To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit.
In this paper, a quantum edge detection algorithm was proposed for the blurry and complex characteristic of medical
images with the elicitation of the basic concept and principle of quantum signal processing. Firstly, based on the pixel
qubit and quantum states superposition concept, an image enhancement operator based on quantum probability statistic is
presented which combines with gray correlative characteristic of the pixels in 3×3 neighborhood windows. Then, in
order to realize edge detection, an edge measurement operator based on fuzzy entropy is adopted to the quantum
enhancement image. Experiments showed that this method is more efficient than traditional edge detection methods
because it has a better capability of edge detection to medical images, which can extract not only strong edge but also the
weak one.
In this paper, a method of multi-threshold image segmentation was proposed using the principle of maximum entropy
and an improved quantum-inspired genetic algorithm (IQGA). With the increase number of multi-threshold, it is
unrealistic to compute the entropy of all possible combinations and find the maximum entropy in all the multi-threshold
combinations for images segmentation. Quantum-inspired genetic algorithm (QGA) has a better characteristic of
population diversity, rapid convergence and global search capability than that of the conventional genetic algorithm
(CGA). However, the solutions of QGAs may diverge or have a premature convergence to a local optimum due to the
selection of the rotation angle in searching the maximum value of a function. Therefore, IQGA is put forward which
joins the optimal selection and catastrophe operations, and defines an adaptive rotation angle of quantum gate during
quantum chromosomes update procedure. Experimental results demonstrated that the proposed method has a good
performance.
KEYWORDS: Particles, Quantum mechanics, Medical imaging, Mechanics, Quantum information, Polymers, Monte Carlo methods, Systems modeling, Statistical modeling, Digital filtering
Contour extraction is a key issue in many medical applications. A novel statistical approach based on quantum mechanics
to extract contour of the interested object of medical images was proposed in this paper. The natures of quantum
statistical concepts such as the quantum discontinuity and the wave function correspond to the discrete and gray
possibility of an image respectively. Contour extraction will be performed by the quantum particle movement, where the
particle will be moved forward to the position with high probability density edges in image potential field. Experimental
results with medical images demonstrated that the proposed approach has the capability to extract contours with arbitrary
initialization and handle topology changes as well as both the inner and outer contours by a single initialization.
Keywords: Statistical approach, contour extraction, path integral, quantum mechanics.ing
Because Chan and Vese(C-V) model using one level set function can only represent one object and one background, it
cannot represent multiple junctions of multiple objects. In this paper, an improved multi-object segment algorithm is
proposed based on C-V model of single level set. First, the given image resolution is deduced by wavelet transform.
Since the low resolution approximate image contains less noise and pixels, it can speed up the active contour evolution.
Secondly, an improved C-V model of a single level set is introduced to obtain the multi-objects' approximate contour,
which can make use of topology split information of the contour effectively. Thirdly, the inverse discrete wavelet
transform is used to the resulted image and level set of the coarse scale image, which can get the approximation contour
on the original image. Lastly, the approximation contour is taken as an initial level set function and the second active
contour evolution is performed on the original image to get the real multi-objects contour. Experimental results show that
the proposed algorithm can realize the multi-object segmentation effectively and quickly.
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