Area and Length Minimizing Flows for Shape Segmentation

©1997 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.Presented at the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 17-19, 1997, San Juan, Puerto Rico.DOI: 10.1109/CVPR.1997.609390Several active contour models have been proposed to unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in 20) or a surface (in 30) under constraints from image forces so that it clings to features of interest in an intensity image. Recently the evolution equation has. been derived from first principles as the gradient flow that minimizes a modified length functional, tailored io features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. In this paper, we provide a justification for this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow we obtain a pde which offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings

Segmentation of Image Using Watershed and Fast Level set methods

Technology is proliferating. Many methods are used for medical imaging .The important methods used here are fast marching and level set in comparison with the watershed transform .Since watershed algorithm was applied to an image has over clusters in segmentation . Both methods are applied to segment the medical images. First, fast marching method is used to extract the rough contours. Then level set method is utilized to finely tune the initial boundary. Moreover, Traditional fast marching method was modified by the use of watershed transform. The method is feasible in medical imaging and deserves further research. It could be used to segment the white matter, brain tumor and other small and simple structured organs in CT and MR images. In the future, we will integrate level set method with statistical shape analysis to make it applicable to more kinds of medical images and have better robustness to noise

NOVA INFORMACIJSKA TEHNOLOGIJA PROCJENE KORISTI IZDVAJANJA CESTA POMOĆU SATELITSKIH SNIMKI VISOKE REZOLUCIJE TEMELJENE NA PCNN I C-V MODELU

Road extraction from high resolution satellite images has been an important research topic for analysis of urban areas. In this paper road extraction based on PCNN and Chan-Vese active contour model are compared. It is difficult and computationally expensive to extract roads from the original image due to presences of other road-like features with straight edges. The image is pre-processed using median filter to reduce the noise. Then road extraction is performed using PCNN and Chan-Vese active contour model. Nonlinear segments are removed using morphological operations. Finally the accuracy for the road extracted images is evaluated based on quality measures.Izdvajanje cesta pomoću satelitskih slika visoke rezolucije je važna istraživačka tema za analizu urbanih područja. U ovom radu ekstrakcije ceste se uspoređuju na PCNN i Chan-Vese aktivnom modelu. Teško je i računalno skupo izdvojiti ceste iz originalne slike zbog prisutnosti drugih elemenata ravnih rubova sličnih cestama. Slika je prethodno obrađena korištenjem filtera za smanjenje smetnji. Zatim se ekstrakcija ceste izvodi pomoću PCNN i Chan-Vese aktivnog modela konture. Nelinearni segmenti su uklonjeni primjenom morfoloških operacija. Konačno, točnost za ceste izdvojene iz slika se ocjenjuje na temelju kvalitativnih mjera

A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity

A mixed model of active geodesic contours with gradient vector flows for X-ray microtomography segmentation

Actes de la conférence "Mathématiques pour l'image" - PUOInternational audienceThe structural characterization of weathered building stones of historical monuments can be achieved with a powerfull imagering technique, X-ray microtomography. It requires however a carefull extraction of each phase constituting the sample from the raw gray-level images obtained (the segmentation process). This contribution presents an original method of segmentation of such images that combines active contour models driven by gradient vector flows with a morphological preprocessing, alternate sequential filters. Preliminary results on high resolution, structuraly complex images are presented and compared to more classical approaches

Integration of SLAM and Obstacle Avoidance algorithms

The ability of an unmanned aerial vehicle (UAV) to identify an obstacle in its pat

Level set segmentation with multiple regions

The popularity of level sets for segmentation is mainly based on the sound and convenient treatment of regions and their boundaries. Unfortunately, this convenience is so far not known from level set methods when applied to images with more than two regions. This paper introduces a comparatively simple way how to extend active contours to multiple regions keeping the familiar quality of the two-phase case. We further suggest a strategy to determine the optimum number of regions as well as initializations for the contours

Analysis of Amoeba Active Contours

Subject of this paper is the theoretical analysis of structure-adaptive median filter algorithms that approximate curvature-based PDEs for image filtering and segmentation. These so-called morphological amoeba filters are based on a concept introduced by Lerallut et al. They achieve similar results as the well-known geodesic active contour and self-snakes PDEs. In the present work, the PDE approximated by amoeba active contours is derived for a general geometric situation and general amoeba metric. This PDE is structurally similar but not identical to the geodesic active contour equation. It reproduces the previous PDE approximation results for amoeba median filters as special cases. Furthermore, modifications of the basic amoeba active contour algorithm are analysed that are related to the morphological force terms frequently used with geodesic active contours. Experiments demonstrate the basic behaviour of amoeba active contours and its similarity to geodesic active contours.Comment: Revised version with several improvements for clarity, slightly extended experiments and discussion. Accepted for publication in Journal of Mathematical Imaging and Visio