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50
Efficient region tracking with parametric models of geometry and illumination
 PAMI
, 1998
"... Abstract—As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane. Rather, complications arise due to the fact that the object undergoes changes in pose relative to the v ..."
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Cited by 442 (22 self)
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Abstract—As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane. Rather, complications arise due to the fact that the object undergoes changes in pose relative to the viewing camera, changes in illumination relative to light sources, and may even become partially or fully occluded. In this paper, we develop an efficient, general framework for object tracking—one which addresses each of these complications. We first develop a computationally efficient method for handling the geometric distortions produced by changes in pose. We then combine geometry and illumination into an algorithm that tracks large image regions using no more computation than would be required to track with no accommodation for illumination changes. Finally, we augment these methods with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Throughout, we present experimental results performed on live video sequences demonstrating the effectiveness and efficiency of our methods. Index Terms—Visual tracking, realtime vision, illumination, motion estimation, robust statistics.
From Few to many: Illumination cone models for face recognition under variable lighting and pose
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a generative appearancebased method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a smal ..."
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Cited by 433 (12 self)
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We present a generative appearancebased method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render—or synthesize—images of the face under novel poses and illumination conditions. The pose space is then sampled, and for each pose the corresponding illumination cone is approximated by a lowdimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone (based on Euclidean distance within the image space). We test our face recognition method on 4050 images from the Yale Face Database B; these images contain 405 viewing conditions (9 poses ¢ 45 illumination conditions) for 10 individuals. The method performs almost without error, except on the most extreme lighting directions, and significantly outperforms popular recognition methods that do not use a generative model.
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
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Cited by 336 (21 self)
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We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a lowdimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions. Finally, we show a simple way to enforce nonnegative lighting when the images of an object lie near a 4D linear space. Research conducted w...
What is the Set of Images of an Object Under All Possible Lighting Conditions
 IEEE CVPR
, 1996
"... The appearance of a particular object depends on both the viewpoint from which it is observed and the light sources by which it is illuminated. If the appearance of two objects is never identical for any pose or lighting conditions, then in theory the objects can always be distinguished or recogni ..."
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Cited by 318 (28 self)
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The appearance of a particular object depends on both the viewpoint from which it is observed and the light sources by which it is illuminated. If the appearance of two objects is never identical for any pose or lighting conditions, then in theory the objects can always be distinguished or recognized. The question arises: What is the set of images of an object under all lighting conditions and pose? In this paper, ive consider only the set of images of an object under variable allumination (including multiple, extended light sources and attached shadows). We prove that the set of npixel images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in IR " and that the dimension of this illumination cone equals the number of distinct surface normals. Furthermore, we show that the cone for a particular object can be constructed from three properly chosen images. Finally, we prove that the set of npixel images of an object of any shape and with an arbitrary reflectance function, seen under all possible illumination conditions, still forms a convex cone in Rn. Th.ese results immediately suggest certain approaches to object recognition. Throughout this paper, we ofler results demonstrating the empirical validity of the illumination cone representation. 1
Acquiring linear subspaces for face recognition under variable lighting
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in ..."
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Cited by 133 (2 self)
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Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources, and again PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a lowdimensional linear space, and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.
From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
, 2000
"... Abstract Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearancebased methods which, unlike previous appearancebased approaches, require only a small set of training images to generate a rich representation that models this ..."
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Cited by 93 (8 self)
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Abstract Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearancebased methods which, unlike previous appearancebased approaches, require only a small set of training images to generate a rich representation that models this variability. Specifically, from as few as three images of an object in fixed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was specifically gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods. 1 Introduction An object can appear strikingly different due to changes in pose and illumination (see Figure 1). To handle this image variability, object recognition systems usually use one of the following approaches: (a) control viewing conditions, (b) employ a representation that is invariant to the viewing conditions, or (c) directly model this variability. For example, there is a long tradition of performing edge detection at an early stage since the presence of an edge at an image location is thought to be largely independent of lighting. It has been observed, however, that methods for face recognition based on finding local image features and using their geometric relation are generally ineffective [4].
Determining generative models of objects under varying illumination: Shape and albedo from multiple images using svd and integrability
 International Journal of Computer Vision
, 1999
"... We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects’ appearance under a range of lighting conditions. This work is closely related to photometric stereo with unk ..."
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Cited by 68 (3 self)
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We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects’ appearance under a range of lighting conditions. This work is closely related to photometric stereo with unknown light sources and, in particular, to the use of Singular Value Decomposition (SVD) to estimate shape and albedo from multiple images up to a linear transformation [15]. Firstly we analyze and extend the SVD approach to this problem. We demonstrate that it applies to objects for which the dominant imaging effects are Lambertian reflectance with a distant light source and a background ambient term. To determine that this is a reasonable approximation we calculate the eigenvectors of the SVD on a set of real objects, under varying lighting conditions, and demonstrate that the first few eigenvectors account for most of the data in agreement with our predictions. We then analyze the linear ambiguities in the SVD approach and demonstrate that previous methods proposed to resolve them [15] are only valid under certain conditions. We discuss alternative possibilities and, in particular, demonstrate that knowledge of the object class is sufficient to resolve this problem. Secondly, we describe the use of surface consistency for putting constraints on the possible solutions. We prove that this constraint reduces the ambiguities to a subspace called the generalized bas relief ambiguity (GBR) which is inherent in the Lambertian reflectance function (and which can be shown to exist even if attached and cast shadows are present [3]). We demonstrate the use of surface consistency to solve for the shape and albedo up to a GBR and describe, and implement, a variety of additional assumptions to resolve the GBR. Thirdly, we demonstrate an iterative algorithm that can detect and remove some attached shadows from the objects thereby increasing the accuracy of the reconstructed shape and albedo. 1
Face Recognition Under Variable Lighting using Harmonic Image Exemplars
, 2003
"... We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the recent result [1] which demostrated that the set of images of a convex La ..."
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Cited by 55 (7 self)
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We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the recent result [1] which demostrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a lowdimensional linear subspace. In this paper, we show that we can recover basis images spanning this space from just one image taken under arbitrary illumination conditions. First, using a bootstrap set consisting of 3D face models, we compute a statistical model for each basis image. During training, given a novel face image under arbitrary illumination, we recover a set of images for this face. We prove that these images are the set of basis images with maximum probability. During testing, we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our method achieves comparable levels of accuracy with methods that have much more onerous training data requirements.
Nine Points of Lights: Acquiring Subspaces for Face Recognition under Variable Illumination. CVPR2001
"... Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. Basis images spanning this space are usually obtained in one of two ways: A large number of images of the object u ..."
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Cited by 49 (4 self)
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Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. Basis images spanning this space are usually obtained in one of two ways: A large number of images of the object under different conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, a 3D model (perhaps reconstructed from images) is used to render virtual images under either point sources from which a subspace is derived using PCA or more recently under diffuse synthetic lighting based on spherical harmonics. In this paper, we show that there exists a configuration of nine point light source directions such that by taking nine images of each individual under these single sources, the resulting subspace is effective at recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex intermediate steps such as PCA and 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or physically construct complex diffuse (harmonic) light fields. We provide both theoretical and empirical results to explain why these linear spaces should be good for recognition. 1.
Clustering appearances of objects under varying illumination conditions
 In CVPR
, 2003
"... We introduce two appearancebased methods for clustering a set of images of 3D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clusteri ..."
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Cited by 47 (3 self)
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We introduce two appearancebased methods for clustering a set of images of 3D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the highdimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, wellknown image data sets. 1