OpenRaiser/Intern-Atlas huggingface dataset:
Passive Photometric Stereo from Motion
Authors: Jongwoo Lim, Jeffrey Ho, Ming-Hsuan Yang 0001, David J. Kriegman
Venue: ICCV
Year: 2005

We introduce an iterative algorithm for shape reconstruction from multiple images of a moving (Lambertian) object illuminated by distant (and possibly time varying) lighting. Starting with an initial piecewise linear surface, the algorithm iteratively estimates a new surface based on the previous surface estimate and the photometric information available from the input image sequence. During each iteration, standard photometric stereo techniques are applied to estimate the surface normals up to an unknown generalized bas-relief transform, and a new surface is computed by integrating the estimated normals. The algorithm essentially consists of a sequence of matrix factorizations (of intensity values) followed by minimization using gradient descent (integration of the normals). Conceptually, the algorithm admits a clear geometric interpretation, which is used to provide a qualitative analysis of the algorithm's convergence. Implementation-wise, it is straightforward being based on several established photometric stereo and structure from motion algorithms. We demonstrate experimentally the effectiveness of our algorithm using several videos of hand-held objects moving in front of a fixed light and camera.
Opaque Document Imaging: Building Images of Inaccessible Texts
Authors: Yun Lin 0011, W. Brent Seales
Venue: ICCV
Year: 2005

This paper introduces a method for building a readable image of an opaque, rolled or folded text from a volumetric, penetrating scan. The problem is framed by localizing, constructing, and manipulating an image induced by a surface embedded in a 3D voxel space. There are two central contributions that lead to the demonstrated results. First is an energy-based texture formation algorithm, which is a function of voxel intensities and the geometry of the embedded surface. Second is a regularization algorithm based on a constrained mapping of the embedded surface to a regularized image plane. The mapping preserves angles and lengths, which minimizes the distortion of text in the image. The experimental results show readable images derived from custom, high resolution (X-ray-based) CT scans of rolled papyrus and ink samples. These methods are significant for scholars seeking to study inaccessible texts, and may lead to viable techniques for scanning everyday opaque objects (books) without opening them.
Deformation Invariant Image Matching
Authors: Haibin Ling, David W. Jacobs
Venue: ICCV
Year: 2005

We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D space, with intensity weighted relative to distance in x-y. We show that as this weight increases, geodesic distances on the embedded surface are less affected by image deformations. In the limit, distances are deformation invariant. We use geodesic sampling to get neighborhood samples for interest points, and then use a geodesic-intensity histogram (GIH) as a deformation invariant local descriptor. In addition to its invariance, the new descriptor automatically finds its support region. This means it can safely gather information from a large neighborhood to improve discriminability. Furthermore, we propose a matching method for this descriptor that is invariant to affine lighting changes. We have tested this new descriptor on interest point matching for two data sets, one with synthetic deformation and lighting change, and another with real non-affine deformations. Our method shows promising matching results compared to several other approaches
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
Authors: Herve Lombaert, Yiyong Sun, Leo J. Grady, Chenyang Xu 0001
Venue: ICCV
Year: 2005

In the short time since publication of Boykov and Jolly's seminal paper [2001], graph cuts have become well established as a leading method in 2D and 3D semi-automated image segmentation. Although this approach is computationally feasible for many tasks, the memory overhead and supralinear time complexity of leading algorithms results in an excessive computational burden for high-resolution data. In this paper, we introduce a multilevel banded heuristic for computation of graph cuts that is motivated by the well-known narrow band algorithm in level set computation. We perform a number of numerical experiments to show that this heuristic drastically reduces both the running time and the memory consumption of graph cuts while producing nearly the same segmentation result as the conventional graph cuts. Additionally, we are able to characterize the type of segmentation target for which our multilevel banded heuristic yields different results from the conventional graph cuts. The proposed method has been applied to both 2D and 3D images with promising results.
Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?
Authors: Manolis I. A. Lourakis, Antonis A. Argyros
Venue: ICCV
Year: 2005

In order to obtain optimal 3D structure and viewing parameter estimates, bundle adjustment is often used as the last step of feature-based structure and motion estimation algorithms. Bundle adjustment involves the formulation of a large scale, yet sparse minimization problem, which is traditionally solved using a sparse variant of the Levenberg-Marquardt optimization algorithm that avoids storing and operating on zero entries. This paper argues that considerable computational benefits can be gained by substituting the sparse Levenberg-Marquardt algorithm in the implementation of bundle adjustment with a sparse variant of Powell's dog leg non-linear least squares technique. Detailed comparative experimental results provide strong evidence supporting this claim.
Edge-Based Rich Representation for Vehicle Classification
Authors: Xiaoxu Ma, W. Eric L. Grimson
Venue: ICCV
Year: 2005

In this paper, we propose an approach to vehicle classification under a mid-field surveillance framework. We develop a repeatable and discriminative feature based on edge points and modified SIFT descriptors, and introduce a rich representation for object classes. Experimental results show the proposed approach is promising for vehicle classification in surveillance videos despite great challenges such as limited image size and quality and large intra-class variations. Comparisons demonstrate the proposed approach outperforms other methods.
Globally Optimal Solutions for Energy Minimization in Stereo Vision Using Reweighted Belief Propagation
Authors: Talya Meltzer, Chen Yanover, Yair Weiss
Venue: ICCV
Year: 2005

A wide range of low level vision problems have been formulated in terms of finding the most probable assignment of a Markov random field (or equivalently the lowest energy configuration). Perhaps the most successful example is stereo vision. For the stereo problem, it has been shown that finding the global optimum is NP hard but good results have been obtained using a number of approximate optimization algorithms. In this paper, we show that for standard benchmark stereo pairs, the global optimum can be found in about 30 minutes using a variant of the belief propagation (BP) algorithm. We extend previous theoretical results on reweighted belief propagation to account for possible ties in the beliefs and using these results we obtain easily checkable conditions that guarantee that the BP disparities are the global optima. We verify experimentally that these conditions are typically met for the standard benchmark stereo pairs and discuss the implications of our results for further progress in stereo.
Local Features for Object Class Recognition
Authors: Krystian Mikolajczyk, Bastian Leibe, Bernt Schiele
Venue: ICCV
Year: 2005

In this paper, we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors/descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes (Mikolajczyk and Schmid, 2004). However, it is unclear if these results can be generalized to categorization problems, which require different properties of features. We evaluate 5 state-of-the-art scale invariant region detectors and 5 descriptors. Local features are computed for 20 object classes and clustered using hierarchical agglomerative clustering. We measure the quality of appearance clusters and location distributions using entropy as well as precision. We also measure how the clusters generalize from training set to novel test data. Our results indicate that attended SIFT descriptors (Mikolajczyk and Schmid, 2005) computed on Hessian-Laplace regions perform best. Second score is obtained by salient regions (Kadir and Brady, 2001). The results also show that these two detectors provide complementary features. The new detectors/descriptors significantly improve the performance of a state-of-the art recognition approach (Leibe, et al., 2005) in pedestrian detection task
Evaluation of Features Detectors and Descriptors Based on 3D Objects
Authors: Pierre Moreels, Pietro Perona
Venue: ICCV
Year: 2005

We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. We collect a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting changes and scale changes. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25-30/spl deg/.
Integration of Conditionally Dependent Object Features for Robust Figure/Background Segmentation
Authors: Francesc Moreno-Noguer, Alberto Sanfeliu, Dimitris Samaras
Venue: ICCV
Year: 2005

We propose a new technique for focusing multiple cues to robustly segment an object from its background in video sequences that suffer from abrupt changes of both illumination and position of the target. Robustness is achieved by tile integration of appearance and geometric object features and by their description using particle filters. Previous approaches assume independence of the object cues or apply the particle filter formulation to only one of the features, and assume a smooth change in the rest, which can prove is very limiting, especially when the state of some features needs to be updated using other cues or when their dynamics follow non-linear and unpredictable paths. Our technique offers a general framework to model the probabilistic relationship between features. The proposed method is analytically justified and applied to develop a robust tracking system that adapts online and simultaneously the color space where the image points are represented, the color distributions, and the contour of the object. Results with synthetic data and real video sequences demonstrate the robustness and versatility of our method
Dynamic Refraction Stereo
Authors: Nigel J. W. Morris, Kiriakos N. Kutulakos
Venue: ICCV
Year: 2005

In this paper we consider the problem of reconstructing the 3D position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. The key requirements are: (1) knowledge of a function that maps each point on the two image planes to a known 3D point that refracts to it; and (2) light is refracted only once. We apply this result to the problem of reconstructing the time-varying surface of a liquid from patterns placed below it. To do this, we introduce a novel stereo matching criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. Results on reconstructing a variety of complex, deforming liquid surfaces suggest that our technique can yield detailed reconstructions that capture the dynamic behavior of free-flowing liquids
A Shape-Based Segmentation Approach: An Improved Technique Using Level Sets
Authors: Hossam El Din Hassan Abd El Munim, Aly A. Farag
Venue: ICCV
Year: 2005

We propose a novel approach for shape-based segmentation based on a specially designed level set function format. This format permits us to better control the process of object registration which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the partial differential equations (PDE's). Also it is suitable for multidimensional data and computationally efficient. Results of extracting the 2D star fish and the brain ventricles in 3D demonstrate the efficiency of the approach.
Mesh Optimization Using an Inconsistency Detection Template
Authors: Atsutada Nakatsuji, Yasuyuki Sugaya, Ken-ichi Kanatani
Venue: ICCV
Year: 2005

We propose a new technique for optimizing a triangular mesh for polyhedral representation of the scene in a video stream. We introduce a specially designed template that can effectively detect color and texture discontinuities. Using real images, we demonstrate that our method is superior to existing methods.
Improved Sub-pixel Stereo Correspondences through Symmetric Refinement
Authors: Diego Nehab, Szymon Rusinkiewicz, James Davis 0001
Venue: ICCV
Year: 2005

Most dense stereo correspondence algorithms start by establishing discrete pixel matches and later refine these matches to sub-pixel precision. Traditional sub-pixel refinement methods attempt to determine the precise location of points, in the secondary image, that correspond to discrete positions in the reference image. We show that this strategy can lead to a systematic bias associated with the violation of the general symmetry of matching cost functions. This bias produces random or coherent noise in the final reconstruction, but can be avoided by refining both image coordinates simultaneously, in a symmetric way. We demonstrate that the symmetric sub-pixel refinement strategy results in more accurate correspondences by avoiding bias while preserving detail.
Using Eye Reflections for Face Recognition Under Varying Illumination
Authors: Ko Nishino, Peter N. Belhumeur, Shree K. Nayar
Venue: ICCV
Year: 2005

Face recognition under varying illumination remains a challenging problem. Much progress has been made toward a solution through methods that require multiple gallery images of each subject under varying illumination. Yet for many applications, this requirement is too severe. In this paper, we propose a novel method that requires only a single gallery image per subject taken under unknown lighting. The method builds upon two contributions. We first estimate the lighting from its reflection in the eyes. This allows us to explicitly recover the illumination in the single gallery images as well as the probe image. Next, we exploit the local linearity of face appearance variation across different people. We represent the gallery images as locally linear montages of images of many different faces taken under the same lighting (bootstrap images). Then, we transfer the estimated combination of bootstrap images to synthesize each subject's face under tile probe lighting to accomplish recognition. Finally, we show through tests on the CMU PIE database that we can achieve better recognition results using our lighting estimation method and locally linear montages than the current state-of-the-art.
Shape and Appearance Repair for Incomplete Point Surfaces
Authors: Seyoun Park, Xiaohu Guo, Hayong Shin, Hong Qin 0001
Venue: ICCV
Year: 2005

This paper presents a new surface content completion framework that can restore both shape and appearance from scanned, incomplete point set inputs. First, the geometric holes can be robustly identified from noisy and defective data sets without the need of any normal or orientation information, using the method of active deformable models. The geometry and texture information of the holes can then be determined either automatically from the models' context, or semi-automatically with minimal users' intervention. The central idea for this repair process is to establish a quantitative similarity measurement among local surface patches based on their local parameterizations and curvature computation. The geometry and texture information of each hole can be completed by warping the candidate region and gluing it to the hole. The displacement for the alignment process is computed by solving a Poisson equation in 2D. Our experiments show that the unified framework, founded upon the techniques of deformable models, local parameterization, and PDE modeling, can provide a robust and elegant solution for content completion of defective, complex point surfaces.
Combining Image Regions and Human Activity for Indirect Object Recognition in Indoor Wide-Angle Views
Authors: Patrick Peursum, Geoff A. W. West, Svetha Venkatesh
Venue: ICCV
Year: 2005

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wide angle and low detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human interacts with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.
N-Dimensional Probablility Density Function Transfer and its Application to Colour Transfer
Authors: François Pitié, Anil C. Kokaram, Rozenn Dahyot
Venue: ICCV
Year: 2005

This article proposes an original method to estimate a continuous transformation that maps one N-dimensional distribution to another. The method is iterative, non-linear, and is shown to converge. Only 1D marginal distribution is used in the estimation process, hence involving low computation costs. As an illustration this mapping is applied to color transfer between two images of different contents. The paper also serves as a central focal point for collecting together the research activity in this area and relating it to the important problem of automated color grading
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis
Authors: Xuejie Qin, Yee-Hong Yang
Venue: ICCV
Year: 2005

In this paper, we present a new mathematical framework for modeling texture images using independent basic gray level aura matrices (BGLAMs). We prove that independent BGLAMs are the basis of gray level aura matrices (GLAMs), and that an image can be uniquely represented by its independent BGLAMs. We propose a new BGLAM distance measure for automatically evaluating synthesis results w.r.t. input textures to determine if the output is a successful synthesis of the input. For the application to texture synthesis, we present a new algorithm to synthesize textures by sampling only the independent BGLAMs of an input texture. With respect to synthesis of textures and evaluation of the results, the performance of our approach is extensively evaluated and compared with symmetric GLAMs that are used in existing techniques and with gray level cooccurrence matrices (GLCMs). Experimental results have shown that (1) our approach significantly outperforms both symmetric GLAMs and GLCMs; (2) the new BGLAM distance measure has the ability to evaluate synthesis results, which can be used to automate the conventional visual inspection process for determining whether or not the output texture is a successful synthesis of the input; and (3) a broad range of textures can be faithfully synthesized using independent BGLAMs and the synthesis results are comparable to existing techniques.
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
Authors: Xipeng Qiu, Lide Wu
Venue: ICCV
Year: 2005

Linear discriminant analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper; a new nonparametric linear feature extraction method, stepwise nonparametric margin maximum criterion (SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the non- singularity of the within-class scatter matrix neither. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression.
Real-Time Interactively Distributed Multi-Object Tracking Using a Magnetic-Inertia Potential Model
Authors: Wei Qu, Dan Schonfeld, Magdi A. Mohamed
Venue: ICCV
Year: 2005

This paper breaks with the common practice of using a joint state space representation and performing the joint data association in multi-object tracking. Instead, we present an interactively distributed framework with linear complexity for real-time applications. When objects do not interact on each other our approach performs like multiple independent trackers. When, the objects are in close proximity or present occlusions, we propose a magnetic-inertia potential model to handle the "error merge" and "labeling" problems in a particle filtering framework. Specifically we propose to model the interactive likelihood densities by a "gravitation" and "magnetic" repulsion scheme and relax the common first-order Markov chain assumption by using an "inertia" Markov chain. Our model represents the cumulative effect of virtual physical forces that objects undergo while interacting with others. It implicitly handles the "error merge" and "labeling" problems and thus solves the difficult object occlusion and data association problems using an innovative scheme. Our preliminary work has demonstrated that the proposed approach is far superior to existing methods not only in robustness but also in speed.