OpenRaiser/Intern-Atlas huggingface dataset:
How Do Movie Viewers Perceive Scene Structure from Dynamic Cues
Authors: Loong Fah Cheong, Xu Xiang
Venue: CVPR
Year: 2006

Cinema viewed from a location other than a Canonical Viewing Point (CVP) presents distortions to the viewer in both its static and dynamic aspects. Past works have investigated mainly the static aspect of this problem and attempted to explain why viewers still seem to perceive the scene very well. The dynamic aspect of depth perception, which is known as structure from motion, and its possible distortion, have not been well investigated. In this paper, we derive the dynamic depth cues perceived by the viewer and use the so-called isodistortion framework to understand its distortion. The result is that viewers seated at a reasonably central position experience a shift in the intrinsic parameters of their visual systems. Despite this shift, the key properties of the perceived depths remain largely the same, being determined in the main by the accuracy to which extrinsic motion parameters can be recovered. For a viewer seated at a non-central position and watching the movie screen with a slant angle, the view is related to the view at the CVP by a homography, resulting in various aberrations such as non-central projection.
3-D Shape Reconstruction of Retinal Fundus
Authors: Tae Eun Choe, Isaac Cohen, Gérard G. Medioni
Venue: CVPR
Year: 2006

We present a method for 3-D shape reconstruction of retinal fundus from fluorescein images. Our method extracts the location of vessels’ bifurcation as a reliable feature for estimating the epipolar geometry using a plane-and-parallax approach. The proposed solution robustly estimates the fundamental matrix for nearly planar surfaces, such as the retinal fundus. We propose the use of mutual information criteria for accurate estimation of the disparity maps, where the matched Y-features are used for automatically estimating the bounds of the disparities range space. Our experimental results validate the proposed method on sets of difficult fluorescein image pairs.
Element-Free Elastic Models for Volume Fitting and Capture
Authors: Jaeil Choi, Andrzej Szymczak, Greg Turk, Irfan A. Essa
Venue: CVPR
Year: 2006

We present a new method of fitting an element-free volumetric model to a sequence of deforming surfaces of a moving object. Given a sequence of visual hulls, we iteratively fit an element-free elastic model to the visual hull in order to extract the optimal pose of the captured volume. The fitting of the volumetric model is acheived by minimizing a combination of elastic potential energy, a surface distance measure, and a self-intersection penalty for each frame. A unique aspect of our work is that the model is mesh free - since the model is represented as a point cloud, it is easy to construct, manipulate and update the model as needed. Additionally, linear elasicity with rotation compensation makes it possible to handle local deformations and large rotations of body parts much more efficiently than other volume fitting approaches. Our experimental results for volume fitting and capture in a multi-view camera setting demonstrate the robustness of element-free elastic models against noise and self-occlusions.
Geometric Hashing with Local Affine Frames
Authors: Ondrej Chum, Jiri Matas
Venue: CVPR
Year: 2006

We propose a novel representation of local image structure and a matching scheme that are insensitive to a wide range of appearance changes. The representation is a collection of local affine frames that are constructed on outer boundaries of maximally stable extremal regions (MSERS) in an affine-covariant way. Each local affine frame is described by a relative location of other local affine frames in its neighborhood. The image is thus represented by quantities that depend only on the location of the boundaries of MSERs. Inter-image correspondences between local affine frames are formed in constant time by geometric hashing. Direct detection of local afine frames removes the requirement of a point-based hashing to establish reference frames in a combinatorial way, which has in the case of affine transform complexily that is cubic in the number of points. Local affine frames, which are also the quantities represented in the hash table, occupy a 6 0 space and hence data collisions are less likely compared with 2 0 point hashing. Experimentally, the robustness of the method and its insensitiviq to photometric changes is demonstrated on images from different spectral bands of satellite sensor; on images of a transparent object and on images of an object taken during day and night.
Fast Compact City Modeling for Navigation Pre-Visualization
Authors: Nico Cornelis, Kurt Cornelis, Luc Van Gool
Venue: CVPR
Year: 2006

Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.
Nonparametric Priors on the Space of Joint Intensity Distributions for Non-Rigid Multi-Modal Image Registration
Authors: Daniel Cremers, Christoph Guetter, Chenyang Xu 0001
Venue: CVPR
Year: 2006

The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms. In this work, we focus on the problem of nonrigid image registration. A number of powerful registration criteria have been developed in the last decade, most prominently the criterion of maximum mutual information. Although this criterion provides for good registration results in many applications, it remains a purely low-level criterion. As a consequence, registration results will deteriorate once this low-level information is corrupted, due to noise, partial occlusions or missing image structure. In this paper, we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods. The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs. This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations. Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions.
Bilayer Segmentation of Live Video
Authors: Antonio Criminisi, Geoffrey Cross, Andrew Blake 0001, Vladimir Kolmogorov
Venue: CVPR
Year: 2006

This paper presents an algorithm capable of real-time separation of foreground from background in monocular video sequences. Automatic segmentation of layers from colour/contrast or from motion alone is known to be error-prone. Here motion, colour and contrast cues are probabilistically fused together with spatial and temporal priors to infer layers accurately and efficiently. Central to our algorithm is the fact that pixel velocities are not needed, thus removing the need for optical flow estimation, with its tendency to error and computational expense. Instead, an efficient motion vs nonmotion classifier is trained to operate directly and jointly on intensity-change and contrast. Its output is then fused with colour information. The prior on segmentation is represented by a second order, temporal, Hidden Markov Model, together with a spatial MRF favouring coherence except where contrast is high. Finally, accurate layer segmentation and explicit occlusion detection are efficiently achieved by binary graph cut. The segmentation accuracy of the proposed algorithm is quantitatively evaluated with respect to existing groundtruth data and found to be comparable to the accuracy of a state of the art stereo segmentation algorithm. Foreground/ background segmentation is demonstrated in the application of live background substitution and shown to generate convincingly good quality composite video.
Fusion of Detection and Matching Based Approaches for Laser Based Multiple People Tracking
Authors: Jinshi Cui, Huijing Zhao, Ryosuke Shibasaki
Venue: CVPR
Year: 2006

Most of visual tracking algorithms have been achieved by matching-based searching strategies or detection-based data association algorithms. In this paper, our objective is to analysis laser scan image sequences to track multiple people in a crowded environment. Due to the poor features provided by laser scan images, neither of the above two approaches can achieves good tracking. To address the problem, we propose a novel multiple-target tracking algorithm fusing both detection and matching based strategies. First, target to detected measurement data association is incorporated to the joint state proposal, to form a mixture proposal that combines information from the dynamic model and the detected measurements. And then, we utilize a MCMC sampling step to obtain a more efficient multi-target filter. Our approach has been applied to the real laser scan image data. Evaluations show that the proposed method is a robust and effective multi-target tracking algorithm.
Using Bilinear Models for View-invariant Action and Identity Recognition
Authors: Fabio Cuzzolin
Venue: CVPR
Year: 2006

Human identification from gait is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are imaged by a single camera. In this paper, motivated by the view-invariance issue in the human ID from gait problem, we address the general problem of classifying the "content" of human motions of unknown "style". Given a dataset of sequences in which different people walking normally are seen from several wellseparated views, we propose a three-layer scheme based on bilinear models, in which image sequences are mapped to observation vectors of fixed dimension using Markov modeling. We test our approach on the CMU Mobo database, showing how bilinear separation outperforms other approaches, opening the way to view- and action-invariant identity recognition, as well as subject- and view-invariant action recognition.
Shape-Based Approach to Robust Image Segmentation using Kernel PCA
Authors: Samuel Dambreville, Yogesh Rathi, Allen R. Tannenbaum
Venue: CVPR
Year: 2006

Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, clutter, partial occlusions, or smearing.
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
Authors: Erick Delage, Honglak Lee, Andrew Y. Ng
Venue: CVPR
Year: 2006

When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.
Toward Optimal Kernel-based Tracking
Authors: Maneesh Dewan
Venue: CVPR
Year: 2006

The design and development of methods for tracking targets in visual images has developed rapidly in the past decade. However, in practice the design of tracking algorithms is still largely ad-hoc, based on trial and error. As a result, the performance of such algorithms can vary widely based on the properties of the target of interest and the choice of design. The use of spatial sampling kernels on multiple feature spaces has recently emerged as a promising approach to visual target tracking. In particular, it is possible to show that most popular tracking algorithms can be expressed within this framework. As a result, sampling kernels can be viewed as a flexible design space for tracking algorithms. However, in the current approaches, the kernels are placed in an adhoc fashion at the center of the target with a scale equal to the size of the target. This can lead to sub-optimal tracking results. In this paper, we present results pointing toward the design of optimal and approximately optimal target-specific tracking algorithms. The target tracking problem is formulated in terms of an optimization over a family of kernelbased sampling functions. This optimization is solved to produce an optimal target-specific kernel configuration. Experimental results show greatly improved performance over classical template tracking and naive kernel-based tracking.
Multi-Resolution Spin-Images
Authors: H. Quynh Dinh, Steven Kropac
Venue: CVPR
Year: 2006

Johnson and Hebert’s spin-images have been applied to the registration of range images and object recognition with much success because they are rotation, scale, and pose invariant. In this paper we address two issues concerning spin-images, namely: (1) comparing uncompressed spinimages across large datasets is costly, and (2) a method to select the appropriate bin size and image width for spinimages is not clearly defined. Our solution to these issues is a multi-resolution method that generates a pyramid of spin-images by successively decreasing the spin-image size by powers of two. To efficiently correlate surface points, we compare spin-images in a low-to-high resolution manner. Once multi-resolution spin-images are generated for a given object, we have found that the different resolutions can also be used to compare objects that have differing or non-uniform point densities. To select the appropriate bin sizes for comparing such objects, we use the ratio of the average edge lengths of the objects. We also show preliminary results of using the pyramid to converge on the appropriate image width by traversing the pyramid in a low-to-high resolution manner looking for the highest resolution at which the fewest number of highly correlated points are found to match a given feature point.
Supervised Learning of Edges and Object Boundaries
Authors: Piotr Dollár, Zhuowen Tu, Serge J. Belongie
Venue: CVPR
Year: 2006

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.
Efficient Maximally Stable Extremal Region (MSER) Tracking
Authors: Michael Donoser, Horst Bischof
Venue: CVPR
Year: 2006

This paper introduces a tracking method for the well known local MSER (Maximally Stable Extremal Region) detector. The component tree is used as an efficient data structure, which allows the calculation of MSERs in quasi-linear time. It is demonstrated that the tree is able to manage the required data for tracking. We show that by means of MSER tracking the computational time for the detection of single MSERs can be improved by a factor of 4 to 10. Using a weighted feature vector for data association improves the tracking stability. Furthermore, the component tree enables backward tracking which further improves the robustness. The novel MSER tracking algorithm is evaluated on a variety of scenes. In addition, we demonstrate three different applications, tracking of license plates, faces and fibers in paper, showing in all three scenarios improved speed and stability.
Improving Border Localization of Multi-Baseline Stereo Using Border-Cut
Authors: Marc-Antoine Drouin, Martin Trudeau, Sébastien Roy 0001
Venue: CVPR
Year: 2006

This paper presents a novel algorithm that improves the localization of disparity discontinuities of disparity maps obtained by multi-baseline stereo. Rather than associating a disparity label to every pixel of a disparity map, it associates a position to every disparity discontinuity. This formulation allows us to find an approximate solution to a 2D labeling problem with robust smoothing term by minimizing multiple 1D problems, thus making possible the use of dynamic programming. Dynamic programming allows the efficient computation of the visibility of most of the cameras during the minimization. Whilst the proposed minimization strategy is particularly suitable for stereo with occlusion, it may be used with other labeling problems.
Image Denoising Via Learned Dictionaries and Sparse representation
Authors: Michael Elad, Michal Aharon
Venue: CVPR
Year: 2006

We address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse and redundant representations over a trained dictionary. The proposed algorithm denoises the image, while simultaneously trainining a dictionary on its (corrupted) content using the K-SVD algorithm. As the dictionary training algorithm is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm, with state-of-the-art performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
Image Denoising with Shrinkage and Redundant Representations
Authors: Michael Elad, Boaz Matalon, Michael Zibulevsky
Venue: CVPR
Year: 2006

Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal’s representation is enforced using a unitary transform. Still, shrinkage is also practiced successfully with nonunitary, and even redundant representations. In this paper we shed some light on this behavior. We show that simple shrinkage could be interpreted as the first iteration of an algorithm that solves the basis pursuit denoising (BPDN) problem. Thus, this work leads to a novel iterative shrinkage algorithm that can be considered as an effective pursuit method. We demonstrate this algorithm, both on synthetic data, and for the image denoising problem, where we learn the image prior parameters directly from the given image. The results in both cases are superior to several popular alternatives. 1
Learning Object Shape: From Drawings to Images
Authors: Gal Elidan, Geremy Heitz, Daphne Koller
Venue: CVPR
Year: 2006

We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of "outlining" an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for hand-segmented training images. In this paper we present a novel landmark-based, piecewise-linear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for hand-segmentation by transferring the shape "essence" of an object from drawings to complex images. We show that our method is able to automatically and effectively learn and localize a variety of object classes.
Satellite Features for the Classification of Visually Similar Classes
Authors: Boris Epshtein, Shimon Ullman
Venue: CVPR
Year: 2006

We show that the discrimination between visually similar classes often depends on the detection of socalled ‘satellite features’. These are local features which are not informative by themselves, and can only be detected reliably at locations specified relative to other features. This makes satellite features difficult to extract by current classification methods. We describe a novel scheme which can extract discriminative satellite features and use them to distinguish between visually similar classes. The algorithm first searches for a set of features ("anchor features") that can be found in all the similar classes. Such features can be detected because the classes are visually similar. The anchors are used to determine the locations of satellite features, which are extracted during learning and used in classification to distinguish between the similar classes. The algorithm is fully automatic, and is shown to work well for many categories of visually similar classes.