OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Distribution-based Matching by Random Subsampling
Authors: Alex Po Leung, Shaogang Gong
Venue: CVPR
Year: 2007

We boost the efficiency and robustness of distribution-based matching by random subsampling which results in the minimum number of samples required to achieve a specified probability that a candidate sampling distribution is a good approximation to the model distribution. The improvement is demonstrated with applications to object detection, mean-shift tracking using color distributions and tracking with improved robustness for low-resolution video sequences. The problem of minimizing the number of samples required for robust distribution matching is formulated as a constrained optimization problem with the specified probability as the objective function. We show that surprisingly mean-shift tracking using our method requires very few samples. Our experiments demonstrate that robust tracking can be achieved with even as few as 5 random samples from the distribution of the target candidate. This leads to a considerably reduced computational complexity that is also independent of object size. We show that random subsampling speeds up tracking by two orders of magnitude for typical object sizes.
Spectral Matting
Authors: Anat Levin, Alex Rav-Acha, Dani Lischinski
Venue: CVPR
Year: 2007

We present spectral matting: a new approach to natural image matting that automatically computes a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. These components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input.
Toward Flexible 3D Modeling using a Catadioptric Camera
Authors: Maxime Lhuillier
Venue: CVPR
Year: 2007

Fully automatic 3D modeling from a catadioptric image sequence has rarely been addressed until now, although this is a long-standing problem for perspective images. All previous catadioptric approaches have been limited to dense reconstruction for a few view points, and the majority of them require calibration of the camera. This paper presents a method which deals with hundreds of images, and does not require precise calibration knowledge. In this context, the same 3D point of the scene may be visible and reconstructed in a large number of images at very different accuracies. So the main part of this paper concerns the selection of reconstructed points, a problem largely ignored in previous works. Summaries of the structure from motion and dense stereo steps are also given. Experiments include the 3D model reconstruction of indoor and outdoor scenes, and a walkthrough in a city.
Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans
Authors: Yuan Li 0022, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade
Venue: CVPR
Year: 2007

Tracking object in low frame rate video or with abrupt motion poses two main difficulties which conventional tracking methods can barely handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection, and present a temporal probabilistic combination of discriminative observers of different lifespans. Each observer is learned from different ranges of samples, with different subsets of features, to achieve varying level of discriminative power at varying cost. An efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling. Experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods, under the condition of low frame rate data and abrupt motion of both target and camera.
Hierarchical Structuring of Data on Manifolds
Authors: Jun Li 0010, Pengwei Hao
Venue: CVPR
Year: 2007

Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned embedded representation of the training samples. This process often involves accessing considerable volume of data for large sample set. In this paper, an approach of selecting "landmark points" from the given samples is proposed for hierarchical structuring of data on manifolds. The selection is made such that if one use the Voronoi diagram generated by the landmark points in the ambient space to partition the embeded manifold, the topology of the manifold is preserved. The landmark points then are used to recursively construct a hierarchical structure of the data. Thus it can speed up queries in a manifold data set. It is a general framework that can fit any manifold learning algorithm as long as its result of an input can be predicted by the results of the neighbor inputs. Compared to the existing techniques of organizing data based on spatial partitioning, our method preserves the topology of the latent space of the data. Different from manifold learning algorithms that use landmark points to reduce complexity, our approach is designed for fast retrieval of samples. It may find its way in high dimensional data analysis such as indexing, clustering, and progressive compression. More importantly, it extends the manifold learning methods to applications in which they were previously considered to be not fast enough. Our algorithm is stable and fast, and its validity is proved mathematically.
Implicit Active Contours Driven by Local Binary Fitting Energy
Authors: Chunming Li, Chiu-Yen Kao, John C. Gore, Zhaohua Ding
Venue: CVPR
Year: 2007

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.
A Closed-form Solution to 3D Reconstruction of Piecewise Planar Objects from Single Images
Authors: Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
Venue: CVPR
Year: 2007

This paper proposes a new approach to 3D reconstruction of piecewise planar objects based on two image regularities, connectivity and perspective symmetry. First, we formulate the whole shape of the objects in an image as a shape vector consisting of the normals of all the faces of the objects. Then, we impose several linear constraints on the shape vector using connectivity and perspective symmetry of the objects. Finally, we obtain a closed-form solution to the 3D reconstruction problem. We also develop an efficient algorithm to detect a face of perspective symmetry. Experimental results on real images are shown to demonstrate the effectiveness of our approach.
Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face Recognition
Authors: Yung-hui Li, Marios Savvides
Venue: CVPR
Year: 2007

Traditional linear Fukunaga-Koontz transform (FKT) (F. Fukunaga and W. Koontz, 1970) is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed kernel Fukunaga-Koontz transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
Authors: Li-Jia Li 0001, Gang Wang, Li Fei-Fei 0001
Venue: CVPR
Year: 2007

A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to state-of-the-art algorithms. Dataset collection is, however, a tedious and time-consuming task. This paper presents a novel automatic dataset collecting and model learning approach that uses object recognition techniques in an incremental method. The goal of this work is to use the tremendous resources of the web to learn robust object category models in order to detect and search for objects in real-world cluttered scenes. It mimics the human learning process of iteratively accumulating model knowledge and image examples. We adapt a non-parametric graphical model and propose an incremental learning framework. Our algorithm is capable of automatically collecting much larger object category datasets for 22 randomly selected classes from the Caltech 101 dataset. Furthermore, we offer not only more images in each object category dataset, but also a robust object model and meaningful image annotation. Our experiments show that OPTIMOL is capable of collecting image datasets that are superior to Caltech 101 and LabelMe.
Precise Registration of 3D Models To Images by Swarming Particles
Authors: Joerg Liebelt, Klaus Schertler
Venue: CVPR
Year: 2007

The precise alignment of a 3D model to 2D sensor images to recover the pose of an object in a scene is an important topic in computer vision. In this work, we outline a registration scheme to align arbitrary standard 3D models to optical and synthetic aperture radar (SAR) images in order to recover the full 6 degrees of freedom of the object. We propose a novel similarity measure which combines perspective contour matching and an appearance-based Mutual information (MI) measure. Unlike previous work, the resulting similarity measure is optimized using an evolutionary particle swarming strategy, parallelized to exploit the hardware acceleration potential of current generation graphics processors (GPUs). The performance of our registration scheme is systematically evaluated on an object tracking task using synthetic as well as real input images. We show that our approach leads to precise registration results, even for significant image noise, small object dimensions and partial occlusion where other methods would fail.
Local Ensemble Kernel Learning for Object Category Recognition
Authors: Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
Venue: CVPR
Year: 2007

This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass feature variations, using only a single unified kernel-based classifier may not satisfactorily solve the problem. Our approach is to carry out the recognition task with adaptive ensemble kernel machines, each of which is derived from proper localization and regularization. Specifically, for each training sample, we learn a distinct ensemble kernel constructed in a way to give good classification performance for data falling within the corresponding neighborhood. We achieve this effect by aligning each ensemble kernel with a locally adapted target kernel, followed by smoothing out the discrepancies among kernels of nearby data. Our experimental results on various image databases manifest that the technique to optimize local ensemble kernels is effective and consistent for object recognition.
Map-Enhanced UAV Image Sequence Registration and Synchronization of Multiple Image Sequences
Authors: Yuping Lin, Gérard G. Medioni
Venue: CVPR
Year: 2007

Registering consecutive images from an airborne sensor into a mosaic is an essential tool for image analysts. Strictly local methods tend to accumulate errors, resulting in distortion. We propose here to use a reference image (such as a high resolution map image) to overcome this limitation. In our approach, we register a frame in an image sequence to the map using both frame-to-frame registration and frame-to-map registration iteratively. In frame-to-frame registration, a frame is registered to its previous frame. With its previous frame been registered to the map in the previous iteration, we can derive an estimated transformation from the frame to the map. In frame-to-map registration, we warp the frame to the map by this transformation to compensate for scale and rotation difference and then perform an area based matching using mutual information to find correspondences between this warped frame and the map. These correspondences together with the correspondences in previous frames could be regarded as correspondences between the partial local mosaic and the map. By registering the partial local mosaic to the map, we derive a transformation from the frame to the map. With this two-step registration, the errors between each consecutive frames are not accumulated. We then extend our approach to synchronize multiple image sequences by tracking moving objects in each image sequence, and aligning the frames based on the object's coordinates in the reference image.
A Topic-Motion Model for Unsupervised Video Object Discovery
Authors: David Liu 0001, Tsuhan Chen
Venue: CVPR
Year: 2007

The bag-of-words representation has attracted a lot of attention recently in the field of object recognition. Based on the bag-of-words representation, topic models such as probabilistic latent semantic analysis (PLSA) have been applied to unsupervised object discovery in still images. In this paper, we extend topic models from still images to motion videos with the integration of a temporal model. We propose a novel spatial-temporal framework that uses topic models for appearance modeling, and the probabilistic data association (PDA) filter for motion modeling. The spatial and temporal models are tightly integrated so that motion ambiguities can be resolved by appearance, and appearance ambiguities can be resolved by motion. We show promising results that cannot be achieved by appearance or motion modeling alone.
Visual Curvature
Authors: Hairong Liu, Longin Jan Latecki, Wenyu Liu 0001, Xiang Bai
Venue: CVPR
Year: 2007

In this paper, we propose a new definition of curvature, called visual curvature. It is based on statistics of the extreme points of the height functions computed over all directions. By gradually ignoring relatively small heights, a single parameter multi-scale curvature is obtained. It does not modify the original contour and the scale parameter has an obvious geometric meaning. The theoretical properties and the experiments presented demonstrate that multi-scale visual curvature is stable, even in the presence of significant noise. In particular, it can deal with contours with significant gaps. We also show a relation between multi-scale visual curvature and convexity of simple closed curves. To our best knowledge, the proposed definition of visual curvature is the first ever that applies to regular curves as defined in differential geometry as well as to turn angles of polygonal curves. Moreover, it yields stable curvature estimates of curves in digital images even under sever distortions.
Projector-Camera Guided Fast Environment Restoration of a Biofeedback System for Rehabilitation
Authors: Yufei Liu, Gang Qian
Venue: CVPR
Year: 2007

This paper deals with the issue of fast and accurate physical environment restoration in a real time biofeedback system for stroke patient rehabilitation. The biofeedback system provides an interactive multimodal environment for patients to practice functional therapeutic reaching and grasping tasks, while receiving different types of feedback which indicate measures of performances and information for subsequent movements. The development of such a system will reduce rehabilitation time, promote more extensive recovery and alleviate rehabilitation monotony.
Learning to Detect A Salient Object
Authors: Tie Liu, Jian Sun 0001, Nanning Zheng 0001, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2007

We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper.
Online Appearance Model Learning for Video-Based Face Recognition
Authors: Liang Liu, Yunhong Wang 0001, Tieniu Tan
Venue: CVPR
Year: 2007

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper. First, we propose an algorithm which can learn appearance models online without using a pre-trained model. Second, we propose a method for eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.
Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching
Authors: Fengjun Lv, Ramakant Nevatia
Venue: CVPR
Year: 2007

3D human pose recovery is considered as a fundamental step in view-invariant human action recognition. However, inferring 3D poses from a single view usually is slow due to the large number of parameters that need to be estimated and recovered poses are often ambiguous due to the perspective projection. We present an approach that does not explicitly infer 3D pose at each frame. Instead, from existing action models we search for a series of actions that best match the input sequence. In our approach, each action is modeled as a series of synthetic 2D human poses rendered from a wide range of viewpoints. The constraints on transition of the synthetic poses is represented by a graph model called Action Net. Given the input, silhouette matching between the input frames and the key poses is performed first using an enhanced Pyramid Match Kernel algorithm. The best matched sequence of actions is then tracked using the Viterbi algorithm. We demonstrate this approach on a challenging video sets consisting of 15 complex action classes.
A Graph Cut Approach to Image Segmentation in Tensor Space
Authors: James G. Malcolm, Yogesh Rathi, Allen R. Tannenbaum
Venue: CVPR
Year: 2007

This paper proposes a novel method to apply the standard graph cut technique to segmenting multimodal tensor valued images. The Riemannian nature of the tensor space is explicitly taken into account by first mapping the data to a Euclidean space where non-parametric kernel density estimates of the regional distributions may be calculated from user initialized regions. These distributions are then used as regional priors in calculating graph edge weights. Hence this approach utilizes the true variation of the tensor data by respecting its Riemannian structure in calculating distances when forming probability distributions. Further, the non-parametric model generalizes to arbitrary tensor distribution unlike the Gaussian assumption made in previous works. Casting the segmentation problem in a graph cut framework yields a segmentation robust with respect to initialization on the data tested.
Combining Static Classifiers and Class Syntax Models for Logical Entity Recognition in Scanned Historical Documents
Authors: Song Mao, Praveer Mansukhani, George R. Thoma
Venue: CVPR
Year: 2007

Class syntax can be used to 1) model temporal or locational evolvement of class labels of feature observation sequences, 2) correct classification errors of static classifiers if feature observations from different classes overlap in feature space, and 3) eliminate redundant features whose discriminative information is already represented in the class syntax. In this paper, we describe a novel method that combines static classifiers with class syntax models for supervised feature subset selection and classification in unified algorithms. Posterior class probabilities given feature observations are first estimated from the output of static classifiers, and then integrated into a parsing algorithm to find an optimal class label sequence for the given feature observation sequence. Finally, both static classifiers and class syntax models are used to search for an optimal subset of features. An optimal feature subset, associated static classifiers, and class syntax models are all learned from training data. We apply this method to logical entity recognition in scanned historical U.S. Food and Drug Administration (FDA) documents containing court case Notices of Judgments (NJs) of different layout styles, and show that the use of class syntax models not only corrects most classification errors of static classifiers, but also significantly reduces the dimensionality of feature observations with negligible impact on classification performance.
A Probabilistic Intensity Similarity Measure based on Noise Distributions
Authors: Yasuyuki Matsushita, Stephen Lin 0001
Venue: CVPR
Year: 2007

We derive a probabilistic similarity measure between two observed image intensities that is based on the noise properties of the camera. In many vision algorithms, the effect of camera noise is either neglected or reduced in a preprocessing stage. However, noise reduction cannot be performed with high accuracy due to lack of knowledge about the true intensity signal. Our similarity metric specifically represents the likelihood that two intensity observations correspond to the same unknown noise-free scene radiance. By directly accounting for noise in the evaluation of similarity, the proposed measure makes noise reduction unnecessary and enhances many vision algorithms that involve matching of image intensities. Real-world experiments demonstrate the effectiveness of the proposed similarity measure in comparison to the standard L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm.
Multi-Use Light Engine - Fast Projection
Authors: Ian McDowall
Venue: CVPR
Year: 2007

This demonstration shows a display device delivering thousands of binary frames per second. Binary images are delivered over a standard DVI interface and can be created on the fly in real time on an nVIDIA graphics Card. The images are synchronous to the input and triggers for LED illumination, camera shutters, or motion control can be created. This system can be configured into a projector of into other forms.