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An Object Segmentation Method Based on Sparse Shape Representation in Hidden Kernel Space
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A technology of target segmentation and sparse representation, which is applied in image analysis, image enhancement, instruments, etc., can solve the problem of poor segmentation results of sparse representation of neighbors
Inactive Publication Date: 2019-05-28
ZHEJIANG UNIV
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[0004] (1) The technical problem to be solved is to provide an image segmentation method involving the sparse representation of the hidden kernel space, which overcomes the problem of poor segmentation results caused by the sparse representation of the existing shape neighbors
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Embodiment 1
[0043] As shown in the figure, this embodiment provides a sparse shape representation object segmentation method based on hidden kernel space, including the following steps:
[0044] S1: Perform KPCA processing on the training shape set;
[0045] S2: Based on the KPCA processing results, the hidden kernel shape space is established;
[0046] S3: Establish a high-level sparse representation model based on the hidden kernel shape space;
[0047] S4: Establish the underlying driving energy function based on the probability shape, and at the same time establish the dual connection item between the underlying energy and the high-level energy;
[0048] S5: Initialize the sparse coefficients and the underlying probability shape model;
[0049] S6: Calculate the dual connection item by using the sparse coefficient;
[0050] The dual connection item provided by this embodiment can be calculated by using the initialization sparse coefficient in the initialization phase, and can be ca...
Embodiment 2
[0073] In this embodiment, a two-layer segmentation model framework is established while constructing a hidden kernel shape space and a sparse representation of the hidden kernel space; the specific steps of the two-layer segmentation model framework are as follows:
[0074] A) acquiring image data and performing KPCA processing on the training shape set;
[0075] B) Based on the KPCA processing results, the hidden kernel shape space is established;
[0076] C) Build a high-level sparse representation based on the hidden kernel shape space;
[0077] D) Establish the underlying driving energy function based on the probability shape, and simultaneously establish the dual connection item between the underlying energy and the high-level energy;
[0078] E) Initialize the sparse coefficients and initialize the underlying probability shape;
[0079] F) Use the sparse coefficient to calculate the dual connection term as the constraint of the underlying function;
[0080] G) Optimi...
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Abstract
The invention discloses a sparse shape representation object segmentation method based on hidden kernel space. Firstly, the kernel principal component is extracted from the original prior shape training set; the hidden kernel shape space is established according to the KPCA extraction result and the hidden kernel space is sparsely constructed. Shape representation model; Construct dual constraint items based on sparse coefficients and underlying variational driving energy function based on probability shape; Alternate iterative method is used to solve the objective function; Use hidden kernel space shape sparse representation results to supervise the variational energy function based on probability shape Evolution, and use the evolution curve derived from the energy function to achieve image segmentation. The invention overcomes the problems of poor target segmentation ability and low segmentation accuracy in the existing sparse representation segmentation method based on shape neighbors, thereby solving the problem of poor sparse representation segmentation effect in the original shape domain.
Description
technical field [0001] The invention relates to the field of image segmentation and shape representation, in particular to an object segmentation method based on sparse representation of hidden kernel space shape. Background technique [0002] Object shape representation and segmentation is one of the core tasks in image processing and computer vision. At present, the existing methods can be generally divided into edge-based shape segmentation methods and region-based shape segmentation methods. Most of these methods define an image-based variational energy function and minimize the energy function to drive the evolution of the shape curve. After the energy function converges, a curve based on the energy function is finally used to mark the target area. This method has a good segmentation effect for the case where the target shape is relatively complete. However, when the target has defects, occlusions, or adhesion with background noise similar to the target, simply solving...
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