Active deformable models are simple tools, very popular in computer vision and computer graphics, for solving ill-posed problems or mimic real physical systems. The classical formulation is given in the spatial domain, the motor of the procedure is a second-order linear system, and rigidity and elasticity are the basic parameters for its characterization. This paper proposes a novel formulation based on a frequency-domain analysis: The internal energy functional and the Lagrange minimization are performed entirely in the frequency domain, which leads to a simple formulation and design. The frequency-based implementation offers important computational savings in comparison to the original one, a feature that is improved by the efficient hardware and software computation of the FFT algorithm. This new formulation focuses on the stiffness spectrum, allowing the possibility of constructing deformable models apart from the elasticity and rigidity-based original formulation. Simulation examples validate the theoretical results.
In radiotherapy (RT), organ motion caused by breathing prevents accurate patient positioning, radiation dose, and target volume determination. Most of the motion-compensated trial techniques require collaboration of the patient and expensive equipment. Estimating the motion between two computed tomography (CT) three-dimensional scans at the extremes of the breathing cycle and including this information in the RT planning has been shyly considered, mainly because that is a tedious manual task. This paper proposes a method to compute in a fully automatic fashion the spatial correspondence between those sets of volumetric CT data. Given the large ambiguity present in this problem, the method aims to reduce gradually this uncertainty through two main phases: a similarity-parametrization data analysis and a projection-regularization phase. Results on a real study show a high accuracy in establishing the spatial correspondence between both sets. Embedding this method in RT planning tools is foreseen, after making some suggested improvements and proving the validity of the two-scan approach.
Artificial muscles are formed by attaching a conducting polymeric film to a non-conducting one. The flow of an electric current produces a macroscopic bending movement on the muscle. A good characterization of both, motion rate and energy of curvature, is required for improving the efficiency of these devices. In this paper, a two-cam stereo vision system is proposed to acquire and process the image sequence and a 3D snake for tracking the muscle. From the curve given by the snake, mechanical parameters of the artificial muscle can be estimated. The movements along the life cycle of the muscle can be compared with the energy consumed in each cycle. This is necessary for determining the span life of these devices in applications where they work as actuators. Results prove the validity of this approach.
Image matching of deformable structures has captured great attention in image processing, and specially in the medical field. This papcr proposes a method that faces the ill-posed nature of this problem, by using a cluster-sized similarity cost function, the ambiguity in each similarity map is described by a fuzzy parametric model, and, finally, a spatially non-uniform fuzzy interpolation is used to translate the parametric info into a set of matching field vectors. The method obtains the spatial matching between the two images in a global spatial extent and with sub-pixel accuracy. Results of the method on real images and high non-rigid artificial deformation proves the validity of the approach. Its extension to B volumetric approach is also suggested.
Active contours effectiveness in image segmentation is well known. As any adaptive system, the iterations required by the contour to delineate the target is of importance. In this process, some nodes mach their position before than others, and, due to the internal forces, the neighboring nodes evolve towards a final shape constrained to the external forces. This paper presents a signal-processing perspective of that scenario by deriving a novel frequency-based formulation. The main result of the analysis is the speed of convergence, which depends analytically on the stiffness properties, and especially on the second-order parameters and the length of the snake segments. This initial attempt to characterize the snake dynamics is supported with simulation results.
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