Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions Using Level Sets

Using a camera,the visual object tracking is one of the most important process in searching the spot of moving object over the time. In the case of the object moves fast relative to the frame rate,the visual object tracking is difficult task. The active contour evolution algorithm which is used for the tracking of object in a given frame of an image sequence. Active contour based visual object tracking using the level sets is proposed which does not consider the camera either stationary or moving. We present a framework for active contour-based visual object tracking using the level sets. The main components of our framework consist of the contour-based tracking initialization, colour-based contour evolution, the adaptive shape-based contour evolution for the non-periodic motions, the dynamic shape-based contour evolution for the periodic motions and handling of the abrupt motions. For the contour-based tracking initialization, we use an optical flow-based algorithm for the automatically initializing contours at the first frame. In the color-based contour evolution, we use Markov random field theory to measure correlations between values of the neighboring pixels for the posterior probability estimation.In the adaptive shape-based contour evolution, we combined the global shape information and the local color information to hierarchically develop gradually the contour, and a flexible shape updating model is made. In the dynamic shape based contour evolution, a shape mode transition matrix is gain to characterize the temporal correlations of the object shapes. In the handling of abrupt motions, particle swarm optimization (PSO) is used to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame. DOI: 10.17762/ijritcc2321-8169.15013

Finsler Active Contours

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TPAMI.2007.70713In this paper, we propose an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the isotropic case, the euclidean metric is locally multiplied by a scalar conformal factor based on image information such that the weighted length of curves lying on points of interest (typically edges) is small. The conformal factor that is chosen depends only upon position and is in this sense isotropic. Although directional information has been studied previously for other segmentation frameworks, here, we show that if one desires to add directionality in the conformal active contour framework, then one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming-based schemes. Finally, we demonstrate the technique by extracting roads from aerial imagery, blood vessels from medical angiograms, and neural tracts from diffusion-weighted magnetic resonance imagery

Surface Modeling and Analysis Using Range Images: Smoothing, Registration, Integration, and Segmentation

This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images. The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation. Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme. Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison. Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid. Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method. The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics

Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Grouping Boundary Proposals for Fast Interactive Image Segmentation

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches

DNA microarray image segmentation using active contours without edges method

The goal of this dissertation is to build a better segmentation method for DNA microarray image processing. Segmentation is a partitioning process used to separate a spot area from a non-spot area in DNA microarrays. It directly affects the accuracy of gene expression analysis in the data mining process that follows. A number of DNA microarray segmentation methods have been proposed in the area, but even modern segmentation methods seem to have accuracy problems. In this dissertation, I will present a segmentation method based on the Active Contours Without Edges (ACWE) algorithm and apply it to two types of DNA microarrays, complementary DNA (cDNA) and Affymetrix GeneChip. Several adjustments will be applied to the original ACWE method to use it more efficiently in the microarray processing area. As a secondary research objective, I will improve the ACWE method by using higher order schemes in finite difference method for solving the partial differential equation (PDE). The original ACWE method used the associated Euler-Lagrange partial differential equation for the Lipschitz function Φ. It used the lower order finite difference schemes to solve the PDE. The improved ACWE method defines the higher order finite difference schemes to increase the accuracy of segmentation. Various experimental results will be presented to show that the ACWE method is more efficient than other DNA microarray image segmentation methods. Statistical analysis is performed to compare the newly proposed method with the previously best methods in this area. Experimental results will also be presented to show that the improved ACWE method has more accurate segmentation results than the ACWE method