OpenRaiser/Intern-Atlas huggingface dataset:
On the Small Sample Performance of Boosted Classifiers
Authors: Weiliang Li, Xiang Gao, Ying Zhu 0006, Visvanathan Ramesh, Terrance E. Boult
Venue: CVPR
Year: 2005

Boosting algorithms have been widely applied in the machine vision systems. Two fundamental issues that have to be solved in these systems are how much training data and how many Boosting rounds are needed to achieve a desired performance. We view the Boosting algorithm as a nonlinear estimation scheme that estimates a strong classifier from a given training sample set (that is generated by sampling a true unknown distribution), the weak classifiers, and the number of Boosting rounds T. The performance characterization of this estimator involves the derivation of the classification error statistics of the trained strong classifier as a function of the training set and the collection of the weak classifiers. Although the convergence and the error bounds for the training error and generalization error of the algorithms have been studied for several years, the estimated bounds are still loose bounds that are only meaningful for large training sets. With no effective tools for determining the error bounds, users are now collecting training samples with as much data as they can afford, with no good way to know if they are sufficient. In this paper, we characterize the classification error statistics of the trained strong classifier as a function of the true distributions of classes, the collection of the weak classifiers, and the size of the training set. We show that the statistics can be numerically computed and the results are more accurate than previous bounds in the literature. Theoretical results are verified through the simulations. Face detection is used as a case study to illustrate the application of the theory on real data.
Segmentation of Edge Preserving Gradient Vector Flow: An Approach Toward Automatically Initializing and Splitting of Snakes
Authors: Chunming Li, Jundong Liu, Martin D. Fox
Venue: CVPR
Year: 2005

Active contours or snakes have been extensively utilized in handling image segmentation and classification problems. In traditional active contour models, snake initialization is performed manually by users, and topological changes, such as splitting of the snake, can not be automatically handled. In this paper, we introduce a new method to solve the snake initialization and splitting problem, based on an area segmentation approach: the external force field is segmented first, and then the snake initialization and splitting can be automatically performed by using the segmented external force field. Such initialization and splitting produces multiple snakes, each of which is within the capture range associated to an object and evolved to the object boundary. The external force used in this paper is a gradient vector flow with an edge-preserving property (EPGVF), which can prevent the snakes from passing over weak boundaries. To segment the external force field, we represent it with a graph, and a graph-theory approach can be taken to determine the membership of each pixel. Experimental results establish the effectiveness of the proposed approach.
Nonparametric Subspace Analysis for Face Recognition
Authors: Zhifeng Li 0001, Wei Liu 0026, Dahua Lin, Xiaoou Tang
Venue: CVPR
Year: 2005

Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix, in which the sample distribution in each class is assumed to be normal distribution. So it tends to suffer in the case of non-normal distribution. In this paper a nonparametric scatter matrix is defined to replace the traditional parametric scatter matrix in order to overcome this problem. Two kinds of nonparametric subspace analysis (NSA): PNSA and NNSA are proposed for face recognition. The former is based on the principal space of intra-personal scatter matrix, while the latter is based on the null space. In addition, based on the complementary nature of PNSA and NNSA, we further develop a dual NSA-based classifier framework using Gabor images to further improve the recognition performance. Experiments achieve near perfect recognition accuracy (99.7%) on the XM2VTS database.
Object Detection Using 2D Spatial Ordering Constraints
Authors: Yan Li, Yanghai Tsin, Yakup Genc, Takeo Kanade
Venue: CVPR
Year: 2005

Object detection is challenging partly due to the limited discriminative power of local feature descriptors. We amend this limitation by incorporating spatial constraints among neighboring features. We propose a two-step algorithm. First, a feature together with its spatial neighbors forms a flexible feature template. Two feature templates can be compared more informatively than two individual features without knowing the 3D object model. A large portion of false matches can be excluded after the first step. In a second global matching step, object detection is formulated as a graph-matching problem. A model graph is constructed by applying Delaunay triangulation on the surviving features. The best matching graph in an input image is computed by finding the maximum a posterior (MAP) estimate of a binary Markov random field with triangular maximal clique. The optimization is solved by the max-product algorithm (a.k.a. belief propagation). Experiments on both rigid and non-rigid objects demonstrate the generality and efficacy of the proposed methods.
Object Detection Using 2D Spatial Ordering Constraints
Authors: Yan Li, Yanghai Tsin, Yakup Genc, Takeo Kanade
Venue: CVPR
Year: 2005

Object detection is challenging partly due to the limited discriminative power of local feature descriptors. We propose a two-step algorithm. First, a feature together with its spatial neighbors forms a flexible feature template. Two feature templates can be compared more informatively than two individual features without knowing the 3D object model. A large portion of false matches can be excluded after the first step. In a second global matching step, object detection is formulated as a graph-matching problem. A model graph is constructed by applying Delaunay triangulation on the surviving features. The best matching graph in an input image is computed by finding the maximum a posterior (MAP) estimate of a binary Markov random field with triangular maximal clique. The optimization is solved by the max-product algorithm (a.k.a. belief propagation). Experiments on both rigid and nonrigid objects demonstrate the generality and efficacy of the proposed methods.
Complex 3D Shape Recovery Using a Dual-Space Approach
Authors: Chen Liang 0004, Kwan-Yee Kenneth Wong
Venue: CVPR
Year: 2005

This paper presents a novel method for reconstructing complex 3D objects with unknown topology using silhouettes extracted from image sequences. This method exploits the duality principle governing surface points and their corresponding tangent planes, and enables a direct estimation of points on the contour generators. A major problem in other related works concerns with the search for a tangent basis at singularities in the dual tangent space. This problem is addressed here by utilizing the epipolar parameterization for identifying a well-defined basis at each point, and thus avoids any form of search. For the degenerate cases where epipolar parameterization breaks up, a fast on-the-fly validation is performed for each computed surface point, which consequently leads to a significant improvement in robustness. As the resulting contour generator points are not suitable for direct triangulation, a topologically correct surface extracting method based on slicing plane is presented. Both experiments on synthetic and real world data show that the proposed method has comparable robustness as those existing volumetric methods regarding surface of complex topology, whilst producing more accurate estimation of surface points.
Isophote Properties as Features for Object Detection
Authors: Jeroen Lichtenauer, Emile A. Hendriks, Marcel J. T. Reinders
Venue: CVPR
Year: 2005

Usually, object detection is performed directly on (normalized) gray values or gray primitives like gradients or Haar-like features. In that case the learning of relationships between gray primitives, that describe the structure of the object, is the complete responsibility of the classifier. We propose to apply more knowledge about the image structure in the preprocessing step, by computing local isophote directions and curvatures, in order to supply the classifier with much more informative image structure features. However, a periodic feature space, like orientation, is unsuited for common classification methods. Therefore, we split orientation into two more suitable components. Experiments show that the isophote features result in better detection performance than intensities, gradients or Haar-like features.
A Caratheodory-Fejer Approach to Dynamic Appearance Modeling
Authors: Hwasup Lim, Octavia I. Camps, Mario Sznaier
Venue: CVPR
Year: 2005

This paper presents a technique to learn dynamic appearance models from a small number of training frames. Under this framework, dynamic appearance is modelled as an unknown operator that satisfies certain interpolation conditions and that can be efficiently identified using very little a priori information with off the shelf software. The advantages of the proposed method are illustrated with several examples where the leaned dynamics accurately predict the appearance of the targets preventing tracking failures due to occlusion or clutter.
Fast Illumination-Invariant Background Subtraction Using Two Views: Error Analysis, Sensor Placement and Applications
Authors: Ser-Nam Lim, Anurag Mittal, Larry S. Davis, Nikos Paragios
Venue: CVPR
Year: 2005

Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.
A Direct Method for Modeling Non-Rigid Motion with Thin Plate Spline
Authors: Jongwoo Lim, Ming-Hsuan Yang 0001
Venue: CVPR
Year: 2005

Thin plate spline (TPS) transformations have been applied to non-rigid shape matching with impressive results. However, existing methods often use a sparse set of point correspondences which are established prior to shape matching. A straightforward approach to finding point correspondences and computing TPS parameters imposes expensive computations, thereby motivating us to develop an efficient solution. In this paper, we present a direct method for recovering non-rigid object motion from its appearance in which the point correspondences are simultaneously established while estimating TPS parameters. The motion parameters are estimated in a stiff-to-flexible approach and the principal appearance deformations are learned that can be utilized for motion analysis and recognition. Numerous experiments demonstrate the efficiency and efficacy of the proposed algorithm in modeling the motion details of non-rigid objects undergoing shape deformation and pose variation.
Robust Face Detection with Multi-Class Boosting
Authors: Yen-Yu Lin, Tyng-Luh Liu
Venue: CVPR
Year: 2005

With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple classes. Specifically, our approach focuses on a new multi-class boosting algorithm, called MBHboost, and its integration with a cascade structure for effectively performing face detection. There are three main advantages of using MBHboost: 1) each MBH weak learner is derived by sharing a good projection direction such that each class of data has its own decision boundary; 2) the proposed boosting algorithm is established based on an optimal criterion for multi-class classification; and 3) since MBHboost is flexible with respect to the number of classes, it turns out that it is possible to use only one single boosted cascade for the multi-class detection. All these properties give rise to a robust system to detect faces efficiently and accurately.
Using the Inner-Distance for Classification of Articulated Shapes
Authors: Haibin Ling, David W. Jacobs
Venue: CVPR
Year: 2005

We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.
Detecting Doctored Images Using Camera Response Normality and Consistency
Authors: Zhouchen Lin, Rongrong Wang, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2005

The advance in image/video editing techniques has facilitated people in synthesizing realistic images/videos that may hard to be distinguished from real ones by visual examination. This poses a problem: how to differentiate real images/videos from doctored ones? This is a serious problem because some legal issues may occur if there is no reliable way for doctored image/video detection when human inspection fails. Digital watermarking cannot solve this problem completely. We propose an approach that computes the response functions of the camera by selecting appropriate patches in different ways. An image may be doctored if the response functions are abnormal or inconsistent to each other. The normality of the response functions is classified by a trained support vector machine (SVM). Experiments show that our method is effective for high-contrast images with many textureless edges.
Addressing Radiometric Nonidealities: A Unified Framework
Authors: Anatoly Litvinov, Yoav Y. Schechner
Venue: CVPR
Year: 2005

Cameras may have non-ideal radiometric aspects, including spatial non-uniformity, e.g., due to vignetting; a nonlinear radiometric response of the sensor; and temporal variations due to automatic gain control (AGC). Often, these characteristics exist simultaneously, and are typically unknown. They thus hinder consistent photometric measurements. In particular, they create annoying seams in image mosaics. Prior studies approached part of these problems while excluding others. We handle all these problems in a unified framework. We suggest an approach for simultaneously estimating the radiometric response, the spatial non-uniformity and the temporally varying gain. The approach does not rely on dedicated processes that intentionally vary exposure settings. Rather, it is based on an ordinary frame sequence acquired during camera motion. The estimated non-ideal characteristics are then compensated for. We state fundamental ambiguities associated with this recovery problem, while exposing a novel image invariance. The method is demonstrated in several experiments, where different frames are brought into mutual radiometric consistency. The accuracy achieved is sufficient for seamless mosaicing, with no need to resort to dedicated seam-feathering methods.
A Nonlinear Approach for Face Sketch Synthesis and Recognition
Authors: Qingshan Liu 0001, Xiaoou Tang, Hongliang Jin, Hanqing Lu, Songde Ma
Venue: CVPR
Year: 2005

Most face recognition systems focus on photo-based face recognition. In this paper, we present a face recognition system based on face sketches. The proposed system contains two elements: pseudo-sketch synthesis and sketch recognition. The pseudo-sketch generation method is based on local linear preserving of geometry between photo and sketch images, which is inspired by the idea of locally linear embedding. The nonlinear discriminate analysis is used to recognize the probe sketch from the synthesized pseudo-sketches. Experimental results on over 600 photo-sketch pairs show that the performance of the proposed method is encouraging.
Camera Matchmoving in Unprepared, Unknown Environments
Authors: Manolis I. A. Lourakis, Antonis A. Argyros
Venue: CVPR
Year: 2005

y are being acquired. Furthermore, it does not rely upon the presence in the environment of fiducial markers or special calibration objects. A brief overview of our approach is given in the next section. More detailed descriptions can be found in [1] and online at http://www. ics.forth.gr/lourakis/camtrack/. THE APPROACH Our method is based on a feature-based 3D plane tracking technique, which permits the estimation of the homographies induced by a 3D plane between successive image pairs. At the core of plane tracking lies a homography &quot;chaining&quot; operation that is applied to triplets of consecutive images through a sliding time window and exploits the constraint that all images of a planar surface acquired by a rigidly moving observer depend upon the same 3D geometry. Since the tracked plane is not required to be physically present in the scene, a virtual one can be used instead. Plane tracking is achieved by matching between images the 2D projections of points from all over the scen
Infomax Boosting
Authors: Siwei Lyu
Venue: CVPR
Year: 2005

In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed, based on the quadratic mutual information, non-parametric density estimation and fast Gauss transform. The feature pursuit process is integrated into a boosting framework as infomax boosting. The performance of a face detector based on infomax boosting is reported.
Contrast Enhancement of Multi-Displays Using Human Contrast Sensitivity
Authors: Aditi Majumder
Venue: CVPR
Year: 2005

Study of contrast sensitivity of the human eye shows that we are more sensitive to brightness differences at low intensity levels than at high intensity levels. We apply this fact effectively to achieve brightness seamless-ness in multi-projector displays. Multi-displays, popularly made of a rectangular array of partially overlapping projectors, show severe spatial variation in brightness. Existing methods achieve brightness uniformity across the display by matching the brightness response of every pixel to the pixel with the most limited contrast leading to severe compression in the contrast of the display. In this paper, we propose a method that allows a constrained variation in brightness guided by the human contrast sensitivity function such that it is imperceptible to the human eye. This achieves a seamless multi-display with a dramatic improvement in the contrast making the display practically usable.
Radon-Based Structure from Motion without Correspondences
Authors: Ameesh Makadia, Christopher Geyer, Shankar Sastry 0001, Kostas Daniilidis
Venue: CVPR
Year: 2005

We present a novel approach for the estimation of 3D-motion directly from two images using the Radon transform. We assume a similarity function defined on the cross-product of two images which assigns a weight to all feature pairs. This similarity function is integrated over all feature pairs that satisfy the epipolar constraint. This integration is equivalent to filtering the similarity function with a Dirac function embedding the epipolar constraint. The result of this convolution is a function of the five unknown motion parameters with maxima at the positions of compatible rigid motions. The breakthrough is in the realization that the Radon transform is a filtering operator: If we assume that images are defined on spheres and the epipolar constraint is a group action of two rotations on two spheres, then the Radon transform is a convolution/correlation integral. We propose a new algorithm to compute this integral from the spherical harmonics of the similarity and Dirac functions. The resulting resolution in the motion space depends on the bandwidth we keep from the spherical transform. The strength of the algorithm is in avoiding a commitment to correspondences, thus being robust to erroneous feature detection, outliers, and multiple motions. The algorithm has been tested in sequences of real omnidirectional images and it outperforms correspondence-based structure from motion.
Beyond Lambert: Reconstructing Specular Surfaces Using Color
Authors: Satya P. Mallick, Todd E. Zickler, David J. Kriegman, Peter N. Belhumeur
Venue: CVPR
Year: 2005

We present a photometric stereo method for non-diffuse materials that does not require an explicit reflectance model or reference object. By computing a data-dependent rotation of RGB color space, we show that the specular reflection effects can be separated from the much simpler, diffuse (approximately Lambertian) reflection effects for surfaces that can be modeled with dichromatic reflectance. Images in this transformed color space are used to obtain photometric reconstructions that are independent of the specular reflectance. In contrast to other methods for highlight removal based on dichromatic color separation (e.g., color histogram analysis and/or polarization), we do not explicitly recover the specular and diffuse components of an image. Instead, we simply find a transformation of color space that yields more direct access to shape information. The method is purely local and is able to handle surfaces with arbitrary texture.
Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model
Authors: Nicolas Martel-Brisson, André Zaccarin
Venue: CVPR
Year: 2005

Moving cast shadows are a major concern for foreground detection algorithms. Processing of foreground images in surveillance applications typically requires that such shadows have been identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of non-uniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, and prevent false detection in regions where shadows cannot be detected. Gaussian mixture shadow models (GMSM) are automatically constructed and updated over time, are easily added to GMM architecture for foreground detection, and require only a small number of parameters. Results obtained with different scene types show the robustness of the approach.