OpenRaiser/Intern-Atlas huggingface dataset:
Learning Object Detection from a Small Number of Examples: The Importance of Good Features
Authors: Kobi Levi, Yair Weiss
Venue: CVPR
Year: 2004

Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.
Linear Projection Methods in Face Recognition under Unconstrained Illuminations: A Comparative Study
Authors: Qi Li 0001, Jieping Ye, Chandra Kambhamettu
Venue: CVPR
Year: 2004

Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. P. Belhumer et al. (1996) presented a study on the comparison between principal component analysis (PCA) and subspace linear discriminant analysis (LDA) for this problem. PCA and subspace LDA are two well-known linear projection methods that can be characterized as trace optimization on scatter matrices. Generally, a linear projection method can be derived by applying a specific matrix analysis technique on specific scatter matrices under some optimization criterion. Several novel linear projection methods were proposed recently using generalized singular value decomposition or QR decomposition matrix analysis techniques [H. Park, et al., 2003], [J. Ye and Q. Li, 2004]. In this paper, we present a comparative study on these linear projection methods in FR/I. We further involve multiresolution analysis in the study. Our comparative study is expected to give a relatively comprehensive view on the performance of linear projection methods in FR/I problems.
Radiometric Calibration from a Single Image
Authors: Stephen Lin 0001, Jinwei Gu, Shuntaro Yamazaki, Heung-Yeung Shum
Venue: CVPR
Year: 2004

Photometric methods in computer vision require calibration of the camera's radiometric response, and previous works have addressed this problem using multiple registered images captured under different camera exposure settings. In many instances, such an image set is not available, so we propose a method that performs radiometric calibration from only a single image, based on measured RGB distributions at color edges. This technique automatically selects appropriate edge information for processing, and employs a Bayesian approach to compute the calibration. Extensive experimentation has shown that accurate calibration results can be obtained using only a single input image.
A Model for Dynamic Shape and Its Applications
Authors: Che-Bin Liu, Narendra Ahuja
Venue: CVPR
Year: 2004

Variation in object shape is an important visual cue for deformable object recognition and classification. In this paper, we present an approach to model gradual changes in the 2-D shape of an object. We represent 2-D region shape in terms of the spatial frequency content of the region contour using Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the shape changes. Specifically, we use autoregressive model of the coefficient series. We demonstrate the efficacy of the model on several applications. First, we use the model parameters as discriminating features for object recognition and classification. Second, we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence. Third, we show that, with its capability of predicting shape, the model can be used to predict contours of moving regions, which can be used as initial estimates for the contour, based tracking methods.
Studies on Silhouette Quality and Gait Recognition
Authors: Zongyi Liu, Laura Malave, Sudeep Sarkar
Venue: CVPR
Year: 2004

Recognition of a person from gait has been a focus in computer vision. It is one biometric source that can be acquired at a distance. At this nascent stage of gait recognition research, the pertinent research questions are those related to understanding the limits of gait recognition and the quantitative study of the various factors effecting gait. However, performances of contemporary algorithms have been confounded by errors in the extracted silhouettes, which has been the low-level representation of choice. In this work, (i) we present to the research community a segmentation "ground truth" research resource consisting of a set of manually specified part-level silhouettes for 70 subjects from the formulated gait challenge database, under different conditions involving change in surface, shoe-type, and time; a total of about 8000 manual silhouettes. (ii) We expound an HMM eigen stance model-based silhouette reconstruction method to correct for common errors in silhouette detection arising from shadows and background subtraction. And (iii) using these "cleaned" silhouettes and the manual silhouettes we show that the effects of various factors such as surface, time, and shoe on gait recognition are not due to poor silhouette quality. In fact, the recognition performance actually drops with the use of "clean " silhouettes because of removal of correlation in the error pixel patterns.
A GMM Parts Based Face Representation for Improved Verification through Relevance Adaptation
Authors: Simon Lucey, Tsuhan Chen
Venue: CVPR
Year: 2004

Motivated by the success of parts based representations in face detection we have attempted to address some of the problems associated with applying such a philosophy to the task of face verification. Hitherto, a major problem with this approach in face verification is the intrinsic lack of training observations, stemming from individual subjects, in order to estimate the required conditional distributions. The estimated distributions have to be generalized enough to encompass the differing permutations of a subject's face yet still be able to discriminate between subjects. In our work the well known Gaussian mixture model (GMM) framework is employed to model the conditional density function of the parts based representation of the face. We demonstrate that excellent performance can be obtained from our GMM based representation through the employment of adaptation theory, specifically relevance adaptation (RA). Our results are presented for the frontal images of the BANCA database.
High Resolution Video Mosaicing with Global Alignment
Authors: Roberto Marzotto, Andrea Fusiello, Vittorio Murino
Venue: CVPR
Year: 2004

Image composition (or mosaicing) has attracted a growing attention in recent years as one of the main elements in video analysis and representation. In this paper we deal with the problem of global alignment and super-resolution. We also propose to evaluate the quality of the resulting mosaic by measuring the amount of blurring. Global registration is achieved by combining a graph-based technique that exploits the topological structure of the sequence induced by the spatial overlap - with a bundle adjustment which uses only the homographies computed in the previous steps. Experimental comparison with other techniques shows the effectiveness of our approach.
Autocalibration & 3D Reconstruction with Non-Central Catadioptric Cameras
Authors: Branislav Micusík, Tomás Pajdla
Venue: CVPR
Year: 2004

We present a technique for modeling non-central catadioptric cameras consisting of perspective cameras and curved mirrors. The real catadioptric cameras have to be treated as non-central cameras, since they do not possess a single viewpoint. We present a method for solving the correspondence problem, auto-calibrating cameras, and computing a 3D metric reconstruction automatically from two uncalibrated non-central catadioptric images. The method is demonstrated on spherical, parabolic, and hyperbolic mirrors. We observed that the reconstruction & auto-calibration with non-central catadioptric cameras is as easy (or as difficult) as with central catadioptric cameras, provided that the correspondence problem can be solved with a suitable approximate central model. It turns out that it is the number of parameters of the camera model that matters rather than the exact centrality of the projection. Our technique allows to autocalibrate catadioptric cameras even with genuinely non-central mirrors such as spheres (simple model, low blur, easy to manufacture) or uniform resolution mirrors (optimized projection).
Elastic-String Models for Representation and Analysis of Planar Shapes
Authors: Washington Mio, Anuj Srivastava
Venue: CVPR
Year: 2004

We develop a new framework for the quantitative analysis of shapes of planar curves. Shapes are modeled on elastic strings that can be bent, stretched or compressed at different rates along the curve. Shapes are treated as elements of a space obtained as the quotient of an infinite-dimensional Riemannian manifold of elastic curves by the action of a reparameterization group. The Riemannian metric encodes the elastic properties of the string and has the property that reparameterizations act by isometrics. The geodesies in shape space are used to quantify shape dissimilarities, interpolate and extrapolate shapes, and align shapes according to their elastic properties. The shape spaces and metrics constructed offer a novel environment for the study of shape statistics and for the investigation and simulation of shape dynamics.
Motion-Based Background Subtraction Using Adaptive Kernel Density Estimation
Authors: Anurag Mittal, Nikos Paragios
Venue: CVPR
Year: 2004

Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.
An Unsupervised, Online Learning Framework for Moving Object Detection
Authors: Vinod Nair, James J. Clark
Venue: CVPR
Year: 2004

Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video. Motion information is used to automatically label training examples collected directly from the live detection task video. An online learner based on the Winnow algorithm incrementally trains a task-specific classifier with these examples. Since learning occurs online and without manual help, it can continue in parallel with detection and adapt the classifier over time. The framework is demonstrated on a person detection task for an office corridor scene. In this task, we use background subtraction to automatically label training examples. After the initial manual effort of implementing the labeling method, the framework runs by itself on the scene video stream to gradually train an accurate detector.
Dynamic Geodesic Snakes for Visual Tracking
Authors: Marc Niethammer, Allen R. Tannenbaum
Venue: CVPR
Year: 2004

Visual tracking using active contours is usually accomplished in a static framework. The active contour tracks the object of interest in a given frame of an image sequence, and then a subsequent prediction step ensures good initial placement for the next frame. This approach is unnatural, the curve evolution gets decoupled from the actual dynamics of the objects to be tracked. True dynamic approaches exist, all being marker particle based, and thus prone to the shortcomings of such particle-based implementations. In particular, topological changes are not handled naturally in this framework. The now "classical" level set approach is tailored for codimension one evolutions. However, dynamic curve evolution is at least of codimension two. We propose a natural, efficient, level set based approach for dynamic curve evolution which removes the artificial separation of segmentation and prediction, while retaining all the desirable properties of level set formulations. This is based on a new energy minimization functional which for the first time puts dynamics into the geodesic active contour framework.
Cue Integration through Discriminative Accumulation
Authors: Maria-Elena Nilsback, Barbara Caputo
Venue: CVPR
Year: 2004

Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for support vector machines (SVMs), but we also show that it is easily extendible to any large margin classifier. In the case of one-class SVMs the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.
A Minimal Solution to the Generalised 3-Point Pose Problem
Authors: David Nistér
Venue: CVPR
Year: 2004

It is a well known classical result that given the image projections of three known world points it is possible to solve for the pose of a calibrated perspective camera to up to four pairs of solutions. We solve the generalised problem where the camera is allowed to sample rays in some arbitrary but known fashion and is not assumed to perform a central perspective projection. That is, given three back-projected rays that emanate from a camera or multi-camera rig in an arbitrary but known fashion, we seek the possible poses of the camera such that the three rays meet three known world points. We show that the generalised problem has up to eight solutions that can be found as the intersections between a circle and a ruled quartic surface. A minimal and efficient constructive numerical algorithm is given to find the solutions. The algorithm derives an octic polynomial whose roots correspond to the solutions. In the classical case, when the three rays are concurrent, the ruled quartic surface and the circle possess a reflection symmetry such that their intersections come in symmetric pairs. This manifests itself in that the odd order terms of the octic polynomial vanish. As a result, up to four pairs of solutions can be found in closed form. The proposed algorithm can be used to solve for the pose of any type of calibrated camera or camera rig. The intended use for the algorithm is in a hypothesise-and-test architecture.
Visual Odometry
Authors: David Nistér, Oleg Naroditsky, James R. Bergen
Venue: CVPR
Year: 2004

We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time.
Grouping with Bias Revisited
Authors: Richard Nock, Frank Nielsen
Venue: CVPR
Year: 2004

In this paper, we improve and tailor a recent statistical region merging approach to biased (partially supervised) grouping. The approach appears to be attractive both for its theoretical benefits and its experimental results, as light bias brings dramatic improvements over unbiased approaches on difficult pictures. Comparisons with another biased grouping algorithm display very favorable results.
Towards Robust Structure-Based Enhancement and Horizon Picking in 3-D Seismic Data
Authors: Sean M. O&apos, Malley, Ioannis A. Kakadiaris
Venue: CVPR
Year: 2004

We present a novel structure-enhancing adaptive filter guided by features derived from the gradient structure tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for initializing an automatic horizon picking algorithm. In addition, our algorithm takes seismic attributes into consideration to reduce the possibilities of false horizon generation and fault crossing. Comparative experimental results are presented to highlight the potential of our approach.
Spherical Harmonics vs. Haar Wavelets: Basis for Recovering Illumination from Cast Shadows
Authors: Takahiro Okabe, Imari Sato, Yoichi Sato 0001
Venue: CVPR
Year: 2004

The problem of estimating an illumination distribution from images is called inverse lighting. For inverse lighting, three approaches have been developed based on specular reflection components, diffuse reflection components, and cast shadows. The present study provides theoretical insights as to why the approach based on cast shadows works in a reliable manner, and discusses what kind of basis functions are appropriate to be used for recovering illumination from cast shadows. First, we formalize the approach based on cast shadows by using spherical harmonics. Then, we analyze the approach in the frequency domain and show the advantages and the limitations of the approach. Second, motivated by the observations in the frequency domain, we propose an efficient method using Haar wavelets that provide compact supports and sparsity of coefficients. Finally, we report the results of experiments that compared the method using spherical harmonics and the method using Haar wavelets.
Scale Selection for Anisotropic Scale-Space: Application to Volumetric Tumor Characterization
Authors: Kazunori Okada, Dorin Comaniciu, Arun Krishnan
Venue: CVPR
Year: 2004

A unified approach for treating the scale selection problem in the anisotropic scale-space is proposed. The anisotropic scale-space is a generalization of the classical isotropic Gaussian scale-space by considering the Gaussian kernel with a fully parameterized analysis scale (bandwidth) matrix. The "maximum-over-scales" and the "most-stable-over-scales" criteria are constructed by employing the "L-normalized scale-space derivatives", i.e., response-normalized derivatives in the anisotropic scale-space. This extension allows us to directly analyze the anisotropic (ellipsoidal) shape of local structures. The main conclusions are (i) the norm of the /spl gamma/- and L-normalized anisotropic scale-space derivatives with a constant /spl gamma/ =1/2 are maximized regardless of the signal's dimension iff the analysis scale matrix is equal to the signal's covariance and (ii) the most-stable-over-scales criterion with the isotropic scale-space outperforms the maximum-over-scales criterion in the presence of noise. Experiments with 1D and 2D synthetic data confirm the above findings. 3D implementations of the most-stable-over-scales methods are applied to the problem of estimating anisotropic spreads of pulmonary tumors shown in high-resolution computed-tomography (HRCT) images. Comparison of the first- and second-order methods shows the advantage of exploiting the second-order information.
Multiview Occlusion Analysis for Tracking Densely Populated Objects Based on 2-D Visual Angles
Authors: Kazuhiro Otsuka, Naoki Mukawa
Venue: CVPR
Year: 2004

A novel framework of multiview occlusion analysis is presented for tracking densely populated objects moving on two-dimensional plane. This paper explicitly models the spatial structure of the occlusion process between objects and its uncertainty, based on 2D silhouette-based visual angles from fixed viewpoints. The occlusion structure is defined as tangency combination between the objects and the edges of the visual angles, based on geometric constraints inherent in the visual angles. The problem is then formulated as recursive Bayesian estimation consisting of hypothesis generation/testing of the occlusion structure and the estimation of posterior probability distribution for the object states including position and posture, on each hypothesis of the occlusion structure. For implementing the proposed framework, we develop a novel type of particle filter that supports multiple state distributions. Experiments using synthetic and real data show the robustness of the framework even in the face of severe occlusions.
Reconstructing 3D Independent Motions Using Non-Accidentalness
Authors: Kemal Egemen Ozden, Kurt Cornelis, Luc Van Eycken, Luc Van Gool
Venue: CVPR
Year: 2004

Reconstructing 3D scenes with independently moving objects from uncalibrated monocular image sequences still poses serious challenges. One important problem is to find the relative scales between these different reconstructed objects. The perspective reconstruction of a single object can only be known up to a certain scale which results in a one-parameter family of relative scales and trajectories per moving object. The paper formulates this ambiguity and proposes solutions in the vein of "non-accidentalness". Two instantiations of its use are analyzed: planar motion and the 'heading constraint'.