IEEE Trans Image Process - GPU accelerated edge-region based level set evolution constrained by 2D gray-scale histogram.

Tópicos

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Resumo

Due to its intrinsic nature which allows to easily handle complex shapes and topological changes, the level set method (LSM) has been widely used in image segmentation. Nevertheless, LSM is computationally expensive, which limits its applications in real-time systems. For this purpose, we propose a new level set algorithm, which uses simultaneously edge, region, and 2D histogram information in order to efficiently segment objects of interest in a given scene. The computational complexity of the proposed LSM is greatly reduced by using the highly parallelizable lattice Boltzmann method (LBM) with a body force to solve the level set equation (LSE). The body force is the link with image data and is defined from the proposed LSE. The proposed LSM is then implemented using an NVIDIA graphics processing units to fully take advantage of the LBM local nature. The new algorithm is effective, robust against noise, independent to the initial contour, fast, and highly parallelizable. The edge and region information enable to detect objects with and without edges, and the 2D histogram information enable the effectiveness of the method in a noisy environment. Experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

Resumo Limpo

due intrins natur allow easili handl complex shape topolog chang level set method lsm wide use imag segment nevertheless lsm comput expens limit applic realtim system purpos propos new level set algorithm use simultan edg region d histogram inform order effici segment object interest given scene comput complex propos lsm great reduc use high paralleliz lattic boltzmann method lbm bodi forc solv level set equat lse bodi forc link imag data defin propos lse propos lsm implement use nvidia graphic process unit fulli take advantag lbm local natur new algorithm effect robust nois independ initi contour fast high paralleliz edg region inform enabl detect object without edg d histogram inform enabl effect method noisi environ experiment result synthet real imag demonstr subject object perform propos method

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