OpenRaiser/Intern-Atlas huggingface dataset:
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
Authors: Changbo Yang, Ming Dong 0001, Jing Hua 0001
Venue: CVPR
Year: 2006

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.
Gesture Recognition using Hidden Markov Models from Fragmented Observations
Authors: Ruiduo Yang, Sudeep Sarkar
Venue: CVPR
Year: 2006

We consider the problem of computing the likelihood of a gesture from regular, unaided video sequences, without relying on perfect segmentation of the scene. Instead of requiring that low-and mid-level processes produce near-perfect segmentation of relevant body parts such as hands, we take into account that such processes can only produce uncertain information. The hands can only be detected as fragmented regions along with clutter. To address this problem, we propose an extension of the HMM formalism, which we call the frag-HMM, to allow for reasoning based on fragmented observations, via the use of an intermediate grouping process. In this formulation, we do not match the frag- HMMto one observation sequence, but rather to a sequence of observation sets, where each observation set is a collection of groups of fragmented observations. Based on the developed model, we show how to perform three kinds of computations. The first one is to decide on the best observation group for each frame, given a sequence of observation groups for the past frames. This allows us to incrementally compute the best segmentation of the hand for each frame, given the model. The second one involves the computation of likelihood of a sequence, averaged over all possible states sequences and possible groupings. The third is the computation of the likelihood of a sequence, maximized over all possible state sequences and group sequences. This can give us the best possible groupings for each frame, as well. We demonstrate our ideas using a publicly available hand gesture dataset that spans different subjects, is against complex background, and involves hand occlusions. The recognition performance is within 2% of that obtained with manually segmented hands and about 10% better than that obtained with segmentations that use the prior knowledge of the hand color.
Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling
Authors: Qingxiong Yang, Liang Wang 0002, Ruigang Yang, Henrik Stewénius, David Nistér
Venue: CVPR
Year: 2006

In this paper, we formulate an algorithm for the stereo matching problem with careful handling of disparity, discontinuity and occlusion. The algorithm works with a global matching stereo model based on an energy- minimization framework. The global energy contains two terms, the data term and the smoothness term. The data term is first approximated by a color-weighted correlation, then refined in occluded and low-texture areas in a repeated application of a hierarchical loopy belief propagation algorithm. The experimental results are evaluated on the Middlebury data set, showing that our algorithm is the top performer.
Spatial Divide and Conquer with Motion Cues for Tracking through Clutter
Authors: Zhaozheng Yin, Robert T. Collins
Venue: CVPR
Year: 2006

Tracking can be considered a two-class classification problem between the foreground object and its surrounding background. Feature selection to better discriminate object from background is thus a critical step to ensure tracking robustness. In this paper, a spatial divide and conquer approach is used to subdivide foreground and background into smaller regions, with different features being selected to distinguish between different pairs of object and background regions. Temporal cues are incorporated into the process using foreground motion prediction and motion segmentation. Appearance weight maps tailored to each spatial region are merged and combined with the motion information to form a joint weight image suitable for mean-shift tracking. Examples are presented to illustrate that divide and conquer feature selection combined with motion cues handles spatial background clutter and camouflage well.
Stereo Matching with Symmetric Cost Functions
Authors: Kuk-Jin Yoon, In-So Kweon
Venue: CVPR
Year: 2006

Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global methods mainly focus on how to minimize conventional cost functions efficiently, although it is more important to define cost functions well to improve performance. In this paper, we propose new symmetric cost functions for global stereo methods. We first present a symmetric data cost function for the likelihood and then propose a symmetric discontinuity cost function for the prior in the MRF model for stereo. In defining cost function, both the reference image and the target image are taken into account to improve performance without modeling half-occluded pixels explicitly and without using color segmentation. The performance improvement of stereo matching due to the proposed symmetric cost functions is verified by applying the proposed symmetric cost functions to the belief propagation (BP) based stereo method. Experimental results for standard testbed images show that the performance of the BP based stereo method is greatly improved by the proposed symmetric cost functions.
SDG Cut: 3D Reconstruction of Non-lambertian Objects Using Graph Cuts on Surface Distance Grid
Authors: Tian-Li Yu 0002, Narendra Ahuja, Wei-Chao Chen
Venue: CVPR
Year: 2006

We show that the approaches to 3D reconstruction that use volumetric graph cuts to minimize a cost function over the object surface have two types of biases, the minimal surface bias and the discretization bias. These biases make it difficult to recover surface extrusions and other details, especially when a non-lambertian photo-consistency measure is used. To reduce these biases, we propose a new iterative graph cuts based algorithm that operates on the Surface Distance Grid (SDG), which is a special discretization of the 3Dspace, constructed using a signed distance transform of the current surface estimate. It can be shown that SDG significantly reduces the minimal surface bias, and transforms the discretization bias into a controllable degree of surface smoothness. Experiments on 3D reconstruction of non-lambertian objects confirm the effectiveness of our algorithm over previous methods.
Toward Robust Distance Metric Analysis for Similarity Estimation
Authors: Jie Yu 0001, Qi Tian 0001, Jaume Amores, Nicu Sebe
Venue: CVPR
Year: 2006

In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.
Recursive Recovery of Position and Orientation from Stereo Image Sequences without Three-Dimensional Structures
Authors: Ying Kin Yu, Kin-hong Wong, Siu-Hang Or, Michael Ming Yuen Chang
Venue: CVPR
Year: 2006

Traditional vision-based 3-D motion estimation algorithms for robots require given or calculated 3-D models while the motion is being tracked. We propose a high-speed extended-Kalman-filter-based approach that recovers position and orientation from stereo image sequences without prior knowledge as well as the procedure for the reconstruction of 3-D structures. Empowered by the use of the trifocal tensor, the computation step of 3-D models can be eliminated. The algorithm is thus more flexible and can be applied to a wide range of domains. The twist motion model is also adopted to parameterize the 3-D motion such that the motion representation in the proposed algorithm is robust and minimal. As the number of parameters to be estimated is reduced, our algorithm is more efficient, stable and accurate compared to traditional approaches. The proposed method has been verified using a real image sequence with ground truth.
A Generative-Discriminative Hybrid Method for Multi-View Object Detection
Authors: DongQing Zhang, Shih-Fu Chang
Venue: CVPR
Year: 2006

We present a novel discriminative-generative hybrid approach in this paper, with emphasis on application in multiview object detection. Our method includes a novel generative model called Random Attributed Relational Graph (RARG) which is able to capture the structural and appearance characteristics of parts extracted from objects. We develop new variational learning methods to compute the approximation of the detection likelihood ratio function. The variaitonal likelihood ratio function can be shown to be a linear combination of the individual generative classifiers defined at nodes and edges of the RARG. Such insight inspires us to replace the generative classifiers at nodes and edges with discriminative classifiers, such as support vector machines, to further improve the detection performance. Our experiments have shown the robustness of the hybrid approach - the combined detection method incorporating the SVM-based discriminative classifiers yields superior detection performances compared to prior works in multiview object detection.
Local Steerable Phase (LSP) Feature for Face Representation and Recognition
Authors: Xiaoxun Zhang, Yunde Jia
Venue: CVPR
Year: 2006

In this paper, we propose a novel local steerable phase (LSP) feature extracted from the face image using steerable filter for face representation and recognition. Steerable filter is a kind of oriented filters. It is rotated very efficiently by taking a suitable linear combination of basis filters and allows adaptive control over phase as well as orientation. Phase information provided by steerable filter is locally stable with respect to scale, noise and brightness changes. Furthermore, steerable filter is implemented within a Gaussian pyramid to make use of discriminative power in the scale-space of face images. Each face is represented as multiple "steerablefaces" of different scales and orientations. With simple down-sampling, all the steerablefaces are concatenated to an augmented feature vector for evaluating similarity between face images. A nearest-neighbor classifier based on local weighted phase-correlation is used for final decision rule. Experimental results on FERET and XM2VTS databases demonstrate the performance of the proposed method.
Robust Tracking and Stereo Matching under Variable Illumination
Authors: Jingdan Zhang, Leonard McMillan, Jingyi Yu
Venue: CVPR
Year: 2006

Illumination inconsistencies cause serious problems for classical computer vision applications such as tracking and stereo matching. We present a new approach to model illumination variations using an Illumination Ratio Map (IRM). An IRM computes the intensity ratio of corresponding points in an image pair. We formulate IRM recovery as a Markov network, which assumes spatially varying illumination changes can be modeled as a locally smooth function with boundaries. We show that the IRM Markov network can be easily incorporated into low-level vision problems, such as tracking and stereo matching, by integrating IRM estimation with the optical flow field/disparity map solution process. This leads to a unified Markov network. We develop an iterative optimization algorithm based on Belief Propagation to efficiently recover the illumination ratio map and the optical field/disparity map at the same time. Experiments demonstrate that our methods are robust and reliable.
When Fisher meets Fukunaga-Koontz: A New Look at Linear Discriminants
Authors: Sheng Zhang 0007, Terence Sim
Venue: CVPR
Year: 2006

The Fisher Linear Discriminant (FLD) is commonly used in pattern recognition. It finds a linear subspace that maximally separates class patterns according to Fisher’s Criterion. Several methods of computing the FLD have been proposed in the literature, most of which require the calculation of the so-called scatter matrices. In this paper, we bring a fresh perspective to FLD via the Fukunaga-Koontz Transform (FKT). We do this by decomposing the whole data space into four subspaces, and show where Fisher’s Criterion is maximally satisfied. We prove the relationship between FLD and FKT analytically, and propose a method of computing the most discriminative subspace. This method is based on the QR decomposition, which works even when the scatter matrices are singular, or too large to be formed. Our method is general and may be applied to different pattern recognition problems. We validate our method by experimenting on synthetic and real data.
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
Authors: Qilong Zhang, Richard Souvenir, Robert Pless
Venue: CVPR
Year: 2006

We develop theory and algorithms to incorporate image manifold constraints in a level set segmentation algorithm. This provides a framework to simultaneously segment every image of data sets that vary due to two degrees of freedom - such as cardiopulmonary MR images which deform due to patient breathing and heartbeats. We derive two formulations: a 4D level set which loosely couples the level set function between neighbors in the 2D image manifold and a multilayer level set function which uses different levels of the level set function to represent shapes that shrink or grow. We characterize the set of shape manifolds that the multilayer level set function can represent, and derive the evolution equations for both frameworks. We offer results of segmenting the left ventricle in cardiopulmonary MRI; by automatically discovering the 2D manifold structure of the image set then simultaneously segmenting every frame. Both extensions improve on frame-by-frame approaches, and a comparison of the results offers insight into their strengths and weaknesses.
Motion Estimation from Spheres
Authors: Guoqiang Zhang 0003, Kwan-Yee Kenneth Wong
Venue: CVPR
Year: 2006

This paper addresses the problem of recovering epipolar geometry from spheres. Previous works have exploited epipolar tangencies induced by frontier points on the spheres for motion recovery. It will be shown in this paper that besides epipolar tangencies, N^2 point features can be extracted from the apparent contours of the N spheres whenN \gt 2. An algorithm for recovering the fundamental matrices from such point features and the epipolar tangencies from 3 or more spheres is developed, with the point features providing a homography over the view pairs and the epipolar tangencies determining the epipoles. In general, there will be two solutions to the locations of the epipoles. One of the solutions corresponds to the true camera configuration, while the other corresponds to a mirrored configuration. Several methods are proposed to select the right solution. Experiments on using 3 and 4 spheres demonstrate that our algorithm can be carried out easily and can achieve a high precision.
Multiscale Nonlinear Diffusion and Shock Filter for Ultrasound Image Enhancement
Authors: Fan Zhang, Yang Mo Yoo, Yongmin Kim 0001, Lichen Zhang, Liang Mong Koh
Venue: CVPR
Year: 2006

A new noise reduction and edge enhancement method, i.e., Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), is proposed for medical ultrasound imaging. In the proposed LPNDSF, a coupled nonlinear diffusion and shock filter process is applied in Laplacian pyramid domain of an image, to remove speckle and enhance edges simultaneously. The performance of the proposed method was evaluated on a phantom and a real ultrasound image. In the phantom study, we obtained an average gain of 0.55 and 1.11 in contrast-to-noise ratio compared to the speckle reducing anisotropic diffusion (SRAD) and nonlinear coherent diffusion (NCD), respectively. Also, the proposed LPNDSF showed clearer boundaries on the phantom and the real ultrasound image. These preliminary results indicate that the proposed LPNDSF can effectively reduce speckle noise while enhancing image edges for retaining subtle features.
Single-Image Vignetting Correction
Authors: Yuanjie Zheng, Stephen Lin 0001, Sing Bing Kang
Venue: CVPR
Year: 2006

In this paper, we propose a method for determining the vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for vignetting estimation. Within each image region, our method capitalizes on frequency characteristics and physical properties of vignetting to distinguish it from other sources of intensity variation. The vignetting data acquired from regions are weighted according to a presented reliability measure to promote robustness in estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images.
Surface Reconstruction of Bone from X-ray Images and Point Distribution Model Incorporating a Novel Method for 2D-3D Correspondence
Authors: Guoyan Zheng, Lutz-Peter Nolte
Venue: CVPR
Year: 2006

Surface reconstruction of bone from a few X-ray images and point distribution model (PDM) is discussed. We present a robust approach combining regularized morphing and shape deformation, and show its application to surface reconstruction of proximal femur. The robustness of the presented approach relies on the development of a novel method to establish correspondence between the X-ray images and the model estimated from the PDM. The correspondence is based on an iterative non-rigid twodimensional (2D) point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator (SIN-MO) and 2D thin-plate splines (2D-TPS) based deformation to find a fraction of best matched 2D point pairs between edge points detected from the X-ray images and projections of the points on the apparent contours extracted from the estimated model. The advantages of this novel method include robustness with respect to outliers, and automatic exclusion of cross matching, which is an important property for preservation of topology. Initial quantitative and qualitative evaluation results are given which indicate the validity of our approach.
BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation
Authors: Shaohua Kevin Zhou, Bogdan Georgescu, Dorin Comaniciu, Jie Shao 0007
Venue: CVPR
Year: 2006

Motion estimation for applications where appearance undergoes complex changes is challenging due to lack of an appropriate similarity function. In this paper, we propose to learn a discriminative similarity function based on an annotated database that exemplifies the appearance variations. We invoke the LogitBoost algorithm to selectively combine weak learners into one strong similarity function. The weak learners based on local rectangle features are constructed as nonparametric 2D piecewise constant functions, using the feature responses from both images, to strengthen the modeling power and accommodate fast evaluation. Because the negatives possess a location parameter measuring their closeness to the positives, we present a locationsensitive cascade training procedure, which bootstraps negatives for later stages of the cascade from the regions closer to the positives. This allows viewing a large number of negatives and steering the training process to yield lower training and test errors. In experiments of estimating the motion for the endocardial wall of the left ventricle in echocardiography, we compare the learned similarity function with conventional ones and obtain improved performances. We also contrast the proposed method with a learning-based detection algorithm to demonstrate the importance of temporal information in motion estimation. Finally, we insert the learned similarity function into a simple contour tracking algorithm and find that it reduces drifting.
Binocular Stereo Dense Matching in the Presence of Specular Reflections
Authors: Wei Zhou, Chandra Kambhamettu
Venue: CVPR
Year: 2006

Traditional stereo correspondence algorithms rely heavily on the Lambertian model of diffuse reflectance. While this diffuse assumption is generally valid for much of an image, processing of regions that contain specular reflections can result in severe matching errors. In this paper, We address the problem of binocular stereo dense matching in the presence of specular reflections by introducing a novel correspondence measurement which is robust to the specular reflections. Combining our novel correspondence measurement with various local or global optimatization methods, accurate depth can be estimated for both diffuse and specular regions. Unlike the previous works which seek to eliminate or avoid specular reflections using image preprocessing or multibaseline stereo, our approach works in its presence. Our approach is general and fits well in various stereo frameworks. Experiments with both synthetic and real images demonstrate the effectiveness and robustness of our approach.
Image-Based Multiclass Boosting and Echocardiographic View Classification
Authors: Shaohua Kevin Zhou, Jin Hyeong Park, Bogdan Georgescu, Dorin Comaniciu, Costas Simopoulos, Joanne Otsuki
Venue: CVPR
Year: 2006

We tackle the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection. As a solution, we present an imagebased multiclass boosting procedure. In contrast with conventional approaches for multiple object detection that train multiple binary classifiers, one per object, we learn only one multiclass classifier using the LogitBoosting algorithm. To utilize the fact that, in the midst of boosting, one class is fully separated from the remaining classes, we propose to learn a tree structure that focuses on the remaining classes to improve learning efficiency. Further, we accommodate the large number of background images using a cascade of boosted multiclass classifiers, which is able to simultaneously detect and classify multiple objects while rejecting the background class quickly. Our experiments on echocardiographic view classification demonstrate promising performances of image-based multiclass boosting.