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One SUV frame in the report was analyzed(FEA)with FE software HyperMesh,modal calculated with Nastran,and then we analyzed the results with HyperView. Fortunately,we obtained five natural frequencies which show that the natural frequencies of the frame above the road surface exciting frequency,where would be no resonance occur. As a result we can image the later improved design of frame structure could provide a theoretical basis.

This paper proposes a novel region-based active contour model (ACM) for image segmentation, which is robust to noise and intensity non-uniformity. The energy functional of the proposed model consists of three terms, i.e., the patch-statistical region fitting term, the improved regularization term, and the intensity variation penalization term. The patch-statistical region fitting term computes the local statistical information in each patch as the basis for driving the curve accurately with resist to intensity non-uniformity and weak boundaries. And the regularization term coupling with the gradient information improves the ability of capturing the boundaries with cusps and narrow topology structures. Furthermore, an intensity variation penalization term is proposed to make sure that the segmentation result is robust to the irregular intensity variation. Experiments on medical and natural images show that the proposed model is more robust than the popular active contour models for image segmentation with noise and intensity non-uniformity.

In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy.