Fast and accurate circle tracking using active contour models

Real-Time Imaging, 1999

Tracking n this paper we describe a new approach to contour extraction and tracking, which is based on the principles of active contour models and overcomes its shortcomings. We formally introduce active Ir ays, describe the contour extraction as an energy minimization problem and discuss what active contours and active rays have in common. The main difference is that for active rays a unique ordering of the contour elements in the 2D image plane is given, which cannot be found for active contours. This is advantageous for predicting the contour elements' position and prevents crossings in the contour. Furthermore, another advantage is that instead of an energy minimization in the 2D image plane the minimization is reduced to a 1D search problem. The approach also shows anytime behavior, which is important with respect to real-time applications. Finally, the method allows for the management of multiple hypotheses of the object's boundary. This is an important aspect if concave contours are to be tracked. Results on real image sequences (tracking a toy train in a laboratory scene, tracking pedestrians in an outdoor scene) show the suitability of this approach for real-time object tracking in a closed loop between image acquisition and camera movement. The contour tracking can be done within the image frame rate (25 fps) on standard Unix workstations (HP 735) without any specialized hardware.

2007

In computational vision, visual tracking remains one of the most challenging problems due to noise, clutter, occlusion, and dynamic scenes. No one technique has yet managed to solve this problem completely, but those that employ control- theoretic filtering techniques have proven to be quite successful. In this work, we extend one such technique by Niethammer et al. in which implicitly represented dynamically evolving contours are filtered using a geometric observer framework. The effectiveness of the observer hangs upon the solution of two major problems: (1) the calculation of accurate curve velocities and (2) the determination of diffeomorphic correspondence maps between curves for geometric interpolation. We propose the use of novel image registration techniques such as image warping and optimal mass transport for the solution of these problems which increase the performance of the framework and reduce algorithmic complexity. One major drawback to the original scheme, as it relies on PDE solutions, is its computational burden restricting it from real time use. We show that the framework can, in fact, run in near real time by implementing our additions to the framework on the graphics processing unit (GPU) and show better execution times for these algorithms than reported in recent literature.

2002

In this note, we analyze the geometric active contour models proposed in [5, 191 from a curve evolution point of view and propose some modifications based on gradient flows relative to certain new metrics. This leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus the snake is attracted very naturally and efficiently to the desired feature. Moreover, we consider some 3-D active surface models based on these ideas.

Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects, 1994

There has been considerable research interest recently, in the areas of real time contour tracking and active shape models. This paper demonstrates how dynamic ltering can be used in combination with a exible shape model to track an articulated non-rigid body in motion. The results show the method being used to track the silhouette of a walking pedestrian across a scene in real time. The active shape model used was generated automatically from real image data and incorporates variability in shape due to orientation as well as object exibility. A Kalman lter is used to control spatial scale for feature search over successive frames and for contour re nement on an individual frame. Iterative re nement allows accurate contour localisation where feasible, although there is a trade-o between speed and accuracy. The shape model incorporates knowledge of the likely shape of the contour and speeds up tracking by reducing the number of system parameters. A further increase in speed is obtained by ltering the shape parameters independently.

In Asian Conference on Computer Vision, 1995

In the past six years many algorithms and models for active contours (snakes) have been presented. Some of the work has been applied to static image analysis, some other to image sequence processing. Despite of the fact that snakes can be used for object tracking, no comparative study of the performance for real{time object tracking is known up to now. In this paper we compare several active contour models presented earlier in the literature for object tracking: the \Greedy" algorithm, the dynamic programming approach, and the rst work of Kass, based on the variational calculus. We discuss and compare the various active contour models with respect to the quality of contour extraction, the computation time and robustness. All evaluation is done using sequences, grabbed during closed{loop real{time experiments.

2002

Real-time object tracking has become a very important task for a large number of computer vision and robotic applications. The tracking function must be robust, de- spite of the high complexity present in a given environ- ment. Frequently, the object to be tracked is well known (humans, head, hands, traffic signs, etc.). Shape con- straints can be introduced directly in

Lecture Notes in Computer Science, 2006

One fundamental step for image-related research is to obtain an accurate segmentation. Among the available techniques, the active contour algorithm has emerged as an efficient approach towards image segmentation. By progressively adjusting a reference curve using combination of external and internal force computed from the image, feature edges can be identified. The Gradient Vector Flow (GVF) is one efficient external force calculation for the active contour and a GPU-centric implementation of the algorithm is presented in this paper. Since the internal SIMD architecture of the GPU enables parallel computing, General Purpose GPU (GPGPU) based processing can be applied to improve the speed of the GVF active contour for large images. Results of our experiments show the potential of GPGPU in the area of image segmentation and the potential of the GPU as a powerful co-processor to traditional CPU computational tasks.

International Journal of Image and Graphics, 2006

In this paper we present a new algorithm to track an organ in a sequence of medical images in order to achieve a 3D reconstruction. The automatic method that we propose allows the tracking of the external contour of the anatomical organ in all the sequence from one contour initialized by the user on the first image. The required operations for our tracking method are the region-based active contours segmentation. The objects localization with dynamic prediction of displacements is based on the level-set functions and the definition of the region of interest for the robust local estimation of the image model. An application of this method is the 3D reconstruction of abdominal aorta.

Handbook of Research on Natural Computing for Optimization Problems

Recent developments in medical imaging techniques have brought an entirely new research field. Medical images are frequently corrupted by inherent noise and artifacts that could make it difficult to extract accurate information, and hence compromising the quality of clinical examination. So accurate detection is one of the major problems for medical image segmentation. Snakes or Active contour method have gained wide attention in medical image segmentation for a long time. A Snake is an energy-minimizing spline that controlled by an external energy and influenced by image energy that pull it towards features such as lines and edges. One of the key difficulties with traditional active contour algorithms is a large capture range problem. The contribution of this paper is that to in-depth analysis of the existing different contour models and implementation of techniques with minor improvements that to solve the large capture range problem. The experiment results of this model attain hi...

International Journal of Computer Vision, 1988

A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.