OpenRaiser/Intern-Atlas huggingface dataset:
Statistical Shape Analysis of Multi-Object Complexes
Authors: Kevin Gorczowski, Martin Styner, Ja-Yeon Jeong, J. S. Marron, Joseph Piven, Heather Cody Hazlett, Stephen M. Pizer, Guido Gerig
Venue: CVPR
Year: 2007

An important goal of statistical shape analysis is the discrimination between populations of objects, exploring group differences in morphology not explained by standard volumetric analysis. Certain applications additionally require analysis of objects in their embedding context by joint statistical analysis of sets of interrelated objects. In this paper, we present a framework for discriminant analysis of populations of 3-D multi-object sets. In view of the driving medical applications, a skeletal object parametrization of shape is chosen since it naturally encodes thickening, bending and twisting. In a multi-object setting, we not only consider a joint analysis of sets of shapes but also must take into account differences in pose. Statistics on features of medial descriptions and pose parameters, which include rotational frames and distances, uses a Riemannian symmetric space instead of the standard Euclidean metric. Our choice of discriminant method is the distance weighted discriminant (DWD) because of its generalization ability in high dimensional, low sample size settings. Joint analysis of 10 subcortical brain structures in a pediatric autism study demonstrates that multi-object analysis of shape results in a better group discrimination than pose, and that the combination of pose and shape performs better than shape alone. Finally, given a discriminating axis of shape and pose, we can visualize the differences between the populations.
Learning Features for Tracking
Authors: Michael Grabner, Helmut Grabner, Horst Bischof
Venue: CVPR
Year: 2007

We treat tracking as a matching problem of detected key-points between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to incorporate background information. Contrary to existing approaches, we are able to start tracking of the object from scratch requiring no off-line training phase before tracking. The tracker is initialized by a region of interest in the first frame. Afterwards an on-line boosting technique is used for learning descriptions of detected keypoints lying within the region of interest. New frames provide new samples for updating the classifiers which increases their stability. A simple mechanism incorporates temporal information for selecting stable features. In order to ensure correct updates a verification step based on estimating homographies using RANSAC is performed. The approach can be used for real-time applications since on-line updating and evaluating classifiers can be done efficiently.
Eigenboosting: Combining Discriminative and Generative Information
Authors: Helmut Grabner, Peter M. Roth, Horst Bischof
Venue: CVPR
Year: 2007

A major shortcoming of discriminative recognition and detection methods is their noise sensitivity, both during training and recognition. This may lead to very sensitive and brittle recognition systems focusing on irrelevant information. This paper proposes a method that selects generative and discriminative features. In particular, we boost classical Haar-like features and use the same features to approximate a generative model (i.e., eigenimages). A modified error function for boosting ensures that only features are selected that show a good discrimination and reconstruction. This allows a robust feature selection using boosting. Thus, we can handle problems where discriminant classifiers fail while still retaining the discriminative power. Our experiments show that we can significantly improve the recognition performance when learning from noisy data. Moreover, the feature type used allows efficient recognition and reconstruction.
The Hierarchical Isometric Self-Organizing Map for Manifold Representation
Authors: Haiying Guan, Matthew Turk 0001
Venue: CVPR
Year: 2007

We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimensional input data in a low dimensional space. The main contribution of our algorithm is threefold. First, we modify the previous ISOSOM algorithm by a local linear interpolation (LLl) technique, which maps the data samples from low dimensional space back to high dimensional space and makes the complete mapping pseudo-invertible. The modified-ISOSOM (M-ISOSOM) follows the global geometric structure of the data, and also preserves local geometric relations to reduce the nonlinear mapping distortion and make the learning more accurate. Second, we propose the H-ISOSOM algorithm for the computational complexity problem of Isomap, SOM and LLI and the nonlinear complexity problem of the highly twisted manifold. H-ISOSOM learns an organized structure of a non-convex, large scale manifold and represents it by a set of hierarchical organized maps. The hierarchical structure follows a coarse-to-fine strategy. According to the coarse global structure, it "unfolds " the manifold at the coarse level and decomposes the sample data into small patches, then iteratively learns the nonlinearity of each patch in finer levels. The algorithm simultaneously reorganizes and clusters the data samples in a low dimensional space to obtain the concise representation. Third, we give quantitative comparisons of the proposed method with similar methods on standard data sets. Finally, we apply H-ISOSOM to the problem of appearance-based hand pose estimation. Encouraging experimental results validate the effectiveness and efficiency of H-ISOSOM.
Patch-based Image Correlation with Rapid Filtering
Authors: Guodong Guo, Charles R. Dyer
Venue: CVPR
Year: 2007

This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.
3D Face Recognition Founded on the Structural Diversity of Human Faces
Authors: Shalini Gupta, J. K. Aggarwal, Mia K. Markey, Alan C. Bovik
Venue: CVPR
Year: 2007

We present a systematic procedure for selecting facial fiducial points associated with diverse structural characteristics of a human face. We identify such characteristics from the existing literature on anthropometric facial proportions. We also present three dimensional (3D) face recognition algorithms, which employ Euclidean/geodesic distances between these anthropometric fiducial points as features along with linear discriminant analysis classifiers. Furthermore, we show that in our algorithms, when anthropometric distances are replaced by distances between arbitrary regularly spaced facial points, their performances decrease substantially. This demonstrates that incorporating domain specific knowledge about the structural diversity of human faces significantly improves the performance of 3D human face recognition algorithms.
A Surface-Growing Approach to Multi-View Stereo Reconstruction
Authors: Martin Habbecke, Leif Kobbelt
Venue: CVPR
Year: 2007

We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. The central idea of our method is to combine the topological flexibility of a point-based geometry representation with the robust reconstruction properties of scene-aligned planar primitives. This can be achieved by approximating the shape with a set of surface elements (surfels) in the form of planar disks which are independently fitted such that their footprint in the input images matches. Instead of using an artificial energy functional to promote the smoothness of the recovered surface during fitting, we use the smoothness assumption only to initialize planar primitives and to check the feasibility of the fitting result. After an initial disk has been found, the recovered region is iteratively expanded by growing further disks in tangent direction. The expansion stops when a disk rotates by more than a given threshold during the fitting step. A global sampling strategy guarantees that eventually the whole surface is covered. Our technique does not depend on a shape prior or silhouette information for the initialization and it can automatically and simultaneously recover the geometry, topology, and visibility information which makes it superior to other state-of-the-art techniques. We demonstrate with several high-quality reconstruction examples that our algorithm performs highly robustly and is tolerant to a wide range of image capture modalities.
Inter-Reflection Compensation for Immersive Projection Display
Authors: Hitoshi Habe, Nobuo Saeki, Takashi Matsuyama
Venue: CVPR
Year: 2007

This paper proposes an effective method for compensating inter-reflection in immersive projection displays (IPDs). Because IPDs project images onto a screen, which surrounds a viewer, we have perform out both geometric and photometric corrections. Our method compensates inter-reflection on the screen. It requires no special device, and approximates both diffuse and specular reflections on the screen using block-based photometric calibration.
Sparse Kernels for Bayes Optimal Discriminant Analysis
Authors: Onur C. Hamsici, Aleix M. Martínez
Venue: CVPR
Year: 2007

Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research - especially in the C class classification problem. The most popular approach is linear DA (LDA), which provides the C - 1-dimensional Bayes optimal solution, but only when all the class covariance matrices are identical. This is rarely the case in practice. To alleviate this restriction, Kernel LDA (KLDA) has been proposed. In this approach, we first (intrinsically) map the original nonlinear problem to a linear one and then use LDA to find the C - 1-dimensional Bayes optimal subspace. However, the use of KLDA is hampered by its computational cost, given by the number of training samples available and by the limitedness of LDA in providing a C - 1-dimensional solution space. In this paper, we first extend the definition of LDA to provide subspace of q < C - 1 dimensions where the Bayes error is minimized. Then, to reduce the computational burden of the derived solution, we define a sparse kernel representation, which is able to automatically select the most appropriate sample feature vectors that represent the kernel. We demonstrate the superiority of the proposed approach on several standard datasets. Comparisons are drawn with a large number of known DA algorithms.
An Adaptive Focal Connectivity Algorithm for Multifocus Fusion
Authors: Harishwaran Hariharan, Andreas F. Koschan, Mongi A. Abidi
Venue: CVPR
Year: 2007

Multifocus fusion is the process of fusing focal information from a set of input images into one all-in-focus image. Here, a versatile multifocus fusion algorithm is presented for application-independent fusion. A focally connected region is a region or a set of regions in an input image that falls under the depth of field of the imaging system. Such regions are segmented adaptively under the predicate of focal connectivity and fused by partition synthesis. The fused image has information from all focal planes, while maintaining the visual verisimilitude of the scene. In order to validate the fusion performance of our method, we have compared our results with those of tiling and multiscale fusion techniques. In addition to performing a seamless fusion of the focally connected regions, our method out performs the competing methods regarding overall sharpness in all our experiments. Several illustrative examples of multifocus fusion are shown and objective comparisons are provided.
Regularized Mixed Dimensionality and Density Learning in Computer Vision
Authors: Gloria Haro, Gregory Randall, Guillermo Sapiro
Venue: CVPR
Year: 2007

A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.
Simultaneous Depth Reconstruction and Restoration of Noisy Stereo Images using Non-local Pixel Distribution
Authors: Yong Seok Heo, Kyoung Mu Lee, Sang Uk Lee
Venue: CVPR
Year: 2007

In this paper, we propose a new algorithm that solves both the stereo matching and the image denoising problem simultaneously for a pair of noisy stereo images. Most stereo algorithms employ L1 or L2 intensity error-based data costs in the MAP-MRF framework by assuming the naive intensity-constancy. These data costs make typical stereo algorithms suffer from the effect of noise severely. In this study, a new robust stereo algorithm to noise is presented that performs the stereo matching and the image denoising simultaneously. In our approach, we redefine the data cost by two terms. The first term is the restored intensity difference, instead of the observed intensity difference. The second term is the non-local pixel distribution dissimilarity around the matched pixels. We adopted the NL-means (Non Local-means) algorithm for restoring the intensity value as a function of disparity. And a pixel distribution dissimilarity is calculated by using PMHD (Perceptually Modified Hausdorff Distance). The restored intensity values in each image are determined by inferring optimal disparity map at the same time. Experimental results show that the proposed algorithm is more robust and accurate than other conventional algorithms in both stereo matching and denoising.
Probabilistic visibility for multi-view stereo
Authors: Carlos Hernández 0002, George Vogiatzis, Roberto Cipolla
Venue: CVPR
Year: 2007

We present a new formulation to multi-view stereo that treats the problem as probabilistic 3D segmentation. Previous work has used the stereo photo-consistency criterion as a detector of the boundary between the 3D scene and the surrounding empty space. Here we show how the same criterion can also provide a foreground/background model that can predict if a 3D location is inside or outside the scene. This model replaces the commonly used naive foreground model based on ballooning which is known to perform poorly in concavities. We demonstrate how the probabilistic visibility is linked to previous work on depth-map fusion and we present a multi-resolution graph-cut implementation using the new ballooning term that is very efficient both in terms of computation time and memory requirements.
A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features
Authors: Masayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, Ryusuke Miyamoto
Venue: CVPR
Year: 2007

Robust and rapid object detection is one of the great challenges in the field of computer vision. This paper proposes a hardware architecture suitable for object detection by Viola and Jones based on an AdaBoost learning algorithm with Haar-like features as weak classifiers. Our architecture realizes rapid and robust detection with two major features: hybrid parallel execution and an image scaling method. The first exploits the cascade structure of classifiers, in which classifiers located near the beginning of the cascade are used more frequently than subsequent classifiers. We assign more resources to the former classifiers to execute in parallel than subsequent classifiers. This dramatically improves the total processing speed without a great increase in circuit area. The second feature is a method of scaling input images instead of scaling classifiers. This increases the efficiency of hardware implementation while retaining a high detection rate. In addition we implement the proposed architecture on a Virtex-5 FPGA to show that it achieves real-time object detection at 30 frames per second on VGA video.
A New Performance Evaluation Method for Face Identification - Regression Analysis of Misidentification Risk
Authors: Wai Han Ho, Paul Andrew Watters
Venue: CVPR
Year: 2007

The performance of a face identification system varies with its enrollment size. However, most experiments evaluated the performance of algorithms at only one enrollment size with the rank-1 identification rate. The current practice does not demonstrate the usability of algorithms thoroughly. But the problem is, in order to measure identification performance at different sizes, experimenters have to repeat the evaluation with samples of those sizes, which is almost impossible when they are large. Approaches using the Binomial theorem with match and non-match scores have been proposed to estimate performance at different sizes, but as a separate process from the evaluation itself. This paper presents a new way of evaluating identification algorithms that allows the estimating and comparing of performance at different sizes, using the regression analysis of Misidentification Risk.
Robustness of the New Owner-Tester Approach for Face Identification Experiments
Authors: Wai Han Ho, Paul Andrew Watters, Dominic R. Verity
Venue: CVPR
Year: 2007

With the broad application of face identification, it is important that the performance estimated for an algorithm using a sample can be generalized to the population performance. We proposed using an Owner-Tester setup to replace the current approach for experiments on face identification (or other biometrics and pattern recognition systems). This paper looks into the robustness of the Owner-Tester setup in terms of goodness of fit and performance estimation using misidentification risk -the newly suggested performance evaluation metric. Testing results have indicated that the approach is robust in term of goodness of fit and performance estimation.
The ARL Multi-Modal Sensor: A research tool for target signature collection, algorithm validation, and emplacement studies
Authors: Jeff Houser, Lei Zong
Venue: CVPR
Year: 2007

The U.S. Army Research Laboratory (ARL) has a significant program involving the development of UGS (unattended ground sensors) that addresses a variety of military and government missions. ARL's program involves practically every aspect of sensor development including devices, detection and fusion algorithms, communications, and command and control. One element of the ARL UGS program involves the development of low cost sensing techniques for the urban environment and one embodiment of this effort is the multi-modal sensor (MMS). The program objectives of this effort were to develop a networked personnel detection sensor with the following major criteria: low cost in volume production, support for MOUT (military operations in urban terrain) missions, and employ non-imaging sensor diversity techniques. The MMS sensor was an early prototype intended to demonstrate that low cost sensing techniques were suitable for the urban environment and a viable alternative to higher cost and fidelity sensors for some applications. The MMS is used today as a demonstration system and a test bed for many facets of urban sensing. This chapter describe many aspects of the MMS design including: hardware, software, and communications. The detection algorithms was described including the collection of target signatures and validation of algorithm performance. Finally, MMS usage in a force protection application was described including issues encountered when integrating into a larger system.
Sensor noise modeling using the Skellam distribution: Application to the color edge detection
Authors: Youngbae Hwang, Jun-Sik Kim 0001, In-So Kweon
Venue: CVPR
Year: 2007

In this paper, we introduce the Skellam distribution as a sensor noise model for CCD or CMOS cameras. This is derived from the Poisson distribution of photons that determine the sensor response. We show that the Skellam distribution can be used to measure the intensity difference of pixels in the spatial domain, as well as in the temporal domain. In addition, we show that Skellam parameters are linearly related to the intensity of the pixels. This property means that the brighter pixels tolerate greater variation of intensity than the darker pixels. This enables us to decide automatically whether two pixels have different colors. We apply this modeling to detect the edges in color images. The resulting algorithm requires only a confidence interval for a hypothesis test, because it uses the distribution of image noise directly. More importantly, we demonstrate that without conventional Gaussian smoothing the noise model-based approach can automatically extract the fine details of image structures, such as edges and corners, independent of camera setting.
Searching Video for Complex Activities with Finite State Models
Authors: Nazli Ikizler, David A. Forsyth
Venue: CVPR
Year: 2007

We describe a method of representing human activities that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across space and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. We show results for a large range of queries applied to a collection of complex motion and activity. Our models of short time scale limb behaviour are built using labelled motion capture set. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by the changes of clothing.
Joint Priors for Variational Shape and Appearance Modeling
Authors: Jeremy D. Jackson, Anthony J. Yezzi, Stefano Soatto
Venue: CVPR
Year: 2007

We are interested in modeling the variability of different images of the same scene, or class of objects, obtained by changing the imaging conditions, for instance the viewpoint or the illumination. Understanding of such a variability is key to reconstruction of objects despite changes in their appearance (e.g. due to non-Lambertian reflection), or to recognizing classes of objects (e.g. cars), or individual objects seen from different vantage points. We propose a model that can account for changes in shape or viewpoint, appearance, and also occlusions of line of sight. We learn a prior model of each factor (shape, motion and appearance) from a collection of samples using principal component analysis, akin a generalization of "active appearance models" to dense domains affected by occlusions. The ultimate goal of this work is stereo reconstruction in 3D, but first we have developed the first stage in this approach by addressing the simpler case of 2D shape/radiance detection in single images. We illustrate our model on a collection of images of different cars and show how the learned prior can be used to improve segmentation and 3D stereo reconstruction.