year: 2009

Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian mixture model (GMM), and supplied to this graph. The graph cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real world scenes show the validity of our proposed method.
Textural Hausdorff Distance for wider-range tolerance to pose variation and misalignment in 2D face recognition
Authors: Sanqiang Zhao, Yongsheng Gao 0001
Venue: CVPR
Year: 2009

This paper addresses two critical but rarely concerned issues in 2D face recognition: wider-range tolerance to pose variation and misalignment. We propose a new Textural Hausdorff Distance (THD), which is a compound measurement integrating both spatial and textural features. The THD is applied to a Significant Jet Point (SJP) representation of face images, where a varied number of shape-driven SJPs are detected automatically from low-level edge map with rich information content. The comparative experiments conducted on publicly available FERET and AR face databases demonstrated that the proposed approach has a considerably wider range of tolerance against both in-depth head rotation and face misalignment.
Constrained marginal space learning for efficient 3D anatomical structure detection in medical images
Authors: Yefeng Zheng 0001, Bogdan Georgescu, Haibin Ling, Shaohua Kevin Zhou, Michael Scheuering, Dorin Comaniciu
Venue: CVPR
Year: 2009

Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities. To accurately localize a 3D object, we need to estimate nine parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of uniformly searching the original nine-dimensional parameter space, only low-dimensional marginal spaces are uniformly searched in MSL, which significantly improves the speed. In many real applications, a strong correlation may exist among parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. In this paper, we propose constrained MSL to exploit this correlation for further speed-up. As another major contribution, we propose to use quaternions for 3D orientation representation and distance measurement to overcome the inherent drawbacks of Euler angles in the original MSL. The proposed method has been tested on three 3D anatomical structure detection problems in medical images, including liver detection in computed tomography (CT) volumes, and left ventricle detection in both CT and ultrasound volumes. Experiments on the largest datasets ever reported show that constrained MSL can improve the detection speed up to 14 times, while achieving comparable or better detection accuracy. It takes less than half a second to detect a 3D anatomical structure in a volume.
Single-image optical center estimation from vignetting and tangential gradient symmetry
Authors: Yuanjie Zheng, Chandra Kambhamettu, Stephen Lin 0001
Venue: CVPR
Year: 2009

In this paper, we propose a method for estimating the optical center of a camera given only a single image with vignetting. This is accomplished by identifying the center of the vignetting effect in the image through an analysis of semicircular tangential gradients (SCTGs). For a given image pixel, the SCTG is the image gradient along the tangential direction of the circle centered at the currently estimated optical center and passing through the pixel. We show that for natural images with vignetting, the distribution of SCTGs is generally symmetric if the optical center is estimated accurately, but is skewed otherwise. By minimizing the asymmetry of the SCTG distribution with nonlinear optimization, our method is able to obtain reliable estimates of the optical center. Experiments on simulated and real vignetting images demonstrate the effectiveness of this technique.
Fast car detection using image strip features
Authors: Wei Zheng, Luhong Liang
Venue: CVPR
Year: 2009

This paper presents a fast method for detecting multi-view cars in real-world scenes. Cars are artificial objects with various appearance changes, but they have relatively consistent characteristics in structure that consist of some basic local elements. Inspired by this, we propose a novel set of image strip features to describe the appearances of those elements. The new features represent various types of lines and arcs with edge-like and ridge-like strip patterns, which significantly enrich the simple features such as haar-like features and edgelet features. They can also be calculated efficiently using the integral image. Moreover, we develop a new complexity-aware criterion for RealBoost algorithm to balance the discriminative capability and efficiency of the selected features. The experimental results on widely used single view and multi-view car datasets show that our approach is fast and has good performance.
Robust object detection using marginal space learning and ranking-based multi-detector aggregation: Application to left ventricle detection in 2D MRI images
Authors: Yefeng Zheng 0001, Xiaoguang Lu, Bogdan Georgescu, Arne Littmann, Edgar Mueller, Dorin Comaniciu
Venue: CVPR
Year: 2009

Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in the orientation, size, shape, and image intensity of the LV, automatic LV detection is challenging. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Unlike full space learning (FSL) where a monolithic classifier is trained directly in the five dimensional object pose space (two for position, one for rotation, and two for anisotropic scaling), we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. As a contribution of this paper, we perform thorough comparison between MSL and FSL. Experiments show MSL significantly outperforms FSL on both the training and test sets. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness even when one detector fails. A novel ranking-based strategy is proposed to combine the detected candidates from all detectors. Experiments show our ranking-based aggregation approach can significantly reduce the detection outliers.
A streaming framework for seamless building reconstruction from large-scale aerial LiDAR data
Authors: Qian-Yi Zhou, Ulrich Neumann
Venue: CVPR
Year: 2009

We present a streaming framework for seamless building reconstruction from huge aerial LiDAR point sets. By storing data as stream files on hard disk and using main memory as only a temporary storage for ongoing computation, we achieve efficient out-of-core data management. This gives us the ability to handle data sets with hundreds of millions of points in a uniform manner. By adapting a building modeling pipeline into our streaming framework, we create the whole urban model of Atlanta from 17.7 GB LiDAR data with 683 M points in under 25 hours using less than 1 GB memory. To integrate this complex modeling pipeline with our streaming framework, we develop a state propagation mechanism, and extend current reconstruction algorithms to handle the large scale of data.
Nonrigid shape recovery by Gaussian process regression
Authors: Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Venue: CVPR
Year: 2009

Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangulated mesh models heavily relying on the quadratic regularization term are difficult to accurately capture large deformations, such as severe bending. In this paper, we propose a novel Gaussian process regression approach to the nonrigid shape recovery problem, which does not require to involve a predefined triangulated mesh model. By taking advantage of our novel Gaussian process regression formulation together with a robust coarse-to-fine optimization scheme, the proposed method is fully automatic and is able to handle large deformations and outliers. We conducted a set of extensive experiments for performance evaluation in various environments. Encouraging experimental results show that our proposed approach is both effective and robust to nonrigid shape recovery with large deformations.
Joint depth and alpha matte optimization via fusion of stereo and time-of-flight sensor
Authors: Jiejie Zhu, Miao Liao, Ruigang Yang, Zhigeng Pan
Venue: CVPR
Year: 2009

We present a new approach to iteratively estimate both high-quality depth map and alpha matte from a single image or a video sequence. Scene depth, which is invariant to illumination changes, color similarity and motion ambiguity, provides a natural and robust cue for foreground/ background segmentation - a prerequisite for matting. The image mattes, on the other hand, encode rich information near boundaries where either passive or active sensing method performs poorly. We develop a method to combine the complementary nature of scene depth and alpha matte to mutually enhance their qualities. We formulate depth inference as a global optimization problem where information from passive stereo, active range sensor and matte is merged. The depth map is used in turn to enhance the matting. In addition, we extend this approach to video matting by incorporating temporal coherence, which reduces flickering in the composite video. We show that these techniques lead to improved accuracy and robustness for both static and dynamic scenes.
Pareto discriminant analysis
Authors: Karim T. Abou-Moustafa, Fernando De la Torre, Frank P. Ferrie
Venue: CVPR
Year: 2010

Linear Discriminant Analysis (LDA) is a popular tool for multiclass discriminative dimensionality reduction. However, LDA suffers from two major problems: (1) It only optimizes the Bayes error for the case of unimodal Gaussian classes with equal covariances (assuming full rank matrices) and, (2) The multiclass extension maximizes the sum of pairwise distances between the classes, and does not "simultaneously" maximize each pairwise distance between the classes. This typically results in serious overlapping in the projected space between classes that are "close" in the input space. To solve these two problems, this paper proposes Pareto Discriminant Analysis (PARDA). Firstly, PARDA explicitly models each of the classes as a multidimensional Gaussian with a sample covariance. Secondly, PARDA decomposes the multiclass problem to a set of pairwise objective functions representing the pairwise distance between different classes. Unlike existing extensions of Fisher discriminant analysis (FDA) to multiclass problems, that typically maximize the sum of pairwise distances between classes, PARDA simultaneously maximizes each pairwise distance, thus encouraging the case that all classes are equidistant from each other in the lower dimensional space. Solving PARDA is a multiobjective optimization problem - simultaneously optimizing more than one, possibly conflicting, objective functions - and the resulting solution is known to be "Pareto Optimal". Experimental results on synthetic data, several image data sets and data sets from the UCI repository show positive and encouraging results in favor of PARDA when compared with standard and state-of-the-art multiclass extensions of LDA.
A probabilistic framework for joint segmentation and tracking
Authors: Chad Aeschliman, Johnny Park, Avinash C. Kak
Venue: CVPR
Year: 2010

Most tracking algorithms implicitly apply a coarse segmentation of each target object using a simple mask such as a rectangle or an ellipse. Although convenient, such coarse segmentation results in several problems in tracking - drift, switching of targets, poor target localization, to name a few - since it inherently includes extra non-target pixels if the mask is larger than the target or excludes some portion of target pixels if the mask is smaller than the target. In this paper, we propose a novel probabilistic framework for jointly solving segmentation and tracking. Starting from a joint Gaussian distribution over all the pixels, candidate target locations are evaluated by first computing a pixel-level segmentation and then explicitly including this segmentation in the probability model. The segmentation is also used to incrementally update the probability model based on a modified probabilistic principal component analysis (PPCA). Our experimental results show that the proposed method of explicitly considering pixel-level segmentation as a part of solving the tracking problem significantly improves the robustness and performance of tracking compared to other state-of-the-art trackers, particularly for tracking multiple overlapping targets.
Optimal coded sampling for temporal super-resolution
Authors: Amit K. Agrawal, Mohit Gupta 0001, Ashok Veeraraghavan, Srinivasa G. Narasimhan
Venue: CVPR
Year: 2010

Conventional low frame rate cameras result in blur and/or aliasing in images while capturing fast dynamic events. Multiple low speed cameras have been used previously with staggered sampling to increase the temporal resolution. However, previous approaches are inefficient: they either use small integration time for each camera which does not provide light benefit, or use large integration time in a way that requires solving a big ill-posed linear system. We propose coded sampling that address these issues: using N cameras it allows N times temporal superresolution while allowing ~N/2 times more light compared to an equivalent high speed camera. In addition, it results in a well-posed linear system which can be solved independently for each frame, avoiding reconstruction artifacts and significantly reducing the computational time and memory. Our proposed sampling uses optimal multiplexing code considering additive Gaussian noise to achieve the maximum possible SNR in the recovered video. We show how to implement coded sampling on off-the-shelf machine vision cameras. We also propose a new class of invertible codes that allow continuous blur in captured frames, leading to an easier hardware implementation.
What is an object?
Authors: Bogdan Alexe, Thomas Deselaers, Vittorio Ferrari
Venue: CVPR
Year: 2010

We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. This includes an innovative cue measuring the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined measure to perform better than any cue alone. Finally, we show how to sample windows from an image according to their objectness distribution and give an algorithm to employ them as location priors for modern class-specific object detectors. In experiments on PASCAL VOC 07 we show this greatly reduces the number of windows evaluated by class-specific object detectors.
Morphological snakes
Authors: Luis Álvarez 0001, Luis Baumela, Pedro Henríquez, Pablo Márquez-Neila
Venue: CVPR
Year: 2010

We introduce a morphological approach to curve evolution. The differential operators used in the standard PDE snake models can be approached using morphological operations on a binary level set. By combining the morphological operators associated to the PDE components we achieve a new snakes evolution algorithm. This new solution is based on numerical methods which are very simple, fast and stable. Moreover, since the level set is just a binary piecewise constant function, this approach does not require to estimate a contour distance function. To illustrate the results obtained we present some numerical experiments on real images.