OpenRaiser/Intern-Atlas huggingface dataset:
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
Authors: Alaa E. Abdel-Hakim, Aly A. Farag
Venue: CVPR
Year: 2006

SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.
The Panum Proxy Algorithm for Dense Stereo Matching over a Volume of Interest
Authors: Ankur Agarwal, Andrew Blake 0001
Venue: CVPR
Year: 2006

Stereo matching algorithms conventionally match over a range of disparities sufficient to encompass all visible 3D scene points. Human vision however does not do this. It works over a narrow band of disparities - Panum’s fusional band - whose typical range may be as little as 1/20 of the full range of disparities for visible points. Points inside the band are fused visually and the remainder of points are seen as "diplopic" - that is with double vision. The Panum band restriction is important also in machine vision, both with active (pan/tilt) cameras, and with high resolution cameras and digital pan/tilt.
A New Formulation for Shape from Shading for Non-Lambertian Surfaces
Authors: Abdelrehim H. Ahmed, Aly A. Farag
Venue: CVPR
Year: 2006

Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are presented to show the efficiency of the proposed method . To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.
A Planar Light Probe
Authors: Neil Gordon Alldrin, David J. Kriegman
Venue: CVPR
Year: 2006

We develop a novel technique for measuring lighting that exploits the interaction of light with a set of custom BRDFs. This enables the construction of a planar light probe with certain advantages over existing methods for measuring lighting. To facilitate the construction of our light probe, we derive a new class of bi-directional reflectance functions based on the interaction of light through two planar surfaces separated by a transparent medium. Under certain assumptions and proper selection of the two surfaces, we show how to recover Fourier series coefficients of the incident lighting parameterized over the plane. The results are experimentally validated by imaging a sheet of glass with spatially varying patterns printed on either side.
Off-road Path Following using Region Classification and Geometric Projection Constraints
Authors: Yaniv Alon, Andras Ferencz, Amnon Shashua
Venue: CVPR
Year: 2006

We describe a realtime system for finding and tracking unstructured paths in off-road conditions. The system was designed as part of the recent Darpa Grand Challenge and was tested over hundreds of miles of off-road driving. The unique feature of our approach is to combine geometric projection used for recovering Pitch and Yaw with Learning approaches for identifying familiar "drivable" regions in the scene. The region-based component segments the image to "path" and "non-path" regions based on texture analysis borne out of a learning-by-examples principle. The boundary-based component looks for the path bounding lines assuming a geometric model of a planar pathway bounded by parallel edges taken by a perspective camera. The combined effect of both sub-systems forms a robust system capable of finding the path even in situations where the vehicle is positioned out of the path - a situation which is not common for human drivers but is relevant for autonomous driving where the vehicle may find itself occasionally veering out of the path.
Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models
Authors: Alphan Altinok, Motaz A. El Saban, Austin J. Peck, Leslie Wilson, Stuart C. Feinstein, B. S. Manjunath, Kenneth Rose
Venue: CVPR
Year: 2006

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.
Automatic Cast Listing in Feature-Length Films with Anisotropic Manifold Space
Authors: Ognjen Arandjelovic, Roberto Cipolla
Venue: CVPR
Year: 2006

Our goal is to automatically determine the cast of a feature-length film. This is challenging because the cast size is not known, with appearance changes of faces caused by extrinsic imaging factors (illumination, pose, expression) often greater than due to differing identities. The main contribution of this paper is an algorithm for clustering over face appearance manifolds. Specifically: (i) we develop a novel algorithm for exploiting coherence of dissimilarities between manifolds, (ii) we show how to estimate the optimal dataset-specific discriminant manifold starting from a generic one, and (iii) we describe a fully automatic, practical system based on the proposed algorithm. The performance of the system is evaluated on well-known featurelength films and situation comedies on which it is shown to produce good results.
Epipolar Geometry of Central Projection Systems Using Veronese Maps
Authors: João Pedro Barreto 0001, Kostas Daniilidis
Venue: CVPR
Year: 2006

We study the epipolar geometry between views acquired by mixtures of central projection systems including catadioptric sensors and cameras with lens distortion. Since the projection models are in general non-linear, a new representation for the geometry of central images is proposed. This representation is the lifting through Veronese maps of the image plane to the 5D projective space. It is shown that, for most sensor combinations, there is a bilinear form relating the lifted coordinates of corresponding image points. We analyze the properties of the embedding and explicitly construct the lifted fundamental matrices in order to understand their structure. The usefulness of the framework is illustrated by estimating the epipolar geometry between images acquired by a paracatadioptric system and a camera with radial distortion.
Affine Invariance Revisited
Authors: Evgeni Begelfor, Michael Werman
Venue: CVPR
Year: 2006

This paper proposes a Riemannian geometric framework to compute averages and distributions of point configurations so that different configurations up to affine transformations are considered to be the same. The algorithms are fast and proven to be robust both theoretically and empirically. The utility of this framework is shown in a number of affine invariant clustering algorithms on image point data.
A General Framework and New Alignment Criterion for Dense Optical Flow
Authors: Rami Ben-Ari, Nir A. Sochen
Venue: CVPR
Year: 2006

The problem of dense optical flow computation is addressed from a variational viewpoint. A new geometric framework is introduced. It unifies previous art and yields new efficient methods. Along with the framework a new alignment criterion suggests itself. It is shown that the alignment between the gradients of the optical flow components and between the latter and the intensity gradients is an important measure of the flow’s quality. Adding this criterion as a requirement in the optimization process improves the resulting flow. This is demonstrated in synthetic and real sequences.
Animals on the Web
Authors: Tamara L. Berg, David A. Forsyth
Venue: CVPR
Year: 2006

We demonstrate a method for identifying images containing categories of animals. The images we classify depict animals in a wide range of aspects, configurations and appearances. In addition, the images typically portray multiple species that differ in appearance (e.g. ukari’s, vervet monkeys, spider monkeys, rhesus monkeys, etc.). Our method is accurate despite this variation and relies on four simple cues: text, color, shape and texture. Visual cues are evaluated by a voting method that compares local image phenomena with a number of visual exemplars for the category. The visual exemplars are obtained using a clustering method applied to text on web pages. The only supervision required involves identifying which clusters of exemplars refer to which sense of a term (for example, "monkey" can refer to an animal or a bandmember). Because our method is applied to web pages with free text, the word cue is extremely noisy. We show unequivocal evidence that visual information improves performance for our task. Our method allows us to produce large, accurate and challenging visual datasets mostly automatically.
Piecewise Image Registration in the Presence of Multiple Large Motions
Authors: Pravin Bhat, Ke Colin Zheng, Noah Snavely, Aseem Agarwala, Maneesh Agrawala, Michael F. Cohen, Brian Curless
Venue: CVPR
Year: 2006

We present a technique for computing a dense pixel correspondence between two images of a scene containing multiple large, rigid motions. We model each motion with either a homography (for planar objects) or a fundamental matrix. The various motions in the scene are first extracted by clustering an initial sparse set of correspondences between feature points; we then perform a multi-label graph cut optimization which assigns each pixel to an independent motion and computes its disparity with respect to that motion. We demonstrate our technique on several example scenes and compare our results with previous approaches.
Classifying Human Dynamics Without Contact Forces
Authors: Alessandro Bissacco, Stefano Soatto
Venue: CVPR
Year: 2006

We develop a classification algorithm for hybrid autoregressive models of human motion for the purpose of videobased analysis and recognition. We assume that some temporal statistics are extracted from the images, and we use them to infer a dynamical system that explicitly models contact forces. We then develop a distance between such models that explicitly factors out exogenous inputs that are not unique to an individual or her gait. We show that such a distance is more discriminative than the distance between simple linear systems, where most of the energy is devoted to modeling the dynamics of spurious nuisances such as contact forces.
Shape Guided Object Segmentation
Authors: Eran Borenstein, Jitendra Malik
Venue: CVPR
Year: 2006

We construct a Bayesian model that integrates topdown with bottom-up criteria, capitalizing on their relative merits to obtain figure-ground segmentation that is shape-specific and texture invariant. A hierarchy of bottom-up segments in multiple scales is used to construct a prior on all possible figure-ground segmentations of the image. This prior is used by our top-down part to query and detect object parts in the image using stored shape templates. The detected parts are integrated to produce a global approximation for the object’s shape, which is then used by an inference algorithm to produce the final segmentation. Experiments with a large sample of horse and runner images demonstrate strong figure-ground segmentation despite high object and background variability. The segmentations are robust to changes in appearance since the matching component depends on shape criteria alone. The model may be useful for additional visual tasks requiring labeling, such as the segmentation of multiple scene objects.
Modeling and Classifying Breast Tissue Density in Mammograms
Authors: Anna Bosch, Xavier Muñoz, Arnau Oliver, Joan Martí
Venue: CVPR
Year: 2006

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal.
Image Pre-Conditioning for Out-of-Focus Projector Blur
Authors: Michael S. Brown, Peng Song 0010, Tat-Jen Cham
Venue: CVPR
Year: 2006

We present a technique to reduce image blur caused by out-of-focus regions in projected imagery. Unlike traditional restoration algorithms that operate on a blurred image to recover the original, the nature of our problem requires that the correction be applied to the original image before blurring. To accomplish this, a camera is used to estimate a series of spatially varying point-spread-functions (PSF) across the projector’s image. These discrete PSFs are then used to guide a pre-processing algorithm based on Wiener filtering to condition the image before projection. Results show that using this technique can help ameliorate the visual effects from out-of-focus projector blur.
Interactive Feature Tracking using K-D Trees and Dynamic Programming
Authors: Aeron Buchanan, Andrew W. Fitzgibbon
Venue: CVPR
Year: 2006

A new approach to template tracking is presented, incorporating three distinct contributions. Firstly, an explicit definition for a feature track is given. Secondly, the advantages of an image preprocessing stage are demonstrated and, in particular, the effectiveness of highly compressed image patch data stored in k-d trees for fast and discriminatory image patch searches. Thirdly, the k-d trees are used to generate multiple track hypotheses which are efficiently merged to give the optimal solution using dynamic programming. The explicit separation of feature detection and trajectory determination creates the basis for the novel use of k-d trees and dynamic programming. Multiple appearances and occlusion handling are seamlessly integrated into this framework. Appearance variation through the sequence is robustly handled in an iterative process. The work presented is a significant foundation for a powerful off-line feature tracking system, particularly in the context of interactive applications.
"How many planar viewing surfaces are there in noncentral catadioptric cameras?" Towards singe-image localization of space lines
Authors: Vincenzo Caglioti, Simone Gasparini
Venue: CVPR
Year: 2006

In-door environments often contain several straight line segments. The 3D reconstruction of such environments can thus reduce to the localization of lines in the 3D space. Multi-view reconstruction requires the solution of the correspondence problem. The use of a single image to localize space lines is attractive, since the correspondence problem can be avoided. However, using a perspective camera (or a central one), a line can not be localized, since its viewing surface is planar, and hence it can contain infinite lines other than the correct one. In this paper we study the number of planar viewing surfaces for a general class of catadioptric cameras, constituted by an axial symmetric mirror and a perspective camera placed at generic relative position. We show that, under broad conditions, there is only a discrete set of planar viewing surfaces for the considered class of cameras. This result establishes a qualitative difference with respect to axial-symmetric cameras (e.g., catadioptric cameras constituted by an axial-symmetric mirror plus a perspective camera, whose viewpoint is constrained to be on the mirror axis), where an infinite set of planar viewing surfaces exists. Then, some conditions are derived for the localization of lines in the 3D space from single images. Preliminary experiments are also reported.
Structure from Motion with Known Camera Positions
Authors: Rodrigo L. Carceroni, Ankita Kumar, Kostas Daniilidis
Venue: CVPR
Year: 2006

The wide availability of GPS sensors is changing the landscape in the applications of structure from motion techniques for localization. In this paper, we study the problem of estimating camera orientations from multiple views, given the positions of the viewpoints in a world coordinate system and a set of point correspondences across the views. Given three or more views, the above problem has a finite number of solutions for three or more point correspondences. Given six or more views, the problem has a finite number of solutions for just two or more points. In the three-view case, we show the necessary and sufficient conditions for the three essential matrices to be consistent with a set of known baselines. We also introduce a method to recover the absolute orientations of three views in world coordinates from their essential matrices. To refine these estimates we perform a least-squares minimization on the group cross product SO(3) × SO(3) × SO(3). We report experiments on synthetic data and on data from the ICCV2005 Computer Vision Contest.
Vertical Parallax from Moving Shadows
Authors: Yaron Caspi, Michael Werman
Venue: CVPR
Year: 2006

This paper presents a method for capturing and computing 3D parallax. 3D parallax, as used here, refers to vertical offset from the ground plane, height. The method is based on analyzing shadows of vertical poles (e.g., a tall building’s contour) that sweep the object. Unlike existing beam-scanning approaches, such as shadow or structured light, that recover the distance of a point from the camera, our approach measures the height from the ground plane directly. Previous methods compute the distance from the camera using triangulation between rays outgoing from the light-source and the camera. Such a triangulation is difficult when the objects are far from the camera, and requires accurate knowledge of the light source position. In contrast, our approach intersects two (unknown) planes generated separately by two casting objects. This omits the need to precompute the location of the light source. Furthermore, it allows a moving light source to be used. The proposed setup is particularly useful when the camera cannot directly face the scene or when the object is far away from the camera. A good example is an urban scene captured by a single webcam.
Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling
Authors: Frédéric Champagnat, Guy Le Besnerais, Caroline Kulcsar
Venue: CVPR
Year: 2006

We consider the recovery of 1-D image features. Such features can be described by a noisy, blurred and undersampled image of a unique 1-D profile. The profiles recovery is modeled as a 1-D continuous super-resolution (SR) problem. We adopt a functional estimation within a Tikhonov regularization framework. A linear closed-form solution is derived and applied to real data for bar code recovery from low-resolution video frames. Performance modeling in then considered. Thanks to a continuous stochastic model of the input profile, we define a quantitative performance measure which is a mean-square error averaged over a class of profiles with tunable regularity. As a result, an expected SR resolution enhancement ratio is computed, which depends on experimental parameters: SNR, number of input images, sampling rate. A good agreement is found between this theoretical study and empirical performance in experimental SR recovery of bar code profiles.
Searching Off-line Arabic Documents
Authors: Jim Chan, Celal Ziftci, David A. Forsyth
Venue: CVPR
Year: 2006

Currently an abundance of historical manuscripts, journals, and scientific notes remain largely unaccessible in library archives. Manual transcription and publication of such documents is unlikely, and automatic transcription with high enough accuracy to support a traditional text search is difficult. In this work we describe a lexicon-free system for performing text queries on off-line printed and handwritten Arabic documents. Our segmentation-based approach utilizes gHMMs with a bigram letter transition model, and KPCA/LDA for letter discrimination. The segmentation stage is integrated with inference. We show that our method is robust to varying letter forms, ligatures, and overlaps. Additionally, we find that ignoring letters beyond the adjoining neighbors has little effect on inference and localization, which leads to a significant performance increase over standard dynamic programming. Finally, we discuss an extension to perform batch searches of large word lists for indexing purposes.
Scale-Driven Iterative Optimization for Brain Extraction and Registration
Authors: Terrence Chen, Thomas S. Huang
Venue: CVPR
Year: 2006

We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.