Localizing Region-Based 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/TIP.2008.2004611In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models

Improved tracking by decoupling camera and target motion

©2008 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.768453Presented at Real-time image processing 2008, 28-29 January 2008, San Jose, California, USA.DOI: 10.1117/12.768453Video tracking is widely used for surveillance, security, and defense purposes. In cases where the camera is not fixed due to pans and tilts, or due to being fixed on a moving platform, tracking can become more difficult. Camera motion must be taken into account, and objects that come and go from the field of view should be continuously and uniquely tracked. We propose a tracking system that can meet these needs by using a frame registration technique to estimate camera motion. This estimate is then used as the input control signal to a Kalman filter which estimates the target's motion model based on measurements from a mean-shift localization scheme. Thus we decouple the camera and object motion and recast the problem in terms of a principled control theory solution. Our experiments show that using a controller built on these principles we are able to track videos with multiple objects in sequences with moving cameras. Furthermore, the techniques are computationally efficient and allow us to accomplish these results in real-time. Of specific importance is that when objects are lost off-frame they can still be uniquely identified and reacquired when they return to the field of view

Localized statistical models in computer vision

Computer vision approximates human vision using computers. Two subsets are explored in this work: image segmentation and visual tracking. Segmentation involves partitioning an image into logical parts, and tracking analyzes objects as they change over time. The presented research explores a key hypothesis: localizing analysis of visual information can improve the accuracy of segmentation and tracking results. Accordingly, a new class of segmentation techniques based on localized analysis is developed and explored. Next, these techniques are applied to two challenging problems: neuron bundle segmentation in diffusion tensor imagery (DTI) and plaque detection in computed tomography angiography (CTA) imagery. Experiments demonstrate that local analysis is well suited for these medical imaging tasks. Finally, a visual tracking algorithm is shown that uses temporal localization to track objects that change drastically over time.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Al Regib, Ghassan; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Stillman, Arthur; Committee Member: Yezzi, Anthon

Hybrid geodesic region-based curve evolutions for image segmentation

©2007 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.709700Medical imaging 2007: Physics of medical imaging, 18-22 February 2007, San Diego, California, USA.DOI:10.1117/12.709700In this paper we present a gradient descent flow based on a novel energy functional that is capable of producing robust and accurate segmentations of medical images. This flow is a hybridization of local geodesic active contours and more global region-based active contours. The combination of these two methods allows curves deforming under this energy to find only significant local minima and delineate object borders despite noise, poor edge information, and heterogeneous intensity profiles. To accomplish this, we construct a cost function that is evaluated along the evolving curve. In this cost, the value at each point on the curve is based on the analysis of interior and exterior means in a local neighborhood around that point. We also demonstrate a novel mathematical derivation used to implement this and other similar flows. Results for this algorithm are compared to standard techniques using medical and synthetic images to demonstrate the proposed method's robustness and accuracy as compared to both edge-based and region-based alone