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In this paper we present a sampling result about continuous-domain black and white images that form a convex shape. In particular, we will study shapes whose boundaries belong to the zero-level sets (roots) of bivariate polynomials. In [1] it was shown that generalized 2D moments of the image can lead to annihilation equations for the coefficients of the bivariate polynomial that determine the boundary...
Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge-based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined...
In this paper, we propose a descriptor for Brachiopods classification by using a combination between curvature and Fourier descriptors. The curvature properties provide an apparently powerful cue to the underlying structure of the curve and captures completely the structure of planar curve. In addition, it is stable and complete. Fourier descriptors are powerful features for the recognition of two-dimensional...
The past twenty years has seen the explosion of the "shape zoo": myriad shape representations, each with pros and cons. Of the varied denizens, distance transforms and density function shape representations have proven to be the most utile. Distance transforms inherit the numerous geometric advantages of implicit curve representations while density functions are unmatched in their approach...
A novel level set image segmentation method using the prior shape is proposed in this paper in view of the problem which occurs when the existing level set method using the prior shape segmented the images with strong noise, weak boundary or complicated background. The kernel principal component analysis is used in this method to decrease the dimensions of the training samples and extract the principal...
Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image...
Aimed at the defect of the traditional mean shift tracking algorithm which using symmetric kernel function who contains amounts of background pixels, this paper presents an enhanced mean shift tracking algorithm based on evolutive asymmetric kernel to improve the tracking accuracy and stability. The paper firstly described the calculation method of Template Center which is the key issue in introducing...
Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for T1-weighted MR brain images based on an improved level set...
A new algorithm by using geometric active contour model with the fusion of shape and texture priors to manual segment medical images has been presented in this paper. Then the prior knowledge is merged into active contour model with its contour evolution which is evolved using a genetic algorithm technique. The new method has some advantages over classical level set methods in case of images with...
Traditional object tracking based on color histograms can only represent objects with rectangles or ellipses, thus having very limited ability to follow objects with complex shapes or with highly non-rigid motion. In addressing this problem, we formulate histogram-based tracking as a functional optimization problem based on Jesson-Shannon divergence that is bounded, symmetric and a true metric. Optimization...
A new geometric active contour based level-sets model combining gradient, region and shape knowledge information cues is proposed to robust object detection boundaries in presence of occlusions and cluttered background. The gradient, region and shape knowledge information are incorporated as energy terms. The a priori shape model is based on statistical learning of the training data distribution where...
Recently, a new reformulation of geometric active contour model is introduced by reformulating the gradient flow with Sobolev-type inner products. Classical inner product induces a pathological Riemannian metric on the space of smooth curves. However, there are also undesirable features associated with the gradient flow that this inner product induces. Sobolev metrics induce good regularity properties...
In this paper, we propose an image deformation technique using nonparametric regression to animate characters in a still image for multimedia applications. The proposed approach effectively produces a sequence of contiguous frames in an animation. It automatically generates deformed shapes by using elliptic radial basis functions (ERBFs) and locally weighted regression (LOESS). ERBFs are used for...
Traditional mean shift algorithm requires a symmetrical kernel, such as a circle or an ellipse, and assumes the kernel represents the object shape. Because the symmetrical kernel always contains some background regions, the performance of moving object tracking is dramatically affected when background is complex and changes greatly. To address above issue, this paper proposes an improved mean shift...
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