In this paper, we propose a depth image generation method by stereo matching on super-pixel (SP) basis. In the proposed method, block matching is performed only at the center of the SP, and the obtained disparity is a...In this paper, we propose a depth image generation method by stereo matching on super-pixel (SP) basis. In the proposed method, block matching is performed only at the center of the SP, and the obtained disparity is applied to all pixels of the SP. Next, in order to improve the disparity, a new SP-based cost filter is introduced. This filter multiplies the matching cost of the surrounding SP by a weight based on reliability and similarity and sums the weighted costs of neighbors. In addition, we propose two new error checking methods. One-way check uses only a unidirectional disparity estimation with a small amount of calculation to detect errors. Cross recovery uses cross checking and error recovery to repair lacks of objects that are problematic with SP-based matching. As a result of the experiment, the execution time of the proposed method using the one-way check was about 1/100 of the full search, and the accuracy was almost equivalent. The accuracy using cross recovery exceeded the full search, and the execution time was about 1/60. Speeding up while maintaining accuracy increases the application range of depth images.展开更多
Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,b...Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.展开更多
Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clus...Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.展开更多
针对现有显著性检测模型准确度不高的问题,提出一种应用局部特征和全局特征对比的显著性检测模型.该算法首先使用简单的线性迭代聚类(Simple Linear Iterative Clustering,SLIC)分割算法将图像预分割为若干紧凑的超像素,选取边界区域集...针对现有显著性检测模型准确度不高的问题,提出一种应用局部特征和全局特征对比的显著性检测模型.该算法首先使用简单的线性迭代聚类(Simple Linear Iterative Clustering,SLIC)分割算法将图像预分割为若干紧凑的超像素,选取边界区域集并计算所有超像素的边界权重;然后计算颜色和纹理特征的局部对比度得到局部显著图,利用全局特征的独特性,空间分布特性得到全局显著图;最后采用求和乘积(Sum and Product,SP)方法将局部和全局显著图融合得到最终的显著图.在Achanta测试集上进行对比分析,实验结果表明本文算法能更准确地检测出显著区域,与其它5种算法相比具有较大的优势.展开更多
影像分割是面向对象影像分析的基础和关键。针对传统影像分割方法地物边界依附性差、易受影像噪声影响等问题,提出一种简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)的高分辨率遥感影像分割方法。该方法首先用SLIC算法对...影像分割是面向对象影像分析的基础和关键。针对传统影像分割方法地物边界依附性差、易受影像噪声影响等问题,提出一种简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)的高分辨率遥感影像分割方法。该方法首先用SLIC算法对影像过分割生成SLIC超像素,之后根据相似性规则对SLIC超像素进行合并实现影像分割;然后通过构造Lab颜色空间下的五维特征参数度量影像像素的局部特征差异,并通过SLIC算法把具有相似性特征的像素聚类生成超像素,克服影像噪声对分割结果的影响;最后根据相似性合并规则以超像素为基本单元进行区域合并,从而达到分割目的。实验结果表明,所提出方法具有良好的高分辨率遥感影像分割结果。展开更多
文摘In this paper, we propose a depth image generation method by stereo matching on super-pixel (SP) basis. In the proposed method, block matching is performed only at the center of the SP, and the obtained disparity is applied to all pixels of the SP. Next, in order to improve the disparity, a new SP-based cost filter is introduced. This filter multiplies the matching cost of the surrounding SP by a weight based on reliability and similarity and sums the weighted costs of neighbors. In addition, we propose two new error checking methods. One-way check uses only a unidirectional disparity estimation with a small amount of calculation to detect errors. Cross recovery uses cross checking and error recovery to repair lacks of objects that are problematic with SP-based matching. As a result of the experiment, the execution time of the proposed method using the one-way check was about 1/100 of the full search, and the accuracy was almost equivalent. The accuracy using cross recovery exceeded the full search, and the execution time was about 1/60. Speeding up while maintaining accuracy increases the application range of depth images.
文摘Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.
基金funded by Scientific and Technological Innovation Team of Universities in Henan Province,grant number 22IRTSTHN008Innovative Research Team(in Philosophy and Social Science)in University of Henan Province grant number 2022-CXTD-02the National Natural Science Foundation of China,grant number 41371524.
文摘Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.
文摘针对现有显著性检测模型准确度不高的问题,提出一种应用局部特征和全局特征对比的显著性检测模型.该算法首先使用简单的线性迭代聚类(Simple Linear Iterative Clustering,SLIC)分割算法将图像预分割为若干紧凑的超像素,选取边界区域集并计算所有超像素的边界权重;然后计算颜色和纹理特征的局部对比度得到局部显著图,利用全局特征的独特性,空间分布特性得到全局显著图;最后采用求和乘积(Sum and Product,SP)方法将局部和全局显著图融合得到最终的显著图.在Achanta测试集上进行对比分析,实验结果表明本文算法能更准确地检测出显著区域,与其它5种算法相比具有较大的优势.
文摘影像分割是面向对象影像分析的基础和关键。针对传统影像分割方法地物边界依附性差、易受影像噪声影响等问题,提出一种简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)的高分辨率遥感影像分割方法。该方法首先用SLIC算法对影像过分割生成SLIC超像素,之后根据相似性规则对SLIC超像素进行合并实现影像分割;然后通过构造Lab颜色空间下的五维特征参数度量影像像素的局部特征差异,并通过SLIC算法把具有相似性特征的像素聚类生成超像素,克服影像噪声对分割结果的影响;最后根据相似性合并规则以超像素为基本单元进行区域合并,从而达到分割目的。实验结果表明,所提出方法具有良好的高分辨率遥感影像分割结果。
