OpenRaiser/Intern-Atlas huggingface dataset:
3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions
Authors: Rómer Rosales, Stan Sclaroff
Venue: CVPR
Year: 1999

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliable segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.
Color Edge Detection with the Compass Operator
Authors: Mark A. Ruzon, Carlo Tomasi
Venue: CVPR
Year: 1999

The compass operator detects step edges without assuming that the regions on either side have constant color. Using distributions of pixel colors rather than the mean, the operator finds the orientation of a diameter that maximizes the difference between two halves of a circular window. Junctions can also be detected by exploiting their lack of bilateral symmetry. This approach is superior to a multi-dimensional gradient method in situations that often result in false negatives, and it localizes edges better as scale increases.
Shape Reconstruction in Projective Grid Space from Large Number of Images
Authors: Hideo Saito 0001, Takeo Kanade
Venue: CVPR
Year: 1999

This paper proposes a new scheme for multi-image projective reconstruction based on a projective grid space. The projective grid space is defined by two basis views and the fundamental matrix relating these views. Given fundamental matrices relating other views to each of the two basis views, this projective grid space can be related to any view. In the projective grid space as a general space that is related to all images, a projective shape can be reconstructed from all the images of weakly calibrated cameras. The projective reconstruction is one way to reduce the effort of the calibration because it does not need Euclid metric information, but rather only correspondences of several points between the images. For demonstrating the effectiveness of the proposed projective grid definition, we modify the voxel coloring algorithm for the projective voxel scheme. The quality of the virtual view images re-synthesized from the projective shape demonstrates the effectiveness of our proposed scheme for projective reconstruction from a large number of images.
Simultaneous Image Classification and Restoration Using a Variational Approach
Authors: Christophe Samson, Laure Blanc-Féraud, Josiane Zerubia, Gilles Aubert
Venue: CVPR
Year: 1999

Herein, we present a variational model devoted to image classification coupled with an edge-preserving regularization process. In the last decade, the variational approach has proven its efficiency in the field of edge-preserving restoration. In this paper, we add a classification capability which contributes to provide images compound of homogeneous regions with regularized boundaries. The soundness of this model is based on the works developed on the phase transition theory in mechanics. The proposed algorithm is fast, easy to implement and efficient. We compare our results on both synthetic and satellite images with the ones obtained by a stochastic model using a Potts regularization.
The Discriminatory Power of Ordinal Measures - Towards a New Coefficient
Authors: Stefan Scherer, Axel Pinz, Peter Werth
Venue: CVPR
Year: 1999

Perspective distortion, occlusion and specular reflection are challenging problems in shape-from-stereo. In this paper we review one recently published area-based stereo matching algorithm (Bhat and Nayar, 1998) designed to be robust in these cases. Although the algorithm is an important contribution to stereo-matching, we show that its coefficient has a low discriminatory power, which leads to a significant number of multiple best matches. In order to cope with this drawback we introduce a new normalized ordinal correlation coefficient. Experiments showing the behavior of the proposed coefficient are performed on various datasets including real data with ground truth. The new coefficient reduces the occurrence of multiple best matches to almost zero per cent. It also shows a more robust and equally accurate behavior. These benefits are achieved at almost no additional computational costs.
Torque-Based Recursive Filtering Approach to the Recovery of 3D Articulated Motion from Image Sequences
Authors: Hiroyuki Segawa, Takashi Totsuka
Venue: CVPR
Year: 1999

In this paper we introduce a recursive filtering method to recover the 3D articulated motion from image sequences. In recursive filtering frameworks, the quality of the results heavily depends on the choice of state variables and the determination of the process model; which models a real object whose motion is to be estimated. Our approach employs robotics dynamics into the recursive filtering framework. And the key strategy is to incorporate joint torques into the model state variables. In addition, we assumed the variations of the joint torques are Gaussian noises. We describe how to integrate dynamics equations into Kalman filters, and with the experimental results our method is shown to be effective.
Shadow Gestures: 3D Hand Pose Estimation Using a Single Camera
Authors: Jakub Segen, Senthil Kumar
Venue: CVPR
Year: 1999

This paper describes a system that uses a camera and a point light source to track a user's hand in three dimensions. Using depth cues obtained from projections of the hand and its shadow, the system computes the 3D position and orientation of two fingers (thumb and pointing finger). The system recognizes one dynamic and two static gestures. Recognition and pose estimation are user independent and robust. The system operates at the rate of 60 Hz and can be used as an intuitive input interface to applications that require multi-dimensional control. Examples include 3D fly-thru's, object manipulation and computer games.
Real-Time Gabor-Type Filtering Using Analog Focal Plane Image Processors
Authors: Bertram Emil Shi
Venue: CVPR
Year: 1999

We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing is done using analog circuits fabricated on the same die as the photosensors allowing combined sensing and processing at rates as high as 5000 frames per second. Both 1D and 2D sensors have been designed, fabricated and tested. In the 2D sensor the tuned orientation can be steered and the filter response scaled by adjusting external bias voltages controlling parameters such as the gains of the analog processing circuits. Because both even and odd Gabor-type filter outputs are calculated, sensors can be used in Gabor phase-based algorithms. As a simple example, we have embedded a 1D sensor on a mobile robot platform to perform image fixation.
Comparison of Edge Detectors Using an Object Recognition Task
Authors: Min C. Shin, Dmitry B. Goldgof, Kevin W. Bowyer
Venue: CVPR
Year: 1999

This paper presents a methodology and results of evaluating edge detection algorithms using an object recognition task. A dataset consisting of 37 real images with 5 different jeep-like vehicles is used. Five edge detectors are compared using ROC curve analysis. The Heitger detector gives the best results. The work is being extended to include more images and a train-and-test style evaluation.
Indexing using a Spectral Encoding of Topological Structure
Authors: Ali Shokoufandeh, Sven J. Dickinson, Kaleem Siddiqi, Steven W. Zucker
Venue: CVPR
Year: 1999

In an object recognition system, if the extracted image features are multilevel or multiscale, the indexing structure may take the form of a tree. Such structures are not only common in computer vision, but also appear in linguistics, graphics, computational biology, and a wide range of other domains. In this paper, we develop an indexing mechanism that maps the topological structure of a tree into a low-dimensional vector space. Based on a novel eigenvalue characterization of a tree, this topological signature allows us to efficiently retrieve a small set of candidates from a database of models. To accommodate occlusion and local deformation, local evidence is accumulated in each of the tree's topological subspaces. We demonstrate the approach with a series of indexing experiments in the domain of 2-D object recognition.
Efficient Bundle Adjustment with Virtual Key Frames: A Hierarchical Approach to Multi-Frame Structure from Motion
Authors: Heung-Yeung Shum, Zhengyou Zhang, Qifa Ke
Venue: CVPR
Year: 1999

In this paper we present an efficient hierarchical approach to structure from motion for long image sequences. There are two key elements to our approach: accurate 3D reconstruction for each segment and efficient bundle adjustment for the whole sequence. The image sequence is first divided into a number of segments so that feature points can be reliably tracked across each segment. Each segment has a long baseline to ensure accurate 3D reconstruction. To efficiently bundle adjust 3D structures from ail segments, we reduce the number of frames in each segment by introducing "virtual keyframes". The virtual frames encode the 3D structure of each segment along with its uncertainty but they form a small subset of the original frames. Our method achieves significant speedup over conventional bundle adjustment methods.
Material Classification for 3D Objects in Aerial Hyperspectral Images
Authors: David Slater, Glenn Healey
Venue: CVPR
Year: 1999

Automated material classification from airborne imagery is an important capability for many applications including target recognition and geospatial database construction. Hyperspectral imagery provides a rich source of information for this purpose but utilization is complicated by the variability in a material's observed spectral signature due to the ambient conditions and the scene geometry. In this paper, we present a method that uses a single spectral radiance function measured from a material under unknown conditions to synthesize a comprehensive set of radiance spectra that corresponds to that material over a wide range of conditions. This set of radiance spectra can be used to build a hyperspectral subspace representation that can be used for material identification over a wide range of circumstances. We demonstrate the use of these algorithms for model synthesis and material mapping using HYDICE imagery acquired at Fort Hood, Texas. The method correctly maps several classes of roofing materials, roads, and vegetation over significant spectral changes due to variation in surface orientation. We show that the approach outperforms methods based on direct spectral comparison.
Efficient Techniques for Wide-Angle Stereo Vision Using Surface Projection Models
Authors: Philip W. Smith, Keith B. Johnson, Mongi A. Abidi
Venue: CVPR
Year: 1999

Wide-Angle lenses are not often used for 3D reconstruction tasks, in spite of the potential advantages offered by their increased field-of-view, because (1) existing algorithms for high-distortion lens compensation perform poorly at image extremities and (2) procedures for the reconstruction of recti-linear images place a large burden on system resources. In this paper, a projection model based on quadric surfaces is presented which accurately characterizes the effect of wide-angle lenses across the entire image and allows for the use of novel feature matching strategies that do not require nonlinear distortion compensation.
A New Visualization Paradigm for Multispectral Imagery And Data Fusion
Authors: Diego A. Socolinsky, Lawrence B. Wolff
Venue: CVPR
Year: 1999

We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm is optimal and outperforms them. A variety of experimental results are presented.
Automatic Hierarchical Classification Using Time-Based Co-Occurrences
Authors: Chris Stauffer
Venue: CVPR
Year: 1999

While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by using accumulated joint cooccurrences of the representations within the sequence to create a hierarchical binary-tree classifier of the representations. This classifier is useful to classify sequences as well as individual instances. We illustrate the use of this method on two separate representations the tracked object's position, movement, and size; and the tracked object's binary motion silhouettes.
Adaptive Background Mixture Models for Real-Time Tracking
Authors: Chris Stauffer, W. Eric L. Grimson
Venue: CVPR
Year: 1999

A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.
Tracking from Multiple View Points: Self-calibration of Space and Time
Authors: Gideon P. Stein
Venue: CVPR
Year: 1999

This paper tackles the problem of self-calibration of multiple cameras which are very far apart. Given a set of feature correspondences one can determine the camera geometry. The key problem we address is finding such correspondences. Since the camera geometry (location and orientation) and photometric characteristics vary considerably between images one cannot use brightness and/or proximity constraints. Instead we propose a three step approach: first we use moving objects in the scene to determine a rough planar alignment, next we use static features to improve the alignment, finally we compute the epipolar geometry from the the homography matrix of the planar alignment. We do not assume synchronized cameras and we show that enforcing geometric constraints enables us to align the tracking data in time. We present results on challenging outdoor scenes using real time tracking data.
Estimating Model Parameters and Boundaries By Minimizing a Joint, Robust Objective Function
Authors: Charles V. Stewart, Kishore Bubna, A. G. Amitha Perera
Venue: CVPR
Year: 1999

Many problems in computer vision require estimation of both model parameters and boundaries, which limits the usefulness of standard estimation techniques from statistics. Example problems include surface reconstruction from range data, estimation of parametric motion models, fitting circular or elliptic arcs to edgel data, and many others. This paper introduces a new estimation technique, called the "Domain Bounding M-Estimator", which is a generalization of ordinary M-estimators combining error measures on model parameters and boundaries in a joint, robust objective function. Minimization of the objective function given a rough initialization yields simultaneous estimates of parameters and boundaries. The DBM-Estimator has been applied to estimating line segments, surfaces, and the symmetry transformation between two edgel chains. It is unaffected by outliers and prevents boundary estimates from crossing even small magnitude discontinuities.
Robust Hierarchical Algorithm for Constructing a Mosaic from Images of the Curved Human Retina
Authors: Charles V. Stewart, Badrinath Roysam, Ali Can
Venue: CVPR
Year: 1999

This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique described here automatically constructs a composite (mosaic) image of the retina from a sequence of incomplete views. This mosaic will be useful to ophthalmologists for both diagnosis and surgery. The new technique goes beyond published methods in both the medical and computer vision literatures because it is fully automated, models the patient-dependent curvature of the retina, handles large interframe motions, and does not require calibration. At the heart of the technique is a 12-parameter image transformation model derived by modeling the retina as a quadratic surface and assuming a weak perspective camera, and rigid motion. Estimating the parameters of this transformation model requires robustness to unmatchable image features and mismatches between features caused by large interframe motions. The described estimation technique is a hierarchy of models and methods: the initial match set is pruned based on a 0th order transformation estimated using a similarity-weighted histogram; a 1st order affine transformation is estimated using the reduced match set and least-median of squares; and the final, 2nd order 12-parameter transformation is estimated using an M-estimator initialized from the 1st order results. Initial experimental results show the method to be robust and accurate in accounting for the unknown retinal curvature in a fully automatic manner while preserving image details.
Locating Indexing Structures in Engineering Drawing Databases Using Location Hashing
Authors: Tanveer Fathima Syeda-Mahmood
Venue: CVPR
Year: 1999

Fast and robust localization of image regions that are likely to come from a query object is a critical operation needed in several image database applications. In this paper, we address this problem in the context of locating indexing structures in engineering drawing databases. Specifically, we present a general 2d pattern localization technique called location hashing and apply it to the problem of localizing title block structures in engineering drawings. Location hashing is a variation of geometric hashing that directly identifies locations in images of a database that are likely to contain a query object, possibly depicted under different imaging conditions. We also describe an engineering drawing indexing system that combines title block localization with test recognition to enable automatic index creation of engineering drawing databases.
Low-Cost Interactive Active Monocular Range Finder
Authors: Masahiro Takatsuka, Geoff A. W. West, Svetha Venkatesh, Terry Caelli
Venue: CVPR
Year: 1999

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser and the others from LEDs), and triangulation is carried our using the camera's viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator's knowledge of the objects.
Explanation-Based Facial Motion Tracking Using a Piecewise Bézier Volume Deformation Model
Authors: Hai Tao, Thomas S. Huang
Venue: CVPR
Year: 1999

Capturing real motions from video sequences is a powerful method for automatic building of facial articulation models. In this paper, we propose an explanation-based facial motion tracking algorithm based on a piecewise Bezier volume deformation model (PBVD). The PBVD is a suitable model both for the synthesis and the analysis of facial images. It is linear and independent of the facial mesh structure. With this model, basic facial movements, or action units, are interactively defined. By changing the magnitudes of these action units, animated facial images are generated. The magnitudes of these action units can also be computed from real video sequences using a model-based tracking algorithm. However, in order to customize the articulation model for a particular face, the predefined PBVD action units need to be adaptively modified. In this paper, we first briefly introduce the PBVD model and its application in facial animation. Then a multiresolution PBVD-based motion tracking algorithm is presented. Finally, we describe an explanation-based tracking algorithm that takes the predefined action units as the initial articulation model and adaptively improves them during the tracking process to obtain a more realistic articulation model. Experimental results on PBVD-based animation, model-based tracking, and explanation-based tracking are shown in this paper.
Algebraic Curves That Work Better
Authors: Tolga Tasdizen, Jean-Philippe Tarel, David B. Cooper
Venue: CVPR
Year: 1999

An algebraic curve is defined as the zero set of a polynomial in two variables. Algebraic curves are practical for modeling shapes much more complicated than conics or superquadrics. The main drawback in representing shapes by algebraic curves has been the lack of repeatability in fitting algebraic curves to data. A regularized fast linear fitting method based on ridge regression and restricting the representation to well behaved subsets of polynomials is proposed, and its properties are investigated. The fitting algorithm is of sufficient stability for very fast position-invariant shape recognition, position estimation, and shape tracking, based on new invariants and representations, and is appropriate to open as well as closed curves of unorganized data. Among appropriate applications are shape-based indexing into image databases.