OpenRaiser/Intern-Atlas huggingface dataset:
Robust Visual Tracking Using Case-Based Reasoning with Confidence
Authors: Zhiwei Zhu, Wenhui Liao, Qiang Ji
Venue: CVPR
Year: 2006

The paper describes a simple but robust framework for visual object tracking in a video sequence. Compared with the existing tracking techniques, our proposed tracking technique has two significant contributions. First, a Case- Based Reasoning (CBR) paradigm is introduced to track the non-rigid object robustly under significant appearance changes without drifting away. Second, it can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified successfully. Specifically, under this framework, the appearance changes of the object being tracked can be adapted dynamically during tracking via an adaption mechanism of CBR. Hence, an accurate 2D tracking model can be maintained online for each image frame during tracking. Therefore, the proposed tracking technique possesses a self-recovery capability so that the object can be tracked robustly under significant appearance changes without error accumulation. Application was focused on the development of a real-time face tracking system. Via the proposed framework, the built real-time face tracker can track the human face robustly at 26 frames per second under various face orientations, significant facial expression and external illumination changes.
Selecting Principal Components in a Two-Stage LDA Algorithm
Authors: Manli Zhu, Aleix M. Martínez
Venue: CVPR
Year: 2006

Linear Discriminant Analysis (LDA) is a well-known and important tool in pattern recognition with potential applications in many areas of research. The most famous and used formulation of LDA is that given by the Fisher-Rao criterion, where the problem reduces to a simple simultaneous diagonalization of two symmetric, positive-definite matrices, A and B; i.e. B^-1 AV = VA. Here, A defines the metric to be maximized, while B defines the metric to be minimized. However, when B has near-zero eigenvalues, the Fisher-Rao criterion gets dominated by these. While this works well when such small variances describe vectors where most of the discriminant information is, the results will be incorrect when these small variances are caused by noise. Knowing which of these near-zero values are to be used and which need to be eliminated is a challenging yet fundamental task in LDA. This paper presents a criterion for the selection of those vectors of B that are best for classification. The proposed solution is based on a simple factorization of B^-1 A that permits the re-ordering of the eigenvectors of B without the need to effect the end result. This allows us to readily eliminate the noisy vectors while keeping the most discriminant ones. A theoretical basis for these results is presented along with extensive experimental results to validate the claims.
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
Authors: Qiang Zhu 0006, Mei-Chen Yeh, Kwang-Ting Cheng, Shai Avidan
Venue: CVPR
Year: 2006

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.
Reciprocal Image Features for Uncalibrated Helmholtz Stereopsis
Authors: Todd E. Zickler
Venue: CVPR
Year: 2006

Helmholtz stereopsis is a surface reconstruction method that exploits reciprocity for the recovery of 3D shape without an assumed BRDF model, and it has been shown to yield high quality results when the cameras and light sources are carefully calibrated. In many practical cases, however, accurate off-line calibration is difficult (or impossible) to achieve. We address this issue by exploring widebaseline matching in Helmholtz stereo images. We identify two classes of local image interest regions (‘features’) that can be reliably detected and matched between views in a Helmholtz stereo dataset; and by exploiting reciprocity, we show how these regions can be used to recover both geometric and radiometric calibration information. When used in conjunction with existing methods for dense reconstruction, this provides an automated shape recovery pipeline that operates independent of reflectance and does not require the acquisition of additional calibration images off-line.
Color Subspaces as Photometric Invariants
Authors: Todd E. Zickler, Satya P. Mallick, David J. Kriegman, Peter N. Belhumeur
Venue: CVPR
Year: 2006

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe two such invariants" one invariant to specular reflections, and the other invariant to both specular reflections and diffuse shading" that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from subspaces of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, material-based segmentation, and motion estimation.
Transformation invariant component analysis for binary images
Authors: Zoran Zivkovic, Jakob Verbeek
Venue: CVPR
Year: 2006

There are various situations where image data is binary: character recognition, result of image segmentation etc. As a first contribution, we compare Gaussian based principal component analysis (PCA), which is often used to model images, and "binary PCA" which models the binary data more naturally using Bernoulli distributions. Furthermore, we address the problem of data alignment. Image data is often perturbed by some global transformations such as shifting, rotation, scaling etc. In such cases the data needs to be transformed to some canonical aligned form. As a second contribution, we extend the binary PCA to the "transformation invariant mixture of binary PCAs" which simultaneously corrects the data for a set of global transformations and learns the binary PCA model on the aligned data.
A Multimodality Framework for Creating Speaker/Non-Speaker Profile Databases for Real-World Video
Authors: Jehanzeb Abbas, Charlie K. Dagli, Thomas S. Huang
Venue: CVPR
Year: 2007

We propose a complete solution to full modality person-profiling for speakers and submodality person-profiling for non-speakers in real-world videos. This is a step towards building an elaborate database efface, name and voice correspondence for speakers appearing in the news videos. In addition we are also interested in only name and face correspondence database for non-speakers who appear during voice-overs. We use an unsupervised technique for creating a speaker identification database and a unique primary feature matching and parallel line matching algorithm for creating a non-speaker identification database. We tested our approach on real world data and the results show good performance for news videos. It can be incorporated as part of a larger multimedia news video analysis system or a multimedia search system for efficient news video retrieval and browsing.
Shape matching through particle dynamics warping
Authors: Gady Agam, Suneel Suresh
Venue: CVPR
Year: 2007

Shape matching is fundamental to numerous computer vision algorithms and may be used for similarity determination and registration. Establishing correspondence and measuring similarity between shapes is of great importance. Shape matching often involves simultaneous estimation of both a correspondence and an alignment transformation. Such an estimate is particularly difficult when the alignment transformation is non-linear and so contains a large number of degrees of freedom. We describe a novel approach for shape matching that is based on shape contexts and uses particle dynamics warping to maximize the similarity of shapes while satisfying structural constraints. The approach is based on an iterative solution of a system of first order ordinary differential equations. The main advantage of the proposed approach is its ability to incorporate shape constraints into the matching process. Furthermore, the proposed approach does not require a solution of an optimal assignment problem which is sensitive to outliers, and does not require thin-plate spline warping which is computationally expensive. To illustrate the applicability of our approach we address the problem of offline signature recognition which in contrast to online signature recognition does not provide for a simple parametrization of the signature curves. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.
Symmetric Objects are Hardly Ambiguous
Authors: Gaurav Aggarwal, Soma Biswas, Rama Chellappa
Venue: CVPR
Year: 2007

Given any two images taken under different illumination conditions, there always exist a physically realizable object which is consistent with both the images even if the lighting in each scene is constrained to be a known point light source at infinity. In this paper, we show that images are much less ambiguous for the class of bilaterally symmetric Lambertian objects. In fact, the set of such objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. The conditions required for two objects to belong to the same equivalence class are very restrictive, thereby leading to the conclusion that images of symmetric objects are hardly ambiguous. The observation leads to an illumination-invariant matching algorithm to compare images of bilaterally symmetric Lambertian objects. Experiments on real data are performed to show the implications of the theoretical result even when the symmetry and Lambertian assumptions are not strictly satisfied.
Extraction of 3D Transform and Scale Invariant Patches from Range Scans
Authors: Erdem Akagündüz, Ilkay Ulusoy
Venue: CVPR
Year: 2007

An algorithm is proposed to extract transformation and scale invariant 3D fundamental elements from the surface structure of 3D range scan data. The surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure. The extracted fundamental structures can later be used as nodes in a topological graph where the links between the nodes can be defined as the spatial and geometric relations between the fundamental elements.
Motion and Appearance Contexts for Tracking and Re-Acquiring Targets in Aerial Videos
Authors: Saad Ali, Vladimir Reilly, Mubarak Shah
Venue: CVPR
Year: 2007

In this paper, we use motion and appearance contexts for persistent tracking of objects in aerial imagery. The motion context in a given environment is a collection of trajectories of objects which are representative of the motion of the occluded or unobserved object. It is learned using a clustering scheme based on the Lyapunov characteristic exponent (LCE) which measures the mean exponential rate of divergence of the nearby trajectories. The learned motion context is then used in a regression framework to predict the location of the unobserved object. The appearance context of an occluded (target) object consists of appearance information of objects which are currently occluded or unobserved. It is incorporated by learning a distribution of interclass variation for each target-unobservable object pair. In addition, intra-class variation distribution is constructed for each occluded object using all of its previous observations. Qualitative and quantitative results are reported on challenging aerial sequences.
Resolving the Generalized Bas-Relief Ambiguity by Entropy Minimization
Authors: Neil Gordon Alldrin, Satya P. Mallick, David J. Kriegman
Venue: CVPR
Year: 2007

It is well known in the photometric stereo literature that uncalibrated photometric stereo, where light source strength and direction are unknown, can recover the surface geometry of a Lambertian object up to a 3-parameter linear transform known as the generalized bas relief (GBR) ambiguity. Many techniques have been proposed for resolving the GBR ambiguity, typically by exploiting prior knowledge of the light sources, the object geometry, or non-Lambertian effects such as specularities. A less celebrated consequence of the GBR transformation is that the albedo at each surface point is transformed along with the geometry. Thus, it should be possible to resolve the GBR ambiguity by exploiting priors on the albedo distribution. To the best of our knowledge, the only time the albedo distribution has been used to resolve the GBR is in the case of uniform albedo. We propose a new prior on the albedo distribution : that the entropy of the distribution should be low. This prior is justified by the fact that many objects in the real-world are composed of a small finite set of albedo values.
Object of Interest segmentation and Tracking by Using Feature Selection and Active Contours
Authors: Mohand Saïd Allili, Djemel Ziou
Venue: CVPR
Year: 2007

Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance is used.
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
Authors: Sharon Alpert, Meirav Galun, Ronen Basri, Achi Brandt
Venue: CVPR
Year: 2007

We present a parameter free approach that utilizes multiple cues for image segmentation. Beginning with an image, we execute a sequence of bottom-up aggregation steps in which pixels are gradually merged to produce larger and larger regions. In each step we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using a mixture of experts formulation. This probabilistic approach is integrated into a graph coarsening scheme providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. We test our method on a variety of gray scale images and compare our results to several existing segmentation algorithms.
Virtual Recovery of the Deteriorated Art Object based on AR Technology
Authors: Toshiyuki Amano, Ryo Suzuki
Venue: CVPR
Year: 2007

In this paper, the virtual restoration method of the art piece in the real world is proposed. The correction pattern for restoration is generated from non damaged object's image scanned in advance. As the method to recover the beauty, the marker tracking is used to detect art piece position and the correction image is projected from calibrated LCD projector. In the experiment, the color restoration by a sample image was performed. From the experimental results, the ability of restoration of the discoloring was confirmed in the real world imaginary by the correction image projection.
A Component-Based Approach to Hand Verification
Authors: Gholamreza Amayeh, George Bebis, Ali Erol, Mircea Nicolescu
Venue: CVPR
Year: 2007

This paper describes a novel hand-based verification system based on palm-finger segmentation and fusion. The proposed system operates on 2D hand images acquired by placing the hand on a planar lighting table without any guidance pegs. The segmentation of the palm and the fingers is performed without requiring the extraction of any landmark points on the hand. First, the hand is segmented from the forearm using a robust, iterative methodology based on morphological operators. Then, the hand is segmented into six regions corresponding to the palm and the fingers using morphological operators again. The geometry of each component of the hand is represented using high order Zernike moments which are computed using an efficient methodology. Finally, verification is performed by fusing information from different parts of the hand. The proposed system has been evaluated on a database of 101 subjects illustrating high accuracy and robustness. Comparisons with competitive approaches that use the whole hand illustrate the superiority of the proposed, component-based, approach both in terms of accuracy and robustness. Qualitative comparisons with state of the art systems illustrate that the proposed system has comparable or better performance.
Hardware implementation of an SAD based stereo vision algorithm
Authors: Kristian Ambrosch, Wilfried Kubinger, Martin Humenberger, Andreas Steininger
Venue: CVPR
Year: 2007

This paper presents the hardware implementation of a stereo vision core algorithm, that runs in real-time and is targeted at automotive applications. The algorithm is based on the sum of absolute differences (SAD) and computes the disparity map using 320 times 240 input images with a maximum disparity of 100 pixels. The hardware operates at a frequency of 65 MHz and achieves a frame rate of 425 fps by calculating the data highly parallel and pipelined. Thus an implemented and basically optimized software solution, running on an Intel Pentium 4 with 3 GHz clock frequency is 166 times outperformed.
Face Recognition Using Kernel Ridge Regression
Authors: Senjian An, Wanquan Liu, Svetha Venkatesh
Venue: CVPR
Year: 2007

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem efface recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisher faces and Laplacian faces) and also performs comparably with the recently developed Orthogonal Laplacian faces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.
Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video Surveillance
Authors: Senyo Apewokin, Brian Valentine, Linda M. Wills, D. Scott Wills, Antonio Gentile
Venue: CVPR
Year: 2007

Automated video surveillance applications require accurate separation of foreground and background image content. Cost sensitive embedded platforms place realtime performance and efficiency demands on techniques to accomplish this task. In this paper we evaluate pixel-level foreground extraction techniques for a low cost integrated surveillance system. We introduce a new adaptive technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences. The proposed MM algorithm delivers comparable accuracy of the best alternative (Mixture of Gaussians) with a 6X improvement in execution time and an 18% reduction in required storage.
Texture-Preserving Shadow Removal in Color Images Containing Curved Surfaces
Authors: Eli Arbel, Hagit Hel-Or
Venue: CVPR
Year: 2007

Several approaches to shadow removal in color images have been introduced in recent years. Yet these methods fail in removing shadows that are cast on curved surfaces, as well as retaining the original texture of the image in shadow boundaries, known as penumbra regions. In this paper, we propose a novel approach which effectively removes shadows from curved surfaces while retaining the textural information in the penumbra, yielding high quality shadow-free images. Our approach aims at finding scale factors to cancel the effect of shadows, including penumbra regions where illumination changes gradually. Due to the fact that surface geometry is also taken into account when computing the scale factors, our method can handle a wider range of shadow images than current state-of-the-art methods, as demonstrated by several examples.
Modelling Objects using Distribution and Topology of Multiscale Region Pairs
Authors: Himanshu Arora, Narendra Ahuja
Venue: CVPR
Year: 2007

We propose a method for simultaneous detection, localization and segmentation of objects of a known category. We show that this is possible by using segments as features. To this end, we propose an object model in which the image is represented as a tree, that captures containment relationships among the segments. Using segments as features has the advantage that object detection and segmentation is done simultaneously, forgoing the need for a separate sophisticated model for object segmentation. A generative model of an object category is estimated in a supervised mode, in terms of the characteristics of its constituent regions, their relative locations, and their mutual containment. The novel aspect of this work lies in simplifying the description of the hierarchy in terms of constraints that apply to only pairs of nodes, instead of all nodes in the tree. We show that this indeed improves the speed of learning algorithm. Inference is done using graph cuts. We report the performance of the model on standard datasets.