Biomedical subjects

Rama Chellappa

Publications and source records attributed to Rama Chellappa.

6 recordsLinked to original sources

PubMed2005

Bayesian algorithms for simultaneous structure from motion estimation of multiple independently moving objects.

In this paper, the problem of simultaneous structure from motion estimation for multiple independently moving objects from a monocular image sequence is addressed. Two Bayesian algorithms are presented for solving this problem using the sequential importance sampling (SIS) technique. The empirical posterior distribution of object motion and feature separation parameters is approximated by weighted samples. The first algorithm addresses the problem when only two moving objects are present. A singular value decomposition (SVD)-based sample clustering algorithm is shown to be capable of separating samples related to different objects. A pair of SIS procedures is used to track the posterior distribution of the motion parameters. In the second algorithm, a balancing step is added into the SIS procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. By using the proposed algorithms, the relative motions of all the moving objects with respect to the camera can be simultaneously estimated. Both algorithms have been tested on synthetic and real-image sequences. Improved results have been achieved.

Algorithms↗

PubMed2004

An information theoretic criterion for evaluating the quality of 3-D reconstructions from video.

Even though numerous algorithms exist for estimating the three-dimensional (3-D) structure of a scene from its video, the solutions obtained are often of unacceptable quality. To overcome some of the deficiencies, many application systems rely on processing more data than necessary, thus raising the question: how is the accuracy of the solution related to the amount of data processed by the algorithm? Can we automatically recognize situations where the quality of the data is so bad that even a large number of additional observations will not yield the desired solution? Previous efforts to answer this question have used statistical measures like second order moments. They are useful if the estimate of the structure is unbiased and the higher order statistical effects are negligible, which is often not the case. This paper introduces an alternative information-theoretic criterion for evaluating the quality of a 3-D reconstruction. The accuracy of the reconstruction is judged by considering the change in mutual information (MI) (termed as the incremental MI) between a scene and its reconstructions. An example of 3-D reconstruction from a video sequence using optical flow equations and known noise distribution is considered and it is shown how the MI can be computed from first principles. We present simulations on both synthetic and real data to demonstrate the effectiveness of the proposed criterion.

Algorithms↗

PubMed2004

Identification of humans using gait.

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.

Algorithms↗

PubMed2004

Visual tracking and recognition using appearance-adaptive models in particle filters.

We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.

Algorithms↗

PubMed2003

Statistical-physical model for foliage clutter in ultra-wideband synthetic aperture radar images.

Analyzing foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (SAR) images is a challenging problem owing to the noisy and impulsive nature of foliage clutter. Indeed, many target-detection algorithms for FOPEN SAR data are characterized by high false-alarm rates. In this work, a statistical-physical model for foliage clutter is proposed that explains the presence of outliers in the data and suggests the use of symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling. Furthermore, with the use of general assumptions of the noise sources and propagation conditions, the proposed model relates the parameters of the SalphaS model to physical parameters such as the attenuation coefficient and foliage density.

Journal Article↗

PubMed2003

Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters.

A multiscale algorithm with complexity O(N) (where N is the number of pixels in one image) using wavelet filters is proposed to estimate dense optical flow from two frames. Hierarchical image representation by wavelet decomposition is integrated with differential techniques in a new multiscale framework. It is shown that if a compactly supported wavelet basis with one vanishing moment is carefully selected, hierarchical image, first-order derivative, and corner representations can be obtained from the wavelet decomposition. On the basis of this result, three of the four components of the wave let decomposition are employed to estimate dense optical flow with use of only two frames. This overcomes the "flattening-out" problem in traditional pyramid methods, which produce large errors when low-texture regions become flat at coarse levels as a result of blurring. A two-dimensional affine motion model is used to formulate the optical flow problem as a linear system, with all resolutions simultaneously (i.e., coarse-and-fine) rather than the traditional coarse-to-fine approach, which unavoidably propagates errors from the coarse level. This not only helps to improve the accuracy but also makes the hardware implementation of our algorithm simple. Experiments on different types of image sequences, together with quantitative and qualitative comparisons with several other optical flow methods, are given to demonstrate the effectiveness and the robustness of our algorithm.

Journal Article↗