OpenRaiser/Intern-Atlas huggingface dataset:
3D Reconstruction by Fitting Low-Rank Matrices with Missing Data
Authors: Daniel Martinec, Tomás Pajdla
Venue: CVPR
Year: 2005

A technique for building consistent 3D reconstructions from many views based on fitting a low rank matrix to a matrix with missing data is presented. Rank-four submatrices of minimal, or slightly larger, size are sampled and spans of their columns are combined to constrain a basis of the fitted matrix. The error minimized is expressed in terms of the original subspaces which leads to a better resistance to noise compared to previous methods. More than 90% of the missing data can be handled while finding an acceptable solution efficiently. Applications to 3D reconstruction using both affine and perspective camera models are shown. For the perspective model, a new linear method based on logarithms of positive depths from chirality is introduced to make the depths consistent with an overdetermined set of epipolar geometries. Results are shown for scenes and sequences of various types. Many images in open and closed sequences in narrow and wide base-line setups are reconstructed with reprojection errors around one pixel. It is shown that reconstructed cameras can be used to obtain dense reconstructions from epipolarly aligned images.
Full-Frame Video Stabilization
Authors: Yasuyuki Matsushita, Eyal Ofek, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2005

Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. We propose a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. While most previous methods end up with producing low resolution stabilized videos, our completion method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. To achieve this, motion inpainting is proposed to enforce spatial and temporal consistency of the completion in both static and dynamic image areas. In addition, image quality in the stabilized video is enhanced with a new practical deblurring algorithm. Instead of estimating point spread functions, our method transfers and interpolates sharper image pixels of neighbouring frames to increase the sharpness of the frame. The proposed video completion and deblurring methods enabled us to develop a complete video stabilizer which can naturally keep the original image quality in the stabilized videos. The effectiveness of our method is confirmed by extensive experiments over a wide variety of videos.
Finding Glass
Authors: Kenton McHenry, Jean Ponce, David A. Forsyth
Venue: CVPR
Year: 2005

This paper addresses the problem of finding glass objects in images. Visual cues obtained by combining the systematic distortions in background texture occurring at the boundaries of transparent objects with the strong highlights typical of glass surfaces are used to train a hierarchy of classifiers, identify glass edges, and find consistent support regions for these edges. Qualitative and quantitative experiments involving a number of different classifiers and real images are presented.
Active Contours Using a Constraint-Based Implicit Representation
Authors: Bryan S. Morse, Weiming Liu 0007, Terry S. Yoo, Kalpathi R. Subramanian
Venue: CVPR
Year: 2005

We present a new constraint-based implicit active contour, which shares desirable properties of both parametric and implicit active contours. Like parametric approaches, their representation is compact and can be manipulated interactively. Like other implicit approaches, they can naturally adapt to nonsimple topologies. Unlike implicit approaches using level-set methods, representation of the contour does not require a dense mesh. Instead, it is based on specified on-curve and off-curve constraints, which are interpolated using radial basis functions. These constraints are evolved according to specified forces drawn from the relevant literature of both parametric and implicit approaches. This new type of active contour is demonstrated through synthetic images, photographs, and medical images with both simple and nonsimple topologies. For complex input, this approach produces results comparable to those of level set or parameterized finite-element active models, but with a compact analytic representation. As with other active contours they can also be used for tracking, especially for multiple objects that split or merge.
A SIFT Descriptor with Global Context
Authors: Eric N. Mortensen, Hongli Deng, Linda G. Shapiro
Venue: CVPR
Year: 2005

Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider global context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a global context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with global context to that without.
Shock Filters Based on Implicit Cluster Separation
Authors: Vinay P. Namboodiri, Subhasis Chaudhuri
Venue: CVPR
Year: 2005

One of the classic problems in low level vision is image restoration. An important contribution toward this effort has been the development of shock filters by Osher and Rudin (1990). It performs image deblurring using hyperbolic partial differential equations. In this paper we relate the notion of cluster separation from the field of pattern recognition to the shock filter formulation. A kind of shock filter is proposed based on the idea of gradient based separation of clusters. The proposed formulation is general enough as it can allow various models of density functions in the cluster separation process. The efficacy of the method is demonstrated through various examples.
Interactive Pinpoint Image Object Removal
Authors: Frank Nielsen, Richard Nock
Venue: CVPR
Year: 2005

We present a novel interactive system and its user interface for removing objects in digital pictures. Our system consists of two components: (i) (partially supervised/automatic) image segmentation, and (ii) (guided) texture synthesis.
Blob Segmentation Using Joint Space-Intensity Likelihood Ratio Test: Application to 3D Tumor Segmentation
Authors: Kazunori Okada, Umut Akdemir, Arun Krishnan
Venue: CVPR
Year: 2005

We propose a novel semi-automatic figure-ground segmentation solution for blob-like objects in multi-dimensional images. The blob-like structure constitutes various objects of interest that are hard to segment in many application domains, such as tumor lesions in 3D medical data. The proposed solution is motivated towards computer-aided diagnosis medical applications, justifying our semi-automatic and figure-ground approach. The efficient segmentation is realized by combining the robust anisotropic Gaussian model fitting and the likelihood ratio test (LRT)-based non-parametric segmentation in joint space-intensity domain. The robustly fitted Gaussian is exploited to estimate the foreground and background likelihoods for both spatial and intensity variables. We demonstrate that the LRT with the bootstrapped likelihoods is assured to be the optimal Bayesian classification while automatically determining the LRT threshold. A 3D implementation of the proposed algorithm is applied to the lung nodule segmentation in CT data and validated with 1310 cases. Our efficient solution segments a target nodule in less than 3 seconds in average.
Background Recognition in Dynamic Scenes with Motion Constraints
Authors: Kemal Egemen Ozden, Luc Van Gool
Venue: CVPR
Year: 2005

Consider a monocular image sequence which contains independently moving objects and assume it is already segmented. In order to get a realistic 3D reconstruction of such a scene, we have to solve the relative scale ambiguity between the reconstructions of different moving objects. Recently, we demonstrated the usefulness of the so-called 'non-accidentalness and independence constraints' to disambiguate the mentioned unknown relative scale. However, this technique requires that the video segment which corresponds to the background is known beforehand. In this paper, we analyze the background detection problem in the vein of the aforementioned constraints and show that the background is not just another moving object but the one which results in the simplest overall scene interpretation.
Interactive Montages of Sprites for Indexing and Summarizing Security Video
Authors: Chris Pal, Nebojsa Jojic
Venue: CVPR
Year: 2005

In this video we present a new model of interaction for indexing and visualizing video in the context of security applications. We present a method of indexing video by arranging irregularly shaped icons or sprites into a montage representing motion events or security events within the original video scene. The sprites in the montage are used as an index into the original video. We also generate video montages to summarize video in which motion events are compressed and overlayed in a video of shorter time duration. This summary video also acts as an index into the original video stream. We use a simple, novel method of extracting sprites for the image and video montages based on incrementally building a Gaussian mixture model with conjugate priors for the background. We then use fast morphological operators to extract foreground elements. Our approach can be viewed as a fast maximum a posteriori (MAP) inference procedure in a layered image model. The contributions of this work are new interaction and summary schemes that allow viewers to potentially survey hours of security video in the order of minutes.
Robust Instantaneous Rigid Motion Estimation
Authors: Karl Pauwels, Marc M. Van Hulle
Venue: CVPR
Year: 2005

A novel, nonlinear algorithm is introduced for the estimation of rigid camera motion from instantaneous velocity measurements in the calibrated case. It is shown that by minimizing an approximation to the image-reprojection error rather than the actual error, as done by the optimal algorithms, the proposed algorithm achieves largely increased robustness to local minima at the cost of a slightly decreased accuracy.
Geodesic Computation for Adaptive Remeshing
Authors: Gabriel Peyré, Laurent D. Cohen
Venue: CVPR
Year: 2005

This video presents an application of geodesic computation on 3D meshes to surface remeshing. The connectivity of the resulting mesh is computed using a geodesic Delaunay triangulation of the sampling points. The user can provide a speed function to conform the remeshing to various contraints such as curvature variation or texture gradient. This remeshing method is fast thanks to the use of the fast marching algorithm. It is simple to implement, robust and can serve as a basis building block for further processing of the surface such as segmentation or flattening.
Real-Time Non-Rigid Surface Detection
Authors: Julien Pilet, Vincent Lepetit, Pascal Fua
Venue: CVPR
Year: 2005

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC and we are not aware of any other published technique that produces similar results. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of up to 95%, which is considerably more than what is required in practice.
On the Absolute Quadratic Complex and Its Application to Autocalibration
Authors: Jean Ponce, Kenton McHenry, Théodore Papadopoulo, Monique Teillaud, Bill Triggs
Venue: CVPR
Year: 2005

This article introduces the absolute quadratic complex formed by all lines that intersect the absolute conic. If /spl omega/ denotes the 3 /spl times/ 3 symmetric matrix representing the image of that conic under the action of a camera with projection matrix P, it is shown that /spl omega/ /spl ap/ P/sup ~//spl Omega//sub /spl I.bar//P/sup ~T/ where V is the 3 /spl times/ 6 line projection matrix associated with P and /spl Omega//sub /spl I.bar// is a 6 /spl times/ 6 symmetric matrix of rank 3 representing the absolute quadratic complex. This simple relation between a camera's intrinsic parameters, its projection matrix expressed in a projective coordinate frame, and the metric upgrade separating this frame from a metric one - as respectively captured by the matrices /spl omega/, P/sup ~/ and /spl Omega//sub /spl I.bar// - provides a new framework for autocalibration, particularly well suited to typical digital cameras with rectangular or square pixels since the skew and aspect ratio are decoupled from the other intrinsic parameters in /spl omega/.
Statistical Cue Integration for Foveated Wide-Field Surveillance
Authors: Simon J. D. Prince, James H. Elder, Yuqian Hou, Mikhail Sizintsev, Yevgen Olevskiy
Venue: CVPR
Year: 2005

Reliable wide-field detection of human activity is an unsolved problem. The main difficulty is that low resolution and the unconstrained nature of realistic environments and human behaviour make form cues unreliable. Here we argue that reliability in far- or wide-field detection can still be achieved by probabilistic combination of multiple weak but complementary visual cues that do not depend on detailed form analysis. To demonstrate, we describe a real-time Bayesian algorithm for localizing human activity in relatively unconstrained scenes, using motion, background subtraction and skin colour cues. Fast sampling of scale space is achieved using integral images and a flexible norm that can handle sparse cues without loss of statistical power. We show that the probabilistic approach far outperforms a representative logical approach in which skin and background subtraction classifiers are combined conjunctively. Our method is currently used in a pre-attentive human activity sensor, generating saccadic targets for an attentive foveated vision system that reliably fixates faces over a 130 deg field of view, allowing high-resolution capture of facial images over a large dynamic scene.
Diagram Structure Recognition by Bayesian Conditional Random Fields
Authors: Yuan (Alan) Qi, Martin Szummer, Thomas P. Minka
Venue: CVPR
Year: 2005

Hand-drawn diagrams present a complex recognition problem. Elements of the diagram are often individually ambiguous, and require context to be interpreted. We present a recognition method based on Bayesian conditional random fields (BCRFs) that jointly analyzes all drawing elements in order to incorporate contextual cues. The classification of each object affects the classification of its neighbors. BCRFs allow flexible and correlated features, and take both spatial and temporal information into account. BCRFs estimate the posterior distribution of parameters during training, and average predictions over the posterior for testing. As a result of model averaging, BCRFs avoid the overfitting problems associated with maximum likelihood training. We also incorporate automatic relevance determination (ARD), a Bayesian feature selection technique, into BCRFs. The result is significantly lower error rates compared to ML- and MAP-trained CRFs.
RGB-Z: Mapping a Sparse Depth Map to a High Resolution RGB Camera Image
Authors: Abbas Rafii, Christopher J. Rossbach, Peter Zhao
Venue: CVPR
Year: 2005

The video stream from a commercial RGB camera is mapped to a semi-synchronous video stream of a time-of-flight Z-depth camera to produce a composite RGB-Z video stream. The combined image is rendered to show both the color and depth effects of the scene. Each pixel of the composite image provides both RGB encoding and Z depth in real-world coordinates. The data is useful for both machine vision and consumer imaging applications. The RGB brightness data provides detailed image of the scene including textures, colors and ambient light information. The time-of-flight depth camera provides additional information about the relative location and size of objects independent of texture and ambient-light condition. The demo shows several 3D rendering technique including wire-frame and color shading.
Learning Appearance Manifolds from Video
Authors: Ali Rahimi, Ben Recht, Trevor Darrell
Venue: CVPR
Year: 2005

The appearance of dynamic scenes is often largely governed by a latent low-dimensional dynamic process. We show how to learn a mapping from video frames to this low-dimensional representation by exploiting the temporal coherence between frames and supervision from a user. This function maps the frames of the video to a low-dimensional sequence that evolves according to Markovian dynamics. This ensures that the recovered low-dimensional sequence represents a physically meaningful process. We relate our algorithm to manifold learning, semi-supervised learning, and system identification, and demonstrate it on the tasks of tracking 3D rigid objects, deformable bodies, and articulated bodies. We also show how to use the inverse of this mapping to manipulate video.
Detecting, Localizing and Recovering Kinematics of Textured Animals
Authors: Deva Ramanan, David A. Forsyth, Kobus Barnard
Venue: CVPR
Year: 2005

We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape automatically from videos of animals and an appearance model of texture from a labeled collection of animal images, and combine the two models automatically. We develop a simple texture descriptor that outperforms the state of the art. We test our animal models on two datasets; images taken by professional photographers from the Corel collection, and assorted images from the Web returned by Google. We demonstrate quite good performance on both datasets. Comparing our results with simple baselines, we show that for the Google set, we can recognize objects from a collection demonstrably hard for object recognition.
Face Verification across Age Progression
Authors: Narayanan Ramanathan, Rama Chellappa
Venue: CVPR
Year: 2005

Human faces undergo considerable amount of variations with aging. While studies have revealed the extent to which factors such as illumination variations, pose variations, facial expression and occlusions affect face recognition, the role of natural factors such as aging effects in affecting the same are yet to be studied. How does age progression affect the similarity between two images of an individual? What is the confidence associated with establishing the identity between two age separated face images of an individual? On a database of pairs of passport images, we study similarity of faces as a function time. We propose a Bayesian age-difference classifier that is built on a probabilistic eigenspaces framework. Since age separated face images invariably differ in illumination and have facial variations due to aging, we propose a method to overcome non uniform illumination across face images. The problem discussed in this paper has direct applications in passport renewal and homeland security.
Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects
Authors: Yogesh Rathi, Namrata Vaswani, Allen R. Tannenbaum, Anthony J. Yezzi
Venue: CVPR
Year: 2005

Geometric active contours are formulated in a manner which is parametrization independent. As such, they are amenable to representation as the zero level set of the graph of a higher dimensional function. This representation is able to deal with singularities and changes in topology of the contour. It has been used very successfully in static images for segmentation and registration problems where the contour (represented as an implicit curve) is evolved until it minimizes an image based energy functional. But tracking involves estimating the global motion of the object and its local deformations as a function of time. Some attempts have been made to use geometric active contours for tracking, but most of these minimize the energy at each frame and do not utilize the temporal coherency of the motion or the deformation. On the other hand, tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape. But these are dependent on the chosen parametrization and cannot handle changes in curve topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects.
Dynamosaics: Video Mosaics with Non-Chronological Time
Authors: Alex Rav-Acha, Yael Pritch, Dani Lischinski, Shmuel Peleg
Venue: CVPR
Year: 2005

With the limited field of view of human vision, our perception of most scenes is built over time while our eyes are scanning the scene. In the case of static scenes, this process can be modeled by panoramic mosaicing: stitching together images into a panoramic view. Can a dynamic scene, scanned by a video camera, be represented with a dynamic panoramic video even though different regions were visible at different times? In this paper, we explore time flow manipulation in video, such as the creation of new videos in which events that occurred at different times are displayed simultaneously. More general changes in the time flow are also possible, which enable re-scheduling the order of dynamic events in the video, for example. We generate dynamic mosaics by sweeping the aligned space-time volume of the input video by a time front surface and generating a sequence of time slices in the process. Various sweeping strategies and different time front evolutions manipulate the time flow in the video, enabling many unexplored and powerful effects, such as panoramic movies.
Simultaneous Estimation of Segmentation and Shape
Authors: Jens Rittscher, Peter H. Tu, Nils Krahnstoever
Venue: CVPR
Year: 2005

The main focus of this work is the integration of feature grouping and model based segmentation into one consistent framework. The algorithm is based on partitioning a given set of image features using a likelihood function that is parameterized on the shape and location of potential individuals in the scene. Using a variant of the EM formulation, maximum likelihood estimates of both the model parameters and the grouping are obtained simultaneously. The resulting algorithm performs global optimization and generates accurate results even when decisions can not be made using local context alone. An important feature of the algorithm is that the number of people in the scene is not modeled explicitly. As a result no prior knowledge or assumed distributions are required. The approach is shown to be robust with respect to partial occlusion, shadows, clutter, and can operate over a large range of challenging view angles including those that are parallel to the ground plane. Comparisons with existing crowd segmentation systems are made and the utility of coupling crowd segmentation with a temporal tracking system is demonstrated.