OpenRaiser/Intern-Atlas huggingface dataset:
Invariant-based Registration of Surface Patches
Authors: Joris Vanden Wyngaerd, Luc Van Gool, Reinhard Koch, Marc Proesmans
Venue: ICCV
Year: 1999

3D shape models are often put together from several partial reconstructions. There are good algorithms available now to perform the necessary, precise registration automatically, but only after the partial reconstructions have been brought into approximate positions. This paper proposes a technique to do precisely that. Automatic 'crude registration' is illustrated for Euclidean and affine transformations between parts. The technique is based on the extraction and invariant characterisation of bitangent curve pairs. These can be found and matched efficiently. Registration of these curves yields the crude initialisation that more precise registration algorithms like ICP or mutual information maximisation can start from.
Estimation of Composite Object and Camera Image Motion
Authors: Yaser Yacoob, Larry S. Davis
Venue: ICCV
Year: 1999

An approach for estimating composite independent object and camera image motions is proposed. The approach employs spatio-temporal flow models learned through observing typical movements of the object, to decompose image motion into independent object and camera motions. The spatio-temporal flow models of the object motion are represented as a set of orthogonal flow bases that are learned using principal component analysis of instantaneous flow measurements from a stationary camera. These models are then employed in scenes with a moving camera to extract motion trajectories relative to those learned. The performance of the algorithm is demonstrated on several image sequences of rigid and articulated bodies in motion.
Tracking Rigid Motion using a Compact-Structure Constraint
Authors: Yaser Yacoob, Larry S. Davis
Venue: ICCV
Year: 1999

An approach for tracking the motion of a rigid object using parameterized flow models and a compact-structure constraint is proposed. While polynomial parameterized flow models have been shown to be effective in tracking the rigid motion of planar objects, these models are inappropriate for tracking moving objects that change appearance revealing their 3D structure. We extend these models by adding a structure-compactness constraint that accounts for image motion that deviates from a planar structure. The constraint is based on the assumption that object structure variations are limited with respect to planar object projection onto the image plane and therefore can be expressed as a direct constraint on the image motion. The performance of the algorithm is demonstrated on several long image sequences of rigidly moving objects.
Free-Form Surface Registration using Surface Signatures
Authors: Sameh M. Yamany, Aly A. Farag
Venue: ICCV
Year: 1999

The paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information seen from certain points and produces images called surface signatures at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose to use template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio, is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real time registration. We show that the signature representation can be used to match objects in 3D scenes in the presence of clutter and occlusion. Applications presented include free-form object matching, multimodal medical volume registration and dental teeth reconstruction from intra-oral images.
Multi-View Subspace Constraints on Homographies
Authors: Lihi Zelnik-Manor, Michal Irani
Venue: ICCV
Year: 1999

The motion of a planar surface between two camera views induces a homography. The homography depends on the camera intrinsic and extrinsic parameters, as well as on the 3D plane parameters. While camera parameters vary across different views, the plane geometry remains the same. Based on this fact, the paper derives linear subspace constraints on the relative motion of multiple (/spl ges/2) planes across multiple views. The paper has three main contributions. It shows that the collection of all relative homographies of a pair of planes (homologies) across multiple views, spans a 4-dimensional linear subspace. It shows how this constraint can be extended to the case of multiple planes across multiple views. It suggests two potential application areas which can benefit from these constraints: the accuracy of homography estimation can be improved by enforcing the multi-view subspace constraints; and violations of these multi-view constraints can be used as a cue for moving object detection. All the results derived in this paper are true for uncalibrated cameras.
What Can Be Determined from a Full and a Weak Perspective Image?
Authors: Zhengyou Zhang, P. Anandan 0001, Heung-Yeung Shum
Venue: ICCV
Year: 1999

This paper presents a first investigation on the structure from motion problem from the combination of full and weak perspective images. This problem arises in multiresolution object modeling where multiple zoomed-in or close-up views are combined with wider or distant reference views. The narrow field-of-view (FOV) images from the zoomed-in or closeup views can be approximated as weak perspective projection. Using a full perspective projection model for the narrow FOV images, although more accurate, actually leads to instabilities during the estimation process due to the non-linearities in the imaging model. The weak perspective approximation leads to more stable estimation algorithms, although at the cost of a small amount of modeling inaccuracy. Previous work in structure from motion focused either on two (or more) perspective images or on a set of weak perspective (more generally, affine) images. The main contribution of this paper is the study of the SFM problem for the much neglected case of one perspective and one (or more) weak perspective image. We show that in contrast to the case of a pair of weak perspective images, there is adequate information to recover Euclidean structure from a single perspective and a single weak perspective image. The epipolar geometry is simpler than with two perspective images leading to simpler and more stable estimation algorithms. Computer simulation shows that more stable results can be obtained with the technique presented in this paper than if two images are both considered to be full perspective.
High Dynamic Range Panoramic Imaging
Authors: Manoj Aggarwal, Narendra Ahuja
Venue: ICCV
Year: 2001

Most imaging sensors have a limited dynamic range and hence can satisfactorily respond to only a part of illumination levels present in a scene. This is particularly disadvantageous for omnidirectional and panoramic cameras since larger fields of view have larger brightness ranges. We propose a simple modification to existing high resolution omnidirectional/panoramic cameras in which the process of increasing the dynamic range is coupled with the process of increasing the field of view. This is achieved by placing a graded transparency (mask) in front of the sensor which allows every scene point to be imaged under multiple exposure settings as the camera pans, a process anyway required to capture large fields of view at high resolution. The sequence of images are then mosaiced to construct a high resolution, high dynamic range panoramic/omnidirectional image. Our method is robust to alignment errors between the mask and the sensor grid and does not require the mask to be placed on the sensing surface. We have designed a panoramic camera with the proposed modifications and have discussed various theoretical and practical issues encountered in obtaining a robust design. We show with an example of high resolution, high dynamic range panoramic image obtained from the camera we designed.
Split Aperture Imaging for High Dynamic Range
Authors: Manoj Aggarwal, Narendra Ahuja
Venue: ICCV
Year: 2001

Most imaging sensors have limited dynamic range and hence are sensitive to only a part of the illumination range present in a natural scene. The dynamic range can be improved by acquiring multiple images of the same scene under different exposure settings and then combining them. In this paper, we describe a camera design for simultaneously acquiring multiple images of the same scene under different exposure settings. The cross-section of the incoming beam from a scene point is partitioned into as many parts as the desired degree of split. This is done by splitting the aperture into multiple parts and directing the light exiting from each in a different direction using an assembly of mirrors. A sensor is placed in the path of each beam and exposure of each sensor is controlled either by appropriately setting its exposure parameter, or by splitting the incoming beam unevenly. The resulting multiple exposure images are used to construct a high dynamic range image. We have implemented a video-rate camera based on this design and the results obtained are presented.
On Cosine-Fourth and Vignetting Effects in Real Lenses
Authors: Manoj Aggarwal, Hong Hua, Narendra Ahuja
Venue: ICCV
Year: 2001

This paper has been prompted by observations of disparities between the observed fall-off in irradiance for off-axis points and that accounted for by the cosine-fourth and vignetting effects. A closer examination of the image formation process for real lenses revealed that even in the absence of vignetting a point light source does not uniformly illuminate the aperture, an effect known as pupil aberration. For example, we found the variation for a 16 mm lens to be as large as 31% for a field angle of 10/spl deg/. In this paper, we critically evaluate the roles of cosine-fourth and vignetting effects and demonstrate the significance of the pupil aberration on the fall-off in irradiance away from image center. The pupil aberration effect strongly depends on the aperture size and shape and this dependence has been demonstrated through two sets of experiments with three real lenses. The effect of pupil aberration is thus a third important cause of fall in irradiance away from the image center in addition to the familiar cosine-fourth and vignetting effects, that must be taken into account in applications that rely heavily on photometric variation such as shape from shading and mosaicing.
A Characterization of Inherent Stereo Ambiguities
Authors: Simon Baker, Terence Sim, Takeo Kanade
Venue: ICCV
Year: 2001

The complete set of measurements that could ever be used by a stereo algorithm is the plenoptic function or light-field. We give a concise characterization of when the light-field of a Lambertian scene uniquely determines its shape, and, conversely, when stereo is inherently ambiguous. We show that stereo computed from the complete light-field is ambiguous if and only if the scene is radiating light of a constant intensity (and color) over an extended region.
Confidence and Curvature Estimation of Curvilinear Structures in 3-D
Authors: Peter Bakker, Lucas J. van Vliet, Piet W. Verbeek
Venue: ICCV
Year: 2001

In this paper we present a new method for estimating confidence and curvature of 3-D curvilinear structures. The gradient structure tensor (GST) models shift-invariance. The eigenstructure of the tensor allows estimation of local dimensionality, orientation, and the corresponding confidence value. Local rotational invariance, which occurs often in images, causes a lower confidence estimate. This underestimation can be corrected for by a parabolic deformation of the data, in such a way that it becomes translational invariant. We show that the optimal deformation can be found analytically and yields a local curvature estimate as a valuable by-product. We tested our new method on synthetic images and applied it to the detection of channels in 3-D seismic delta.
A Variational Model for Filling-In Gray Level and Color Images
Authors: Coloma Ballester, Vicent Caselles, Joan Verdera, Marcelo Bertalmío, Guillermo Sapiro
Venue: ICCV
Year: 2001

A variational approach for filling-in regions of missing data in gray-level and color images is introduced in this paper. The approach is based on joint interpolation of the image gray-levels and gradient/isophores directions, smoothly extending in an automatic fashion the isophote lines into the holes of missing data. This interpolation is computed solving the variational problem via its gradient descent flow, which lends to a set of coupled second order partial differential equations, one for the gray-levels and one for the gradient orientations. The process underlying this approach can be considered as an interpretation of the Gestaltist's principle of good continuation. No limitations are imposed on the topology of the holes, and all regions of missing data can be simultaneously processed, even if they are surrounded by completely different structures. Applications of this technique include the restoration of old photographs and removal of superimposed text like dates, subtitles, or publicity. Examples of these applications are given.
Colour Photometric Stereo: Simultaneous Reconstruction of Local Gradient and Colour of Rough Textured Surfaces
Authors: Svetlana Barsky, Maria Petrou
Venue: ICCV
Year: 2001

Classification of a rough 3D surface from 2D images may be difficult due to directional effects introduced by illumination. One possible way of dealing with the problem is to extract the local albedo and gradient surface information which do not depend on the illumination, and classify the texture directly using these intrinsic characteristics. In this paper we present an algorithm for simultaneous recovery of local gradient and colour using multiple photometric images. The algorithm is proven to be optimal in the least squares error sense. Experimental results with real images and comparison with other approaches are also presented.
Projective Structure and Motion from Two Views of a Piecewise Planar Scene
Authors: Adrien Bartoli, Peter F. Sturm, Radu Horaud
Venue: ICCV
Year: 2001

In this paper, we address the problem of structure and motion recovery from two views of a scene containing planes, i.e, sets of coplanar points. Most of the existing works do only exploit this constraint in a sub-optimal manner We propose to parameterize the structure of such scenes with planes and points on planes and derive the MLE (Maximum Likelihood Estimator) using a minimal parameterization based on 2D entities. The result is the estimation of camera motion and 3D structure in projective space, that minimizes reprojection error while satisfying the piecewise planarity. We propose a quasi-linear estimator that provides reliable initialization values for plane equations. Experimental results show that the reconstruction is of clearly superior quality compared to traditional methods based only on points, even if the scene is not perfectly piecewise planar.
Lambertian Reflectance and Linear Subspaces
Authors: Ronen Basri, David W. Jacobs
Venue: ICCV
Year: 2001

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 the images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately with a low-dimensional 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 non-negative lighting functions.
Learning Local Evidence for Shading and Reflectance
Authors: Matt Bell, William T. Freeman
Venue: ICCV
Year: 2001

We address the important and unsolved problem of determining whether variations in image intensity are caused by changes in surface normal (shading) or reflectance (paint). A solution to this problem is necessary for machines to interpret images as people do and could have many applications. We take a learning-based approach. We generate a trainiiag set of synthetic images containing both surface normal and reflectance variations, and then label the variations at each position, scale, and orientation as to whether they are caused by shading or paint. The classification is done locally, using a feature vector of nonlinear filter responses. We fit a probability density model to the filter outputs using a mixture of factor analyzers. The resulting model indicates the probability based on local image evidence, that a pyramid coefficient at each orientation and scale is caused by shading or reflectance variations. Although the classification is done using a fixed lighting direction, we can solve for the correct lighting direction by rotating the image to the orientation, relative to the light source, that gives the most shape-like labelings. The labeling allows us to reconstruct two high passed images: one contains those parts of the input image caused by shading effects, while the other contains only those parts caused by reflectance changes. The resulting classifications compare well with human psychophysical performance on a test set of images, and show good results for test photographs.
Matching Shapes
Authors: Serge J. Belongie, Jitendra Malik, Jan Puzicha
Venue: ICCV
Year: 2001

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solving for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. Dis-similarity between two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework. Results are presented for silhouettes, trademarks, handwritten digits and the COIL dataset.
Affine Invariant Erosion of 3D Shapes
Authors: Santiago Betelú, Guillermo Sapiro, Allen R. Tannenbaum
Venue: ICCV
Year: 2001

A new definition of affine invariant erosion of 3D surfaces is introduced. Instead of being based in terms of Euclidean distances, the volumes enclosed between the surface and its chords are used. The resulting erosion is insensitive to noise, and by construction, it is affine invariant. We prove some key properties about this erosion operation, and we propose a simple method to compute the erosion of implicit surfaces. We also discuss how the affine erosion can be used to define 3D affine invariant robust skeletons.
Depth From Defocus in Presence of Partial Self Occlusion
Authors: Sundeep Singh Bhasin, Subhasis Chaudhuri
Venue: ICCV
Year: 2001

Contrary to the normal belief we show that self occlusion is present in any real aperture image and we present a method on how we can take care of the occlusion while recovering the depth using the defocus as the cue. The space-variant blur is modeled as an MRF and the MAP estimates are obtained for both the depth map and the everywhere focused intensity image. The blur kernel is adjusted in the regions where occlusion is present, particularly at the regions of discontinuities in the scene. The performance of the proposed algorithm is tested over synthetic data and the estimates are found to be better than the earlier schemes where such subtle effects were ignored.
Feature Selection from Huge Feature Sets
Authors: José Bins, Bruce A. Draper
Venue: ICCV
Year: 2001

The number of features that can be completed over an image is, for practical purposes, limitless. Unfortunately, the number of features that can be computed and exploited by most computer vision systems is considerably less. As a result, it is important to develop techniques for selecting features from very large data sets that include many irrelevant or redundant features. This work addresses the feature selection problem by proposing a three-step algorithm. The first step uses a variation of the well known Relief algorithm to remove irrelevance; the second step clusters features using K-means to remove redundancy; and the third step is a standard combinatorial feature selection algorithm. This three-step combination is shown to be more effective than standard feature selection algorithms for large data sets with lots of irrelevant and redundant features. It is also shown to he no worse than standard techniques for data sets that do not have these properties. Finally, we show a third experiment in which a data set with 4096 features is reduced to 5% of its original size with very little information loss.
Illumination Insensitive Eigenspaces
Authors: Horst Bischof, Horst Wildenauer, Ales Leonardis
Venue: ICCV
Year: 2001

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The major idea is to incorporate a set of gradient based filter banks into the eigenspace recognition framework. This can be achieved since the eigenimage coefficients are invariant for linearly filtered images (input and eigenimages). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 2160 images and the results were compared to other approaches.
Using Scene Constraints during the Calibration Procedure
Authors: Didier Bondyfalat, Théodore Papadopoulo, Bernard Mourrain
Venue: ICCV
Year: 2001

This paper focuses on the problem of calibration from a single view and a map of a scene. This situation arises quite often when modelling urban scenes, e.g. for augmented reality purposes. We show how some scenes constraints can be used to achieve a calibration like procedure. An example excerpted from a sequence of pictures for which self-calibration-like techniques consistently fail illustrates some of the benefits of the approach.