We introduce a fast and efficient variational framework for Simultaneous Registration and Segmentation (SRS) applicable to a wide variety of image sequences. We demonstrate that a dense correspondence map (between consecutive frames) can be reconstructed correctly even in the presence of partial occlusion, shading, and reflections. The errors are efficiently handled by exploiting their sparse nature. In addition, the segmentation functional is reformulated using a dual Rudin-Osher-Fatemi (ROF) model for fast implementation. Moreover, nonparametric shape prior terms that are suited for this dual-ROF model are proposed. The efficacy of the proposed method is validated with extensive experiments on both indoor, outdoor natural and biological image sequences, demonstrating the higher accuracy and efficiency compared to various state-of-the-art methods.

Simultaneous registration and segmentation (SRS) provides a powerful framework for tracking an object of interest in an image sequence. The state-of-the-art SRS-based tracking methods assume that the illumination is maintained constant across consecutive frames. However, this assumption does not hold in many natural image sequences due to dynamic light source and shadows. We propose a generalized model for SRS-based tracking in this paper to account for non-uniform additive illumination changes. More specifically, we introduce two new terms in the SRS energy functional which address the above mentioned problem. The first term couples the shape-based cue and intensity-based cue to establish a correspondence between them. The second term compensates for the illumination change which is complementary to the first term. We demonstrate that the proposed SRS energy functional yields superior performance over the state-of-the-art SRS-based methods for various indoor and outdoor image sequences.

We present a method for object tracking over time sequence imagery. The image plane is represented with a 4-connected planar graph where vertices are associated with pixels. On each image, the outer contour of the object is localized by finding the optimal cycle in the graph such that a cost function based on temporal, appearance and shape priors is minimized. Our contribution is the particle filtering-based framework to integrate the shape cue with the temporal and appearance cues. We demonstrate that incorporating the shape prior yields promising performance improvement over temporal and appearance priors on various object tracking scenarios.

We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the color layout descriptor, a compact and robust frame-based descriptor, to create fingerprints which are further encoded by vector quantization (VQ). We propose a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search cannot be performed for high-dimensional data using a nonmetric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38 000 videos, worth 1600 h of content. For individual queries with an average duration of 60 s (about 50% of the average database video length), the duplicate video is retrieved in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a very high accuracy of 97.5%.

We introduce a robust image segmentation method based on a variational formulation using edge flow vectors. We demonstrate the nonconservative nature of this flow field, a feature that helps in a better segmentation of objects with concavities. A multiscale version of this method is developed and is shown to improve the localization of the object boundaries. We compare and contrast the proposed method with well known state-of-the-art methods. Detailed experimental results are provided on both synthetic and natural images that demonstrate that the proposed approach is quite competitive.

We introduce a dynamical model for simultaneous registration and segmentation in a variational framework for image sequences, where the dynamics is incorporated using a Bayesian formulation. A linear stochastic equation relating the tracked object (or a region of interest) is first derived under the assumption that the successive images in the sequence are related by a dense and possibly non-linear displacement field. This derivation allows for the use of a computationally efficient and recursive implementation of the Bayesian formulation in this framework. The contour of the tracked object returned by the dynamical model is not only close to the previously detected shape but is also consistent with the temporal statistics of the tracked object. The performance of the proposed approach is evaluated on real image sequences. It is shown that, with respect to a variety of error metrics such as F-measure, mean absolute deviation and Hausdorff distance, the proposed approach outperforms the state-of-the art approach without the dynamical model.

In this paper we demonstrate the effectiveness of reference (or atlas)-based non-rigid registration for the segmentation of medical and biological imagery. In particular we introduce a segmentation functional exploiting feature information about the reference image and we minimize it with respect to the parameters of the non-rigid transformation, akin to a region-based maximum likelihood estimation process. The warping transformation is modeled using thin plate splines, which incorporate information about the global rigid motion and the non-rigid local displacements. Extensive experimental evaluations and comparisons with other segmentation techniques on a complex biological dataset are presented. The proposed algorithm outperforms the others in both classification rate and, in particular, localization accuracy.

The expression levels of rod opsin and glial fibrillary acidic protein (GFAP) capture important structural changes in the retina during injury and recovery. Quantitatively measuring these expression levels in confocal micrographs requires identifying the retinal layer boundaries and spatially corresponding the layers across different images. In this paper, a method to segment the retinal layers using a parametric active contour model is presented. Then spatially aligned expression levels across different images are determined by thresholding the solution to a Dirichlet boundary value problem. Our analysis provides quantitative metrics of retinal restructuring that are needed for improving retinal therapies after injury.